machine learning research proposal sample

Office Address

#5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
[email protected]
+91- 81240 01111

Social List

Trending topics for phd research proposal in machine learning.

machine learning research proposal sample

Machine learning techniques have prompted at the forefront over the last few years due to the advent of big data. Machine learning is a precise subfield of artificial intelligence (AI) that seeks to analyze the massive data chunks and facilitate the system to learn the data automatically without the explicit support of programming. The machine learning algorithms attempt to reveal the fine-grained patterns from the unprecedented data under multiple perspectives and to build an accurate prediction model as never before. For the purpose of learning, the machine learning algorithm is sub-categorized into four broad groups include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Whenever the new unseen data is fed as input to the machine learning algorithm, it automatically learns and predicts the forthcoming by exploiting the previous experience over time. Machine learning is continually liberating its potency in a broad range of applications, including the Internet of Things (IoT), computer vision, natural language processing, speech processing, online recommendation system, cyber security, neuroscience, prediction analytics, fraud detection, and so on.

Guidelines for Preparing a Phd Research Proposal

Latest Research Proposal Ideas in Machine Learning

Research Proposal on Attention-based Image Segmentation
Research Proposal on Representation learning for multi-modal data
Research Proposal on Machine Learning methods for Heart Disease Prediction
Research Proposal on Graph Pooling and Graph Unpooling
Research Proposal on Multi-modal and cross-domain feature fusion
Research Proposal on Machine Learning for Multimedia classification
Research Proposal on Neural Machine Translation with reinforcement learning
Research Proposal on Multi-view and Multi-modal Fusion for Semi-Supervised Learning
Research Proposal on Stock Market Prediction using Machine Learning
Research Proposal on Adaptive radiotherapy using deep learning
Research Proposal on Adversarial Natural Language Processing
Research Proposal on Deep Recurrent Neural Networks
Research Proposal on Automated image analysis and diagnosis in radiology using deep learning
Research Proposal on Scalable and fault-tolerant data stream processing
Research Proposal on Deep Belief Networks
Research Proposal on Bayesian optimization for deep learning hyperparameters
Research Proposal on Cross-domain opinion mining
Research Proposal on Restricted Boltzmann Machines
Research Proposal on Quantum generative models
Research Proposal on Deep Generative Models using Belief Networks
Research Proposal on Deep Neural Networks for Speech Recognition
Research Proposal on Transfer learning across pattern recognition domains
Research Proposal on Evolutionary optimization for deep learning hyperparameters
Research Proposal on Deep Neural Networks for Computer Vision
Research Proposal on Pre-training of entity embeddings
Research Proposal on Graph Attention Networks
Research Proposal on Deep Learning for Intelligent Wireless Networks
Research Proposal on Multimedia representation learning
Research Proposal on One-stage object detection using YOLO
Research Proposal on Extreme Learning Machines
Research Proposal on Generative adversarial networks for text generation
Research Proposal on Transfer learning across multimedia domains
Research Proposal on Dynamic Neural Networks
Research Proposal on Transformer-based attention
Research Proposal on Motion prediction and synthesis
Research Proposal on Deep Learning for Autonomous Vehicles
Research Proposal on Transfer Learning for Natural Language Processing
Research Proposal on Deep reinforcement learning for image super-resolution
Research Proposal on Long Short-Term Memory Networks
Research Proposal on Multi-Task and Multi-Output Regression
Research Proposal on Improved fingerprint recognition with deep learning
Research Proposal on Radial Basis Function Networks
Research Proposal on Domain Adaptation with Semi-Supervised Learning
Research Proposal on Deep learning for protein structure prediction
Research Proposal on Multi-Goal Reinforcement Learning
Research Proposal on Reinforcement Learning with Convolutional Neural Networks
Research Proposal on Deep learning for blood pressure prediction from wearable devices
Research Proposal on Mutual Information Estimation
Research Proposal on Hybrid Neural Architecture Search with combination of evolutionary and gradient-based methods
Research Proposal on Deep Learning-based Image Restoration with Generative Adversarial Networks
Research Proposal on Generalized Few-Shot Classification
Research Proposal on Privacy-preserving feature engineering
Research Proposal on Crop Yield Prediction using Deep Learning
Research Proposal on Multimodal Deep Learning
Research Proposal on Continuous Learning for Natural Language Processing
Research Proposal on Structured Topic Modeling
Research Proposal on Entity Embeddings
Research Proposal on Attention for knowledge graph representation
Research Proposal on Multi-agent Reinforcement Learning in Partially Observable Environments
Research Proposal on Quantum Machine Learning
Research Proposal on Fine-tuning of entity embeddings
Research Proposal on Deep Reinforcement Learning for Multimodal Representation
Research Proposal on One-Shot Learning
Research Proposal on Object detection in 3D scenes
Research Proposal on Multi-asset Portfolio Optimization with Deep Learning
Research Proposal on Hierarchical Reinforcement Learning
Research Proposal on Attention-based Neural Machine Translation
Research Proposal on Video Deblurring with Deep Learning
Research Proposal on Multiple Instance Learning
Research Proposal on Clinical Event Prediction
Research Proposal on Deep learning for improved identification of disease-causing genetic variations
Research Proposal on Interpretable Machine Learning
Research Proposal on Adversarial attacks and defenses in Convolutional Neural Networks
Research Proposal on Named entity recognition in noisy and unstructured text
Research Proposal on Density Estimation
Research Proposal on Panoptic Segmentation
Research Proposal on Improving the accuracy of treatment planning with deep learning
Research Proposal on Imitation Learning
Research Proposal on Deep Learning for Abnormal Event Detection in Surveillance Videos
Research Proposal on Deep Reinforcement Learning for Microscopy Image Analysis
Research Proposal on Active Learning
Research Proposal on Predictive Analytics for Supply Chain Performance using Deep Learning
Research Proposal on Face Recognition in the Wild
Research Proposal on Object Detection using Deep Learning
Research Proposal on Deep Learning for Compressed Sensing in Remote Sensing
Research Proposal on Multi-task Neural Machine Translation
Research Proposal on Image Segmentation using Deep Learning
Research Proposal on Plant Leaf Shape and Texture Analysis with Deep Learning
Research Proposal on Adversarial robustness in Belief Networks
Research Proposal on Human Motion Recognition using Deep Learning
Research Proposal on Interactive Topic Modeling with Human Feedback
Research Proposal on Attention-based interpretation of neural networks
Research Proposal on Dialogue Systems
Research Proposal on Temporal Consistency Restoration in Videos using Deep Learning
Research Proposal on Meta-Reinforcement Learning
Research Proposal on Multimodal Representation Learning
Research Proposal on Real-time Image Denoising with Deep Learning
Research Proposal on Multi-Modal and Cross-Lingual Word Embeddings
Research Proposal on Face Recognition using Deep Learning
Research Proposal on Deep learning for blood pressure prediction in low-resource settings
Research Proposal on Attention Mechanisms in Convolutional Neural Networks
Research Proposal on Image Captioning using Deep Learning
Research Proposal on Named entity recognition in a multilingual context
Research Proposal on Graph-based pattern recognition
Research Proposal on Named Entity Recognition
Research Proposal on Deep learning for palmprint recognition
Research Proposal on Action recognition in videos
Research Proposal on Pharmacogenomics using Deep Learning
Research Proposal on Deep learning for detecting abnormalities in medical images in radiology
Research Proposal on Deep Learning for Cell Segmentation and Tracking
Research Proposal on Action Recognition using Deep Learning
Research Proposal on Transfer learning in Convolutional Neural Networks
Research Proposal on Multi-Modal and Multi-Task Ensemble Learning
Research Proposal on Microscopic Image Analysis using Deep Learning
Research Proposal in Real-time analytics on big data streams
Research Proposal on Augmentation for object detection and segmentation
Research Proposal on Facial Expression Recognition using Deep Learning
Research Proposal on Attention for visual data processing
Research Proposal on Domain Adaptation for Health Record Analysis
Research Proposal on Radiology using Deep Learning
Research Proposal on Large-scale parallel hyperparameter optimization
Research Proposal on Multi-modal Fusion for Facial Expression Recognition
Research Proposal on Bioinformatics using Deep Learning
Research Proposal on Neural Machine Translation with semantic representation
Research Proposal on Cross-lingual Text Summarization
Research Proposal on Text Summarization
Research Proposal on Task-Oriented Dialogue Systems
Research Proposal on Adversarial attacks and defenses in medical image analysis
Research Proposal on Semantic Analysis
Research Proposal on Image Captioning with Attention Mechanisms
Research Proposal on Named entity recognition in low-resource languages using deep learning
Research Proposal on Radiotherapy using Deep Learning
Research Proposal on Deep Learning for Quantitative Image Analysis in Microscopy
Research Proposal on Deep learning for improved classification of multi-view sequential data
Research Proposal on Image Super Resolution Using Deep Learning
Research Proposal on Deep Learning for Facial Emotion Recognition from Speech
Research Proposal on Incorporating Background Knowledge in Topic Modeling
Research Proposal on Neural Rendering
Research Proposal on Deep learning in radiation therapy planning and optimization
Research Proposal on Multi-modal Text Summarization
Research Proposal on Biometric Recognition using Deep Learning
Research Proposal on Neural rendering for improved video game graphics
Research Proposal on Time-series Regression with Recurrent Neural Networks
Research Proposal on Medical image analysis in resource-limited settings with deep learning
Research Proposal on Meta-learning for few-shot multi-class classification
Research Proposal on Automated evaluation of radiotherapy outcomes using deep learning
Research Proposal on Domain-specific entity embeddings
Research Proposal on Neural rendering for virtual and augmented reality applications
Research Proposal on Semantic Segmentation using FCN
Research Proposal on Improved gene expression analysis with deep learning
Research Proposal on Multi-modal data analysis for disease prediction
Research Proposal on Multi-level Deep Network for Image Denoising
Research Proposal on Deep learning for image-based diagnosis in radiology
Research Proposal on Video Inpainting with Deep Learning
Research Proposal on Deep learning for predicting blood pressure response to treatment
Research Proposal on Multi-agent Reinforcement Learning with Evolutionary Algorithms
Research Proposal on Decentralized Multi-agent Reinforcement Learning
Research Proposal on Deep Learning for Compressed Sensing Reconstruction
Research Proposal on Deep Reinforcement Learning for Supply Chain Optimization
Research Proposal on Multi-modal medical image analysis in radiology with deep learning
Research Proposal on Deep Learning for Multimodal Fusion
Research Proposal on Adversarial attacks and defenses in biometric recognition using deep learning
Research Proposal on Deep Learning for Compressed Sensing in Wireless Communications
Research Proposal on Human-in-the-loop Neural Architecture Search
Research Proposal on Agricultural Resource Management with Deep Learning
Research Proposal on Generative Models for Semi-Supervised Learning
Research Proposal on Deep learning for predicting cancer treatment response
Research Proposal on Graph Generative Models
Research Proposal on Deep generative models for image super-resolution
Research Proposal on Deep Learning for Drug Response Prediction
Research Proposal on Transfer Learning for Face Recognition
Research Proposal on Deep Reinforcement Learning for Facial Expression Recognition
Research Proposal on Neural rendering for photorealistic image synthesis
Research Proposal on Prediction of treatment response using deep learning
Research Proposal on Deep Learning for Plant Species Identification
Research Proposal on Deep transfer learning for medical image analysis
Research Proposal on Improved drug discovery in neglected diseases using deep learning
Research Proposal on Interpretability and Explainability of Convolutional Neural Networks
Research Proposal on Cross-lingual semantic analysis
Research Proposal on Deep learning for predicting genetic interactions
Research Proposal on Deep Reinforcement Learning for Plant Disease Detection
Research Proposal on Fine-grained named entity recognition
Research Proposal on Transfer learning for sentiment analysis
Research Proposal on Deep learning for predicting protein-protein interactions
Research Proposal on Object detection with active learning
Research Proposal on Deep learning for improving drug discovery through in silico experimentation
Research Proposal on Cross-lingual Image Captioning
Research Proposal on Deep Learning for Food Safety Prediction in Agriculture
Research Proposal on Improved epigenetic analysis using deep learning
Research Proposal on Deep Learning for Route Optimization in Logistics
Research Proposal on Deep Learning for Predictive Maintenance in Supply Chain
Research Proposal on Multi-modal Representation Learning for Sentiment Analysis
Research Proposal on Plant Leaf Recognition with Computer Vision
Research Proposal on Cross-lingual named entity recognition
Research Proposal on Deep learning for semantic-aware image super-resolution
Research Proposal on Generative Adversarial Networks with Convolutional Neural Networks
Research Proposal on Attention in reinforcement learning
Research Proposal on Multi-objective optimization for deep learning hyperparameters
Research Proposal on Multi-modal entity embeddings
Research Proposal on Dynamic Graph Neural Networks
Research Proposal on Image Captioning with Visual and Language Context
Research Proposal on Deep Learning for Portfolio Diversification and Optimization
Research Proposal on Motion Compensation for Video Restoration using Deep Learning
Research Proposal on Multi-modal deep learning for multi-view sequential data analysis
Research Proposal on Deep learning for cancer diagnosis from medical images
Research Proposal on Deep transfer learning for radiology image analysis
Research Proposal on Deep learning for improved iris recognition
Research Proposal on Processing high-velocity and high-volume data streams
Research Proposal on Causal inference for multi-class classification
Research Proposal on Deep Extreme Learning Machines
Research Proposal on Meta-representation learning
Research Proposal on Data augmentation in Neural Machine Translation
Research Proposal on Fairness and Bias in Health Record Analysis
Research Proposal on Multi-omics Integration for Personalized Medicine
Research Proposal on Deep Learning for Micro-expression Recognition
Research Proposal on Deep Learning for Compressed Sensing in Compressed Speech
Research Proposal on Transfer learning for feature engineering
Research Proposal in Sentiment analysis on multimodal data
Research Proposal on Online Extreme Learning Machines
Research Proposal on Deep Learning for Face Anti-spoofing
Research Proposal on Domain adaptation and transfer learning for multi-class classification
Research Proposal on Reinforcement Learning for Natural Language Processing
Research Proposal on Transfer Learning for Word Embeddings
Research Proposal on Multi-head attention
Research Proposal on Model-agnostic interpretation methods
Research Proposal on Deep Generative Models for Microscopy Image Synthesis
Research Proposal on Deep learning for quality assurance in radiotherapy
Research Proposal on Low-light image super-resolution with deep learning
Research Proposal on Fine-tuning Pre-trained Transformer Models for Image Captioning
Research Proposal on Deep Learning for Facial Expression Recognition in the Wild
Research Proposal on Multi-modal semantic analysis
Research Proposal on Deep learning for gait recognition
Research Proposal on Graph Reinforcement Learning
Research Proposal on Gradient-based optimization for deep learning hyperparameters
Research Proposal on Object detection with transformers
Research Proposal on Transfer learning for multimedia classification
Research Proposal on Generative adversarial networks for representation learning
Research Proposal on Representation Learning with Graphs for Word Embeddings
Research Proposal on Deep Learning for Motion Anomaly Detection in Videos
Research Proposal on Deep Reinforcement Learning for Face Recognition
Research Proposal on Deep Learning for Microscopy Image Restoration and Denoising
Research Proposal on Deep Reinforcement Learning for Text Summarization
Research Proposal on Deep learning for medical image registration
Research Proposal on Improved computer-aided diagnosis in radiology with deep learning
Research Proposal on Multi-modal cancer diagnosis using deep learning
Research Proposal on Improved single image super-resolution using deep learning
Research Proposal on Image Captioning in the Wild
Research Proposal on Graph Convolutional Networks
Research Proposal on Deep Learning for Small-Molecule Property Prediction
Research Proposal on Real-time image super-resolution using deep learning
Research Proposal on Deep learning for improved image quality assessment in radiology
Research Proposal on Quantum reinforcement learning
Research Proposal on Adaptive attention
Research Proposal on Transfer Ensemble Learning
Research Proposal on Multi-Task and Multi-Modal Learning with Convolutional Neural Networks
Research Proposal on Two-stage object detection using Faster R-CNN
Research Proposal on Face Attribute Prediction and Analysis
Research Proposal on Deep learning for medical image synthesis and augmentation
Research Proposal on Weather Forecasting for Agriculture using Deep Learning
Research Proposal on Deep Learning for Video Compression and Restoration
Research Proposal on Non-Parametric Topic Modeling
Research Proposal on Deep Learning for Demand Forecasting in Supply Chain Management
Research Proposal on Soil Moisture Prediction using Deep Learning
Research Proposal on Deep Learning for Predictive Portfolio Management
Research Proposal on Plant Disease Image Analysis with Deep Learning
Research Proposal on Inventory Optimization with Deep Learning
Research Proposal on Attention-based Image Denoising
Research Proposal on Deep Generative Models for Drug Repurposing
Research Proposal on Deep Learning for Compressed Sensing in Compressed Video
Research Proposal on Transfer Learning for Topic Modeling
Research Proposal on Representation learning for graph-structured data
Research Proposal on Federated Learning for Recommendation System
Research Proposal on Adversarial Ensemble Learning
Research Proposal on Graph-based Natural Language Processing
Research Proposal on Cross-domain sentiment analysis
Research Proposal on Unsupervised feature learning using Belief Networks
Research Proposal on Quantum neural networks
Research Proposal on Representation learning for speech data
Research Proposal on Object detection with semantic segmentation
Research Proposal on Zero-shot Neural Machine Translation
Research Proposal on Dialogue State Tracking
Research Proposal on Image Captioning with Semantic Segmentation
Research Proposal on Deep Learning for Image Registration and Stitching
Research Proposal on Text Summarization with Sentiment Analysis
Research Proposal on Deep learning for radiation therapy-related toxicity prediction
Research Proposal on Improved image quality assessment in medical imaging using deep learning
Research Proposal on Scene synthesis and manipulation using neural rendering
Research Proposal on Multi-modal biometric recognition using deep learning
Research Proposal on Named entity recognition for multi-modal data
Research Proposal on Improved lung cancer diagnosis using deep learning
Research Proposal on Multi-view sequential data analysis in low-resource settings using deep learning
Research Proposal on Deep Learning for Video Denoising
Research Proposal on Multi-agent Reinforcement Learning for Dynamic Environments
Research Proposal on Deep Learning for Quality Control in Supply Chain
Research Proposal on Object detection with domain adaptation
Research Proposal on Plant Disease Segmentation and Recognition with Deep Learning
Research Proposal on Adversarial Attacks and Defences in Face Recognition
Research Proposal on Anomaly Detection in Videos with Deep Reinforcement Learning
Research Proposal on Integrating Electronic Health Records and Genomics for Personalized Medicine
Research Proposal on Adversarial attacks and defenses in radiology using deep learning
Research Proposal on Deep Generative Models for Synthetic Facial Expression Data
Research Proposal on Transfer Learning for Text Summarization
Research Proposal on Extractive Text Summarization
Research Proposal on Multi-modal Face Recognition
Research Proposal on Multi-frame super-resolution using deep learning
Research Proposal on Spatial-Temporal Graph Convolutional Networks
Research Proposal on Real-time neural rendering for interactive environments
Research Proposal on Convolutional Neural Networks for Object Detection and Segmentation
Research Proposal on Transfer learning for named entity recognition
Research Proposal on Transfer Learning for Semi-Supervised Learning
Research Proposal on Deep learning for early cancer detection
Research Proposal on Imitation Learning and Inverse Reinforcement Learning
Research Proposal on Deep reinforcement learning for multi-view sequential data analysis
Research Proposal on Attention for sequential reasoning
Research Proposal on Deep learning for drug repurposing and de-novo drug discovery
Research Proposal on Generative adversarial networks for domain adaptation
Research Proposal on Crop Growth Monitoring using Deep Learning
Research Proposal in Opinion mining on social media
Research Proposal on Deep Learning for Video Frame Interpolation
Research Proposal on Multi-lingual entity embeddings
Research Proposal on Multi-agent Reinforcement Learning with Communication
Research Proposal on Semantic augmentation
Research Proposal on Deep Learning for Supplier Selection in Supply Chain
Research Proposal on Domain adaptation in Neural Machine Translation
Research Proposal on Deep Learning for Plant Leaf Disease Diagnosis
Research Proposal on Multi-Turn conversational Dialogue Systems
Research Proposal on Attention-based Multimodal Representation Learning
Research Proposal on Deep Generative Models for Face Synthesis
Research Proposal on Fine-grained Plant Disease Recognition with Deep Learning
Research Proposal on Deep Reinforcement Learning for Drug Discovery
Research Proposal on Deep Learning for Compressed Sensing in Medical Imaging
Research Proposal on Transfer Learning for Microscopy Image Analysis
Research Proposal on Multi-object Anomaly Detection in Videos with Deep Learning
Research Proposal on Adversarial Training for Text Summarization
Research Proposal on Human-in-the-loop Active Learning
Research Proposal on Contextual word embeddings for semantic analysis
Research Proposal on Scalable Neural Architecture Search for large-scale datasets and hardware accelerators
Research Proposal on Deep learning for super-resolution of microscopy images
Research Proposal on Causal inference and causal feature engineering
Research Proposal on Improved 3D object rendering using deep neural networks
Research Proposal on Convolutional Neural Networks (CNN) for Computer Vision tasks
Research Proposal on Deep transfer learning for bioinformatics analysis
Research Proposal on Self-training and Co-training for Semi-Supervised Learning
Research Proposal on Video Super-resolution using Deep Learning
Research Proposal on Meta-Learning for Few-shot Semi-Supervised Learning
Research Proposal on Fertilizer Recommendation System using Deep Learning
Research Proposal on Non-Linear Regression with Gaussian Processes
Research Proposal on Adversarial Training for Image Denoising
Research Proposal on Active and Semi-Supervised Ensemble Learning
Research Proposal on Improved blood pressure prediction in cardiovascular disease patients using deep learning
Research Proposal on Privacy-preserving Natural Language Processing
Research Proposal on Named entity disambiguation using deep learning
Research Proposal on Continuous Learning and Adaptation for Word Embeddings
Research Proposal on Deep reinforcement learning in medical imaging and radiology
Research Proposal on Incremental and online machine learning for data streams
Research Proposal on Adversarial training for image super-resolution
Research Proposal on Attention in federated learning
Research Proposal on Multi-modal Microscopy Image Analysis
Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning
Research Proposal on Mode collapse and stability in generative adversarial networks
Research Proposal on Image Captioning with Scene Graphs
Research Proposal Topics on Convolutional Neural Networks Research Challenges and Future Impacts
Research Proposal on Adversarial attacks and defenses in sentiment analysis
Research Proposal on Multi-person Motion Analysis
Research Proposal on Graph Neural Network for Graph Analytics
Research Proposal on Sentiment polarity detection
Research Proposal on Transformer-based Neural Machine Translation
Research Proposal on Deep Reinforcement Learning Methods for Active Decision Making
Research Proposal on Cross-modal correspondence learning
Research Proposal on Graph Transformer Networks
Research Proposal on Deep Learning based Medical Imaging
Research Proposal on Representation learning for pattern recognition
Research Proposal on Mixup and cutmix data augmentation
Research Proposal On Pre-trained Word embedding for Language models
Research Proposal on Multi-modal data analysis using Belief Networks
Research Proposal on Object detection with instance segmentation
Research Proposal on Medical Machine Learning for Healthcare Analysis
Research Proposal on Multi-modal data analysis using Extreme Learning Machines
Research Proposal on Graph-based entity embeddings
Research Proposal on Generative Adversarial Network
Research Proposal on Quantum clustering algorithms
Research Proposal on Sentiment analysis for low-resource languages
Research Proposal on Hyperparameter Optimization and Fine-Tuning in Deep Neural Network
Research Proposal on Transfer learning for hyperparameter optimization
Research Proposal on Self-attention for sequential data
Research Proposal on Deep Learning Models for Epilepsy Detection
Research Proposal on Geometrical transformations for data augmentation
Research Proposal on Meta-Learning for Word Embeddings
Research Proposal Topics on Deep Learning Models for Epilepsy Detection
Research Proposal on Anchor-free object detection
Research Proposal on Multi-Task and Multi-lingual Natural Language Processing
Research Proposal on Machine Learning in Alzheimer-s Disease Detection
Research Proposal on Graph Autoencoders
Research Proposal on Graph-based Semi-Supervised Learning
Research Proposal on Machine Learning in Cancer Diagnosis
Research Proposal on Human Pose Estimation
Research Proposal on Adversarial Reinforcement Learning
Research Proposal on Machine Learning in Covid-19 Diagnosis
Research Proposal on Medication Recommendation
Research Proposal in Light-weight and Efficient Convolutional Neural Networks for deployment on edge devices
Research Proposal on Machine Learning in Diagnosis of Diabetes
Research Proposal on Cross-age Face Recognition
Research Proposal on Adversarial robustness in pattern recognition
Research Proposal on Machine Learning in Heart Disease Diagnosis
Research Proposal on Image Captioning with Transfer Learning
Research Proposal on Representation learning for time series data
Research Proposal on Machine Learning in Parkinson-s Diagnosis
Research Proposal on Deep Learning for Toxicology and Safety Assessment
Research Proposal on Instance Segmentation using Mask R-CNN
Research Proposal on Deep Learning Models for Epileptic Focus Localization
Research Proposal on Adversarial Training for Robust Microscopy Image Analysis
Research Proposal on Dialogue Generation using Generative Models
Research Proposal on Preprocessing Methods for Epilepsy Detection
Research Proposal on Neural Text Summarization
Research Proposal on Convolutional Deep Belief Networks
Research Proposal on Human-in-the-loop Deep Reinforcement Learning
Research Proposal on Multi-modal data analysis for multimedia classification
Research Proposal on Interactive Machine Learning with Human Feedback
Research Proposal on Online and Stream-based Regression
Research Proposal on Deep Learning for Compressed Sensing in Image and Video Processing
Research Proposal on Multi-view and multi-modal fusion for multi-class classification
Research Proposal on Deep Learning for Risk Management in Portfolio Optimization
Research Proposal on Sparse and Low-Rank Regression
Research Proposal on Epilepsy Prediction
Research Proposal on Deep Learning for Early Disease Detection in Plants
Research Proposal on Compressed Sensing with Deep Autoencoders
Research Proposal on Deep Learning for Multi-modal Representation in Healthcare
Research Proposal on Deep Learning for Algorithmic Trading and Portfolio Optimization
Research Proposal on Deep Learning for Predictive Sourcing in Supply Chain Management
Research Proposal on Cross-modal Representation Learning with Deep Learning
Research Proposal on Multi-agent Reinforcement Learning for Resource Allocation
Research Proposal on Cooperative Multi-agent Reinforcement Learning
Research Proposal on Plant Leaf Segmentation and Recognition with Deep Learning
Research Proposal on Multi-topic Modeling with Deep Learning
Research Proposal on Deep Learning for Topic Modeling
Research Proposal on Supply Chain Risk Management with Deep Learning
Research Proposal on Video Color Correction with Deep Learning
Research Proposal on Fine-grained Plant Leaf Recognition with Deep Learning
Research Proposal on Self-Supervised Image Denoising
Research Proposal on Multi-class Plant Disease Recognition with Deep Learning
Research Proposal on Deep Learning for Pest and Disease Detection in Crops
Research Proposal on Topic Modeling with Graph-based Approaches
Research Proposal on Video Restoration with Generative Adversarial Networks
Research Proposal on Precision Irrigation Scheduling with Deep Learning
Research Proposal on Deep learning for improved representation of multi-view sequential data
Research Proposal on Deep learning for predicting drug efficacy and toxicity
Research Proposal on Improved analysis of large-scale genomics data with deep learning
Research Proposal on Deep learning for summarization and visualization of multi-view sequential data
Research Proposal on Personalized cancer diagnosis using deep learning
Research Proposal on Deep transfer learning for cancer diagnosis
Research Proposal on Improved biometric recognition in low-resource settings using deep learning
Research Proposal on Deep learning for improved facial recognition
Research Proposal on Improved voice recognition with deep learning
Research Proposal on Deep learning for patient-specific dose modeling
Research Proposal on Neural rendering for product visualization in e-commerce
Research Proposal on Deep learning for computer-aided diagnosis
Research Proposal on Deep learning for improved medical image interpretation in radiology
Research Proposal on Summarization with Pre-trained Language Models
Research Proposal on Image super-resolution with attention mechanisms in deep learning
Research Proposal on Deep Learning for Cross-cultural Facial Expression Recognition
Research Proposal on Deep learning for dose prediction in radiotherapy
Research Proposal on Deep Learning for Drug-Drug Interaction Prediction
Research Proposal on Multi-modality medical image analysis with deep learning
Research Proposal on Adversarial Training for Fair and Robust Drug Response Prediction
Research Proposal on Improved segmentation of anatomical structures in radiotherapy using deep learning
Research Proposal on Transfer Learning for Facial Expression Recognition
Research Proposal on Abstractive Text Summarization
Research Proposal on Deep Learning for Livestock Health Monitoring
Research Proposal on Adversarial Training for Robust Facial Expression Recognition
Research Proposal on Multi-class Plant Leaf Recognition with Deep Learning
Research Proposal on Deep Learning for Object Detection and Classification in Microscopy Images
Research Proposal on Multi-modal Representation Learning for Image and Text
Research Proposal on Transfer Learning for Drug Response Prediction
Research Proposal on Deep Learning for Event Detection in Video Surveillance
Research Proposal on Time Series Data Analysis
Research Proposal on Face Detection and Landmark Localization
Research Proposal on Human-in-the-loop Anomaly Detection
Research Proposal on Machine Learning for Pattern Recognition
Research Proposal on Cross-Lingual Dialogue Systems
Research Proposal on Deep Learning for Facial Action Unit Detection
Research Proposal on Regression Model for Machine Learning
Research Proposal on Medical Concept Embedding
Research Proposal on Semantic parsing and question answering
Research Proposal on Deep learning Algorithms and Recent advancements
Research proposal on Natural Language Processing using Deep Learning
Research Proposal on Predictive Analytics to forecast future outcomes
Research proposal on Deep Learning-based Contextual Word Embedding for Text Generation
Research Proposal on Discourse Representation-Aware Text Generation using Deep Learning Model
Research Proposal on Deep Autoencoder based Text Generation for Natural Language
Research Proposal on Reinforcement Learning
Research Proposal Topics on Conversational Recommendation Systems
Research Proposal on Pre-trained Deep Learning Model based Text Generation
Research Proposal on Text Sequence Generation with Deep Transfer Learning
Research Proposal in Modeling Deep Semi-Supervised Learning for Non-Redundant Text Generation
Research Proposal in Utterances and Emoticons based Multi-Class Emotion Recognition
Research Proposal in Negation Handling with Contextual Representation for Sentiment Classification
Research Proposal on Deep Learning-based Emotion Classification
Research Proposal in Sentiment Classification in Social Media with Deep Contextual Embedding
Research Proposal in Deep Learning-based Emotion Classification in Conversational Text
Research Proposal on Attention Mechanism-based Argument Mining using Deep Neural Network
Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining
Research Proposal on Context-aware Argument Mining with Deep Semi-supervised Learning
Research Proposal in Deep Transfer Learning-based Sequential Keyphrase Generation
Research Proposal on Deep Bi-directional Text Analysis for Sarcasm Detection
Research Proposal in Emotion Transition Recognition with Contextual Embedding in Sarcasm Detection
Research Topic on Attention-based Sarcasm Detection with Psycholinguistic Sources
Research Proposal in Deep Attentive Model based Irony Text and Sarcasm Detection
Research Proposal Topic on Discourse Structure and Opinion based Argumentation Mining
Research Proposal in Sarcasm Detection using Syntactic and Semantic Feature Representation
Research Proposal in Multi-Class Behavior Modeling in Deep Learning-based Sarcasm Detection
Research Proposal on Deep Transfer Learning for Irony Detection
Research Proposal in Deep Neural Network-based Sarcasm Detection with Multi-Task Learning
Research Proposal in Deep Learning-Guided Credible User Identification using Social Network Structure and User-Generated Content
Research Proposal in Semi-supervised Misinformation Detection in Social Network
Research Proposal in Deep Contextualized Word Representation for Fake News Classification
Research Proposal on Self-Attentive Network-based Rumour Classification in Social Media
Research Proposal in Multi-Modal Rumour Classification with Deep Ensemble Learning
Research Proposal in Hybrid Deep Learning Model based Fake News Detection in Social Network
Research Proposal on Anomaly Detection by Applying the Machine Learning Technique
Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection
Research Proposal in Data Augmentation for Deep Learning-based Plant Disease Detection
Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection
Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification
Research Proposal in Class-Incremental Learning for Large-Scale IoT Prediction
Research Proposal in Time-series Forecasting using Weighted Incremental Learning
Research Proposal on Deep Reinforcement Learning based Time Series Prediction
Research Proposal in Federated Learning for Intelligent IoT Healthcare System
Research Proposal on Deep Learning based Stream Data Imputation for IoT Applications
Research Proposal on Deep Incremental Learning-based Cyber Security Threats Prediction
Research Proposal in Aspect based Opinion Mining for Personalized Recommendation
Research Proposal in Personalized Recommendation with Contextual Pre-Filtering
Research Proposal in Temporal and Spatial Context-based Group Recommendation
Research Proposal on Session based Recommender System with Representation learning
Research Proposal in Serendipity-aware Product Recommendation
Research Proposal in Deep Preference Prediction for Novelty and Diversity-Aware Top-N Recommendation
Research Proposal on Personalized Recommendation with Neural Attention Model
Research Proposal in Cross-domain Depression Detection in Social Media
Research Proposal in Emotional Feature Extraction in Depression Detection
Research Proposal in Contextual Recommendation with Deep Reinforcement Learning
Research Proposal on Deep Neural Network-based Cross-Domain Recommendation
Research Proposal in Multimodal Extraction for Depression Detection
Research Proposal on Early Depression Detection with Deep Attention Network
Research Proposal in Proactive Intrusion Detection in Cloud using Deep Reinforcement Learning
Research Proposal in Context Vector Representation of Text Sequence for Depression Detection
Research Proposal in Sparsity Handling in Recommender System with Transfer Learning
Research Proposal on Artificial Intelligence and Lexicon based Suicide Attempt Prevention
Research Proposal in Modeling Deep Neural Network for Mental Illness Detection from Healthcare Data
Research Proposal in Deep Learning-based Domain Adaptation for Recommendation
Research proposal on Emotion Classification using Deep Learning Models
Research Proposal in Topic Modeling for Personalized Product Recommendation
Research Proposal in Deep Reinforcement Learning based Resource Provisioning for Container-based Cloud Environment
Research Proposal in Energy and Delay-Aware Scheduling with Deep Learning in Fog Computing
Research Proposal in Spammer Detection in Social Network from the Advertiser Behavior Modeling
Research Proposal in Social Information based People Recommendation
Research Proposal in Deep Learning-based Advertiser Reliability Computation in Social Network
Research Proposal in Artificial Neural Network-based Missing Value Imputation in Disease Detection
Research Proposal in Deep Transfer Learning-based Disease Detection
Research Proposal in Aspect based Depressive Feature Extraction for Multi-Class Depression Classification
Research Proposal in Environmental Data-Aware Behavior Modeling and Depression Detection
Research Proposal on User Credibility Detection in Social Networks using Deep Learning Models
Research Proposal in Incremental Clustering Model for Malware Classification
Research Proposal in Deep Feature Extraction with One-class Classification for Anomaly Detection
Research Proposal in Fake News Dissemination Detection with Deep Neural Network
Research Proposal in Deep Learning-based Prediction for Mobile Cloud Offloading Decision-Making
Research Proposal in Hybrid Deep Learning-based Traffic Congestion Prediction in VANET
Research Proposal in Deep Ensemble Learning-based Android Malware Detection
Research Proposal in Proactive Prediction of Insider Threats using Deep Neural Network
Research Proposal in Delay-constrained Computation Offloading with Deep Learning in Fog Environment
Research Proposal in Latency-aware IoT Service Placement in Fog through Deep Learning Prediction
Research Proposal in Proactive Management of Edge Resources with Federated Learning
Research Proposal in Reinforcement Learning with Incentive Mechanism for Offloading Decision in Edge Computing
Research Proposal on Multi-Agent Offloading Decision in Fog Computing using Deep Reinforcement Learning
Research Proposal in Energy-efficient Edge Computation with Light-weight Deep Learning Model
Research Proposal Topic on Integrating Deep Learning with Rule-based Model for Sarcasm Detection
Research Proposal in Deep Incremental Learning based Intrusion Detection in IoT Environment
Research Proposal for Deep Learning based Explainable Recommendation Systems
Research Proposal in Federated Learning-based Cooperative Management of Fog Computation Resources
Research Proposal in Mobility-aware Collaborative Resource Management in Edge Intelligence System
Research Proposal in Deep learning-driven Computation Offloading in Fog-Cloud Environment
Research Proposal on Zero-day Attack Detection using Deep Neural Network
Research Proposal in Deep Learning-based Dynamic Workload Prediction in Cloud
Research Proposal in Federated Learning-based Dynamic Resource Scheduling in Edge-Cloud Environment
Research Proposal in Anomaly-aware Cloud Security Model with Deep Transfer Learning
Research Proposal in Federated Transfer Learning for Smart Decision-Making in Cloud Environment
Research Proposal on Explicit and Implicit Feedback-Aware Top-N Recommendation
Research Proposal in Disease Diagnosis using Unsupervised Deep Learning
Research Proposal for Hybrid Deep Learning based Recommendation
Research Proposal in Privacy Preservation using Deep Neural Network in Cloud Computing
Research Proposal in Machine learning Techniques for Social Media Analytics
Adversarial-aware Modeling of Federated Learning with Personalization
Research Proposal in Adversarial-aware Modeling of Federated Learning with Personalization
Research Proposal in Energy-efficient Federated Learning for Resource-Constrained Edge Environment
Research Proposal in Deep Learning-based Task Offloading for Dynamic Edge Computing
Research Proposal in Dynamic Joint Resource Allocation for Federated Edge Learning
Research Proposal in Deep Ensemble Learning-based Housing Price Prediction with Self-Attention Mechanism
Research Proposal in Pre-trained One-Class Anomaly Detection with Unsupervised Learning
Research Proposal in Unsupervised Deep Learning-based Anomaly Detection in IoT Environment
Research Proposal in Deep Federated Learning-based IoT Malware Detection
Research Proposal in Deep Learning-based Contextual Text Generation for Conversational Text
Research Proposal in Designing Intelligent Internet of Things with Deep Reinforcement Learning for Mobile Edge Computing
Research Proposal on Adaptive IoT Traffic Prediction with Deep Neural Network
Research Proposal in Deep Learning-based Congestion-aware Dynamic Routing in IoT Network
Machine Learning Based intelligent Trust Computational Model for IoT Applications
Research proposal in Ensemble Machine Learning for Big Data Stream Processing
One-Shot Learning
PhD Guidance and Support Enquiry
Masters and PhD Project Enquiry
Research Topics in Machine Learning
PhD Research Guidance in Machine Learning
Latest Research Papers in Machine Learning
Literature Survey in Machine Learning
Python Projects In Machine Learning
Python Sample Source Code
PhD Thesis in Machine Learning
PhD Projects in Machine Learning
Python Project Titles in Machine Learning
Leading Journals in Machine Learning
Leading Research Books in Machine Learning
Research Proposal Topics in Natural Language Processing (NLP)
Research Topics in Medical Machine Learning
Research Proposal On Pre-trained Word Embedding for Language Models
Research Proposal Topics on Deep learning Algorithms and Recent advancements
Research Topics in Federated Learning for Smart City Application
Research Topics in Computer Science
PhD Thesis Writing Services in Computer Science
PhD Paper Writing Services in Computer Science
How to Write a PhD Research Proposal in Computer Science
Ph.D Support Enquiry
Project Enquiry
Research Guidance in Machine Learning
Research Proposal in Machine Learning
Research Papers in Machine Learning
Ph.D Thesis in Machine Learning
Research Projects in Machine Learning
Project Titles in Machine Learning
Project Source Code in Machine Learning

Machine Learning: Algorithms, Real-World Applications and Research Directions

Review Article
Published: 22 March 2021
Volume 2 , article number 160 , ( 2021 )

Cite this article

Iqbal H. Sarker ORCID: orcid.org/0000-0003-1740-5517 1 , 2

491k Accesses

1392 Citations

23 Altmetric

Explore all metrics

In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning , which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

Machine learning and deep learning

Artificial intelligence-based solutions for climate change: a review

Avoid common mistakes on your manuscript.

Introduction

We live in the age of data, where everything around us is connected to a data source, and everything in our lives is digitally recorded [ 21 , 103 ]. For instance, the current electronic world has a wealth of various kinds of data, such as the Internet of Things (IoT) data, cybersecurity data, smart city data, business data, smartphone data, social media data, health data, COVID-19 data, and many more. The data can be structured, semi-structured, or unstructured, discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”, which is increasing day-by-day. Extracting insights from these data can be used to build various intelligent applications in the relevant domains. For instance, to build a data-driven automated and intelligent cybersecurity system, the relevant cybersecurity data can be used [ 105 ]; to build personalized context-aware smart mobile applications, the relevant mobile data can be used [ 103 ], and so on. Thus, the data management tools and techniques having the capability of extracting insights or useful knowledge from the data in a timely and intelligent way is urgently needed, on which the real-world applications are based.

The worldwide popularity score of various types of ML algorithms (supervised, unsupervised, semi-supervised, and reinforcement) in a range of 0 (min) to 100 (max) over time where x-axis represents the timestamp information and y-axis represents the corresponding score

Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [ 95 ]. ML usually provides systems with the ability to learn and enhance from experience automatically without being specifically programmed and is generally referred to as the most popular latest technologies in the fourth industrial revolution (4 IR or Industry 4.0) [ 103 , 105 ]. “Industry 4.0” [ 114 ] is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning automation. Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key. The learning algorithms can be categorized into four major types, such as supervised, unsupervised, semi-supervised, and reinforcement learning in the area [ 75 ], discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”. The popularity of these approaches to learning is increasing day-by-day, which is shown in Fig. 1 , based on data collected from Google Trends [ 4 ] over the last five years. The x - axis of the figure indicates the specific dates and the corresponding popularity score within the range of \(0 \; (minimum)\) to \(100 \; (maximum)\) has been shown in y - axis . According to Fig. 1 , the popularity indication values for these learning types are low in 2015 and are increasing day by day. These statistics motivate us to study on machine learning in this paper, which can play an important role in the real-world through Industry 4.0 automation.

In general, the effectiveness and the efficiency of a machine learning solution depend on the nature and characteristics of data and the performance of the learning algorithms . In the area of machine learning algorithms, classification analysis, regression, data clustering, feature engineering and dimensionality reduction, association rule learning, or reinforcement learning techniques exist to effectively build data-driven systems [ 41 , 125 ]. Besides, deep learning originated from the artificial neural network that can be used to intelligently analyze data, which is known as part of a wider family of machine learning approaches [ 96 ]. Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics [ 106 ]. Thus, it is important to understand the principles of various machine learning algorithms and their applicability to apply in various real-world application areas, such as IoT systems, cybersecurity services, business and recommendation systems, smart cities, healthcare and COVID-19, context-aware systems, sustainable agriculture, and many more that are explained briefly in Sect. “ Applications of Machine Learning ”.

Based on the importance and potentiality of “Machine Learning” to analyze the data mentioned above, in this paper, we provide a comprehensive view on various types of machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, the key contribution of this study is explaining the principles and potentiality of different machine learning techniques, and their applicability in various real-world application areas mentioned earlier. The purpose of this paper is, therefore, to provide a basic guide for those academia and industry people who want to study, research, and develop data-driven automated and intelligent systems in the relevant areas based on machine learning techniques.

The key contributions of this paper are listed as follows:

To define the scope of our study by taking into account the nature and characteristics of various types of real-world data and the capabilities of various learning techniques.

To provide a comprehensive view on machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.

To discuss the applicability of machine learning-based solutions in various real-world application domains.

To highlight and summarize the potential research directions within the scope of our study for intelligent data analysis and services.

The rest of the paper is organized as follows. The next section presents the types of data and machine learning algorithms in a broader sense and defines the scope of our study. We briefly discuss and explain different machine learning algorithms in the subsequent section followed by which various real-world application areas based on machine learning algorithms are discussed and summarized. In the penultimate section, we highlight several research issues and potential future directions, and the final section concludes this paper.

Types of Real-World Data and Machine Learning Techniques

Machine learning algorithms typically consume and process data to learn the related patterns about individuals, business processes, transactions, events, and so on. In the following, we discuss various types of real-world data as well as categories of machine learning algorithms.

Types of Real-World Data

Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [ 103 , 105 ]. Data can be of various forms, such as structured, semi-structured, or unstructured [ 41 , 72 ]. Besides, the “metadata” is another type that typically represents data about the data. In the following, we briefly discuss these types of data.

Structured: It has a well-defined structure, conforms to a data model following a standard order, which is highly organized and easily accessed, and used by an entity or a computer program. In well-defined schemes, such as relational databases, structured data are typically stored, i.e., in a tabular format. For instance, names, dates, addresses, credit card numbers, stock information, geolocation, etc. are examples of structured data.

Unstructured: On the other hand, there is no pre-defined format or organization for unstructured data, making it much more difficult to capture, process, and analyze, mostly containing text and multimedia material. For example, sensor data, emails, blog entries, wikis, and word processing documents, PDF files, audio files, videos, images, presentations, web pages, and many other types of business documents can be considered as unstructured data.

Semi-structured: Semi-structured data are not stored in a relational database like the structured data mentioned above, but it does have certain organizational properties that make it easier to analyze. HTML, XML, JSON documents, NoSQL databases, etc., are some examples of semi-structured data.

Metadata: It is not the normal form of data, but “data about data”. The primary difference between “data” and “metadata” is that data are simply the material that can classify, measure, or even document something relative to an organization’s data properties. On the other hand, metadata describes the relevant data information, giving it more significance for data users. A basic example of a document’s metadata might be the author, file size, date generated by the document, keywords to define the document, etc.

In the area of machine learning and data science, researchers use various widely used datasets for different purposes. These are, for example, cybersecurity datasets such as NSL-KDD [ 119 ], UNSW-NB15 [ 76 ], ISCX’12 [ 1 ], CIC-DDoS2019 [ 2 ], Bot-IoT [ 59 ], etc., smartphone datasets such as phone call logs [ 84 , 101 ], SMS Log [ 29 ], mobile application usages logs [ 137 ] [ 117 ], mobile phone notification logs [ 73 ] etc., IoT data [ 16 , 57 , 62 ], agriculture and e-commerce data [ 120 , 138 ], health data such as heart disease [ 92 ], diabetes mellitus [ 83 , 134 ], COVID-19 [ 43 , 74 ], etc., and many more in various application domains. The data can be in different types discussed above, which may vary from application to application in the real world. To analyze such data in a particular problem domain, and to extract the insights or useful knowledge from the data for building the real-world intelligent applications, different types of machine learning techniques can be used according to their learning capabilities, which is discussed in the following.

Types of Machine Learning Techniques

Machine Learning algorithms are mainly divided into four categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning [ 75 ], as shown in Fig. 2 . In the following, we briefly discuss each type of learning technique with the scope of their applicability to solve real-world problems.

Various types of machine learning techniques

Supervised: Supervised learning is typically the task of machine learning to learn a function that maps an input to an output based on sample input-output pairs [ 41 ]. It uses labeled training data and a collection of training examples to infer a function. Supervised learning is carried out when certain goals are identified to be accomplished from a certain set of inputs [ 105 ], i.e., a task-driven approach . The most common supervised tasks are “classification” that separates the data, and “regression” that fits the data. For instance, predicting the class label or sentiment of a piece of text, like a tweet or a product review, i.e., text classification, is an example of supervised learning.

Unsupervised: Unsupervised learning analyzes unlabeled datasets without the need for human interference, i.e., a data-driven process [ 41 ]. This is widely used for extracting generative features, identifying meaningful trends and structures, groupings in results, and exploratory purposes. The most common unsupervised learning tasks are clustering, density estimation, feature learning, dimensionality reduction, finding association rules, anomaly detection, etc.

Semi-supervised: Semi-supervised learning can be defined as a hybridization of the above-mentioned supervised and unsupervised methods, as it operates on both labeled and unlabeled data [ 41 , 105 ]. Thus, it falls between learning “without supervision” and learning “with supervision”. In the real world, labeled data could be rare in several contexts, and unlabeled data are numerous, where semi-supervised learning is useful [ 75 ]. The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the model. Some application areas where semi-supervised learning is used include machine translation, fraud detection, labeling data and text classification.

Reinforcement: Reinforcement learning is a type of machine learning algorithm that enables software agents and machines to automatically evaluate the optimal behavior in a particular context or environment to improve its efficiency [ 52 ], i.e., an environment-driven approach . This type of learning is based on reward or penalty, and its ultimate goal is to use insights obtained from environmental activists to take action to increase the reward or minimize the risk [ 75 ]. It is a powerful tool for training AI models that can help increase automation or optimize the operational efficiency of sophisticated systems such as robotics, autonomous driving tasks, manufacturing and supply chain logistics, however, not preferable to use it for solving the basic or straightforward problems.

Thus, to build effective models in various application areas different types of machine learning techniques can play a significant role according to their learning capabilities, depending on the nature of the data discussed earlier, and the target outcome. In Table 1 , we summarize various types of machine learning techniques with examples. In the following, we provide a comprehensive view of machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.

Machine Learning Tasks and Algorithms

In this section, we discuss various machine learning algorithms that include classification analysis, regression analysis, data clustering, association rule learning, feature engineering for dimensionality reduction, as well as deep learning methods. A general structure of a machine learning-based predictive model has been shown in Fig. 3 , where the model is trained from historical data in phase 1 and the outcome is generated in phase 2 for the new test data.

A general structure of a machine learning based predictive model considering both the training and testing phase

Classification Analysis

Classification is regarded as a supervised learning method in machine learning, referring to a problem of predictive modeling as well, where a class label is predicted for a given example [ 41 ]. Mathematically, it maps a function ( f ) from input variables ( X ) to output variables ( Y ) as target, label or categories. To predict the class of given data points, it can be carried out on structured or unstructured data. For example, spam detection such as “spam” and “not spam” in email service providers can be a classification problem. In the following, we summarize the common classification problems.

Binary classification: It refers to the classification tasks having two class labels such as “true and false” or “yes and no” [ 41 ]. In such binary classification tasks, one class could be the normal state, while the abnormal state could be another class. For instance, “cancer not detected” is the normal state of a task that involves a medical test, and “cancer detected” could be considered as the abnormal state. Similarly, “spam” and “not spam” in the above example of email service providers are considered as binary classification.

Multiclass classification: Traditionally, this refers to those classification tasks having more than two class labels [ 41 ]. The multiclass classification does not have the principle of normal and abnormal outcomes, unlike binary classification tasks. Instead, within a range of specified classes, examples are classified as belonging to one. For example, it can be a multiclass classification task to classify various types of network attacks in the NSL-KDD [ 119 ] dataset, where the attack categories are classified into four class labels, such as DoS (Denial of Service Attack), U2R (User to Root Attack), R2L (Root to Local Attack), and Probing Attack.

Multi-label classification: In machine learning, multi-label classification is an important consideration where an example is associated with several classes or labels. Thus, it is a generalization of multiclass classification, where the classes involved in the problem are hierarchically structured, and each example may simultaneously belong to more than one class in each hierarchical level, e.g., multi-level text classification. For instance, Google news can be presented under the categories of a “city name”, “technology”, or “latest news”, etc. Multi-label classification includes advanced machine learning algorithms that support predicting various mutually non-exclusive classes or labels, unlike traditional classification tasks where class labels are mutually exclusive [ 82 ].

Many classification algorithms have been proposed in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the most common and popular methods that are used widely in various application areas.

Naive Bayes (NB): The naive Bayes algorithm is based on the Bayes’ theorem with the assumption of independence between each pair of features [ 51 ]. It works well and can be used for both binary and multi-class categories in many real-world situations, such as document or text classification, spam filtering, etc. To effectively classify the noisy instances in the data and to construct a robust prediction model, the NB classifier can be used [ 94 ]. The key benefit is that, compared to more sophisticated approaches, it needs a small amount of training data to estimate the necessary parameters and quickly [ 82 ]. However, its performance may affect due to its strong assumptions on features independence. Gaussian, Multinomial, Complement, Bernoulli, and Categorical are the common variants of NB classifier [ 82 ].

Linear Discriminant Analysis (LDA): Linear Discriminant Analysis (LDA) is a linear decision boundary classifier created by fitting class conditional densities to data and applying Bayes’ rule [ 51 , 82 ]. This method is also known as a generalization of Fisher’s linear discriminant, which projects a given dataset into a lower-dimensional space, i.e., a reduction of dimensionality that minimizes the complexity of the model or reduces the resulting model’s computational costs. The standard LDA model usually suits each class with a Gaussian density, assuming that all classes share the same covariance matrix [ 82 ]. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which seek to express one dependent variable as a linear combination of other features or measurements.

Logistic regression (LR): Another common probabilistic based statistical model used to solve classification issues in machine learning is Logistic Regression (LR) [ 64 ]. Logistic regression typically uses a logistic function to estimate the probabilities, which is also referred to as the mathematically defined sigmoid function in Eq. 1 . It can overfit high-dimensional datasets and works well when the dataset can be separated linearly. The regularization (L1 and L2) techniques [ 82 ] can be used to avoid over-fitting in such scenarios. The assumption of linearity between the dependent and independent variables is considered as a major drawback of Logistic Regression. It can be used for both classification and regression problems, but it is more commonly used for classification.

K-nearest neighbors (KNN): K-Nearest Neighbors (KNN) [ 9 ] is an “instance-based learning” or non-generalizing learning, also known as a “lazy learning” algorithm. It does not focus on constructing a general internal model; instead, it stores all instances corresponding to training data in n -dimensional space. KNN uses data and classifies new data points based on similarity measures (e.g., Euclidean distance function) [ 82 ]. Classification is computed from a simple majority vote of the k nearest neighbors of each point. It is quite robust to noisy training data, and accuracy depends on the data quality. The biggest issue with KNN is to choose the optimal number of neighbors to be considered. KNN can be used both for classification as well as regression.

Support vector machine (SVM): In machine learning, another common technique that can be used for classification, regression, or other tasks is a support vector machine (SVM) [ 56 ]. In high- or infinite-dimensional space, a support vector machine constructs a hyper-plane or set of hyper-planes. Intuitively, the hyper-plane, which has the greatest distance from the nearest training data points in any class, achieves a strong separation since, in general, the greater the margin, the lower the classifier’s generalization error. It is effective in high-dimensional spaces and can behave differently based on different mathematical functions known as the kernel. Linear, polynomial, radial basis function (RBF), sigmoid, etc., are the popular kernel functions used in SVM classifier [ 82 ]. However, when the data set contains more noise, such as overlapping target classes, SVM does not perform well.

Decision tree (DT): Decision tree (DT) [ 88 ] is a well-known non-parametric supervised learning method. DT learning methods are used for both the classification and regression tasks [ 82 ]. ID3 [ 87 ], C4.5 [ 88 ], and CART [ 20 ] are well known for DT algorithms. Moreover, recently proposed BehavDT [ 100 ], and IntrudTree [ 97 ] by Sarker et al. are effective in the relevant application domains, such as user behavior analytics and cybersecurity analytics, respectively. By sorting down the tree from the root to some leaf nodes, as shown in Fig. 4 , DT classifies the instances. Instances are classified by checking the attribute defined by that node, starting at the root node of the tree, and then moving down the tree branch corresponding to the attribute value. For splitting, the most popular criteria are “gini” for the Gini impurity and “entropy” for the information gain that can be expressed mathematically as [ 82 ].

An example of a decision tree structure

An example of a random forest structure considering multiple decision trees

Random forest (RF): A random forest classifier [ 19 ] is well known as an ensemble classification technique that is used in the field of machine learning and data science in various application areas. This method uses “parallel ensembling” which fits several decision tree classifiers in parallel, as shown in Fig. 5 , on different data set sub-samples and uses majority voting or averages for the outcome or final result. It thus minimizes the over-fitting problem and increases the prediction accuracy and control [ 82 ]. Therefore, the RF learning model with multiple decision trees is typically more accurate than a single decision tree based model [ 106 ]. To build a series of decision trees with controlled variation, it combines bootstrap aggregation (bagging) [ 18 ] and random feature selection [ 11 ]. It is adaptable to both classification and regression problems and fits well for both categorical and continuous values.

Adaptive Boosting (AdaBoost): Adaptive Boosting (AdaBoost) is an ensemble learning process that employs an iterative approach to improve poor classifiers by learning from their errors. This is developed by Yoav Freund et al. [ 35 ] and also known as “meta-learning”. Unlike the random forest that uses parallel ensembling, Adaboost uses “sequential ensembling”. It creates a powerful classifier by combining many poorly performing classifiers to obtain a good classifier of high accuracy. In that sense, AdaBoost is called an adaptive classifier by significantly improving the efficiency of the classifier, but in some instances, it can trigger overfits. AdaBoost is best used to boost the performance of decision trees, base estimator [ 82 ], on binary classification problems, however, is sensitive to noisy data and outliers.

Extreme gradient boosting (XGBoost): Gradient Boosting, like Random Forests [ 19 ] above, is an ensemble learning algorithm that generates a final model based on a series of individual models, typically decision trees. The gradient is used to minimize the loss function, similar to how neural networks [ 41 ] use gradient descent to optimize weights. Extreme Gradient Boosting (XGBoost) is a form of gradient boosting that takes more detailed approximations into account when determining the best model [ 82 ]. It computes second-order gradients of the loss function to minimize loss and advanced regularization (L1 and L2) [ 82 ], which reduces over-fitting, and improves model generalization and performance. XGBoost is fast to interpret and can handle large-sized datasets well.

Stochastic gradient descent (SGD): Stochastic gradient descent (SGD) [ 41 ] is an iterative method for optimizing an objective function with appropriate smoothness properties, where the word ‘stochastic’ refers to random probability. This reduces the computational burden, particularly in high-dimensional optimization problems, allowing for faster iterations in exchange for a lower convergence rate. A gradient is the slope of a function that calculates a variable’s degree of change in response to another variable’s changes. Mathematically, the Gradient Descent is a convex function whose output is a partial derivative of a set of its input parameters. Let, \(\alpha\) is the learning rate, and \(J_i\) is the training example cost of \(i \mathrm{th}\) , then Eq. ( 4 ) represents the stochastic gradient descent weight update method at the \(j^\mathrm{th}\) iteration. In large-scale and sparse machine learning, SGD has been successfully applied to problems often encountered in text classification and natural language processing [ 82 ]. However, SGD is sensitive to feature scaling and needs a range of hyperparameters, such as the regularization parameter and the number of iterations.

Rule-based classification : The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction. Several classification algorithms such as Zero-R [ 125 ], One-R [ 47 ], decision trees [ 87 , 88 ], DTNB [ 110 ], Ripple Down Rule learner (RIDOR) [ 125 ], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [ 126 ] exist with the ability of rule generation. The decision tree is one of the most common rule-based classification algorithms among these techniques because it has several advantages, such as being easier to interpret; the ability to handle high-dimensional data; simplicity and speed; good accuracy; and the capability to produce rules for human clear and understandable classification [ 127 ] [ 128 ]. The decision tree-based rules also provide significant accuracy in a prediction model for unseen test cases [ 106 ]. Since the rules are easily interpretable, these rule-based classifiers are often used to produce descriptive models that can describe a system including the entities and their relationships.

Classification vs. regression. In classification the dotted line represents a linear boundary that separates the two classes; in regression, the dotted line models the linear relationship between the two variables

Regression Analysis

Regression analysis includes several methods of machine learning that allow to predict a continuous ( y ) result variable based on the value of one or more ( x ) predictor variables [ 41 ]. The most significant distinction between classification and regression is that classification predicts distinct class labels, while regression facilitates the prediction of a continuous quantity. Figure 6 shows an example of how classification is different with regression models. Some overlaps are often found between the two types of machine learning algorithms. Regression models are now widely used in a variety of fields, including financial forecasting or prediction, cost estimation, trend analysis, marketing, time series estimation, drug response modeling, and many more. Some of the familiar types of regression algorithms are linear, polynomial, lasso and ridge regression, etc., which are explained briefly in the following.

Simple and multiple linear regression: This is one of the most popular ML modeling techniques as well as a well-known regression technique. In this technique, the dependent variable is continuous, the independent variable(s) can be continuous or discrete, and the form of the regression line is linear. Linear regression creates a relationship between the dependent variable ( Y ) and one or more independent variables ( X ) (also known as regression line) using the best fit straight line [ 41 ]. It is defined by the following equations:

where a is the intercept, b is the slope of the line, and e is the error term. This equation can be used to predict the value of the target variable based on the given predictor variable(s). Multiple linear regression is an extension of simple linear regression that allows two or more predictor variables to model a response variable, y, as a linear function [ 41 ] defined in Eq. 6 , whereas simple linear regression has only 1 independent variable, defined in Eq. 5 .

Polynomial regression: Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is not linear, but is the polynomial degree of \(n^\mathrm{th}\) in x [ 82 ]. The equation for polynomial regression is also derived from linear regression (polynomial regression of degree 1) equation, which is defined as below:

Here, y is the predicted/target output, \(b_0, b_1,... b_n\) are the regression coefficients, x is an independent/ input variable. In simple words, we can say that if data are not distributed linearly, instead it is \(n^\mathrm{th}\) degree of polynomial then we use polynomial regression to get desired output.

LASSO and ridge regression: LASSO and Ridge regression are well known as powerful techniques which are typically used for building learning models in presence of a large number of features, due to their capability to preventing over-fitting and reducing the complexity of the model. The LASSO (least absolute shrinkage and selection operator) regression model uses L 1 regularization technique [ 82 ] that uses shrinkage, which penalizes “absolute value of magnitude of coefficients” ( L 1 penalty). As a result, LASSO appears to render coefficients to absolute zero. Thus, LASSO regression aims to find the subset of predictors that minimizes the prediction error for a quantitative response variable. On the other hand, ridge regression uses L 2 regularization [ 82 ], which is the “squared magnitude of coefficients” ( L 2 penalty). Thus, ridge regression forces the weights to be small but never sets the coefficient value to zero, and does a non-sparse solution. Overall, LASSO regression is useful to obtain a subset of predictors by eliminating less important features, and ridge regression is useful when a data set has “multicollinearity” which refers to the predictors that are correlated with other predictors.

Cluster Analysis

Cluster analysis, also known as clustering, is an unsupervised machine learning technique for identifying and grouping related data points in large datasets without concern for the specific outcome. It does grouping a collection of objects in such a way that objects in the same category, called a cluster, are in some sense more similar to each other than objects in other groups [ 41 ]. It is often used as a data analysis technique to discover interesting trends or patterns in data, e.g., groups of consumers based on their behavior. In a broad range of application areas, such as cybersecurity, e-commerce, mobile data processing, health analytics, user modeling and behavioral analytics, clustering can be used. In the following, we briefly discuss and summarize various types of clustering methods.

Partitioning methods: Based on the features and similarities in the data, this clustering approach categorizes the data into multiple groups or clusters. The data scientists or analysts typically determine the number of clusters either dynamically or statically depending on the nature of the target applications, to produce for the methods of clustering. The most common clustering algorithms based on partitioning methods are K-means [ 69 ], K-Mediods [ 80 ], CLARA [ 55 ] etc.

Density-based methods: To identify distinct groups or clusters, it uses the concept that a cluster in the data space is a contiguous region of high point density isolated from other such clusters by contiguous regions of low point density. Points that are not part of a cluster are considered as noise. The typical clustering algorithms based on density are DBSCAN [ 32 ], OPTICS [ 12 ] etc. The density-based methods typically struggle with clusters of similar density and high dimensionality data.

Hierarchical-based methods: Hierarchical clustering typically seeks to construct a hierarchy of clusters, i.e., the tree structure. Strategies for hierarchical clustering generally fall into two types: (i) Agglomerative—a “bottom-up” approach in which each observation begins in its cluster and pairs of clusters are combined as one, moves up the hierarchy, and (ii) Divisive—a “top-down” approach in which all observations begin in one cluster and splits are performed recursively, moves down the hierarchy, as shown in Fig 7 . Our earlier proposed BOTS technique, Sarker et al. [ 102 ] is an example of a hierarchical, particularly, bottom-up clustering algorithm.

Grid-based methods: To deal with massive datasets, grid-based clustering is especially suitable. To obtain clusters, the principle is first to summarize the dataset with a grid representation and then to combine grid cells. STING [ 122 ], CLIQUE [ 6 ], etc. are the standard algorithms of grid-based clustering.

Model-based methods: There are mainly two types of model-based clustering algorithms: one that uses statistical learning, and the other based on a method of neural network learning [ 130 ]. For instance, GMM [ 89 ] is an example of a statistical learning method, and SOM [ 22 ] [ 96 ] is an example of a neural network learning method.

Constraint-based methods: Constrained-based clustering is a semi-supervised approach to data clustering that uses constraints to incorporate domain knowledge. Application or user-oriented constraints are incorporated to perform the clustering. The typical algorithms of this kind of clustering are COP K-means [ 121 ], CMWK-Means [ 27 ], etc.

A graphical interpretation of the widely-used hierarchical clustering (Bottom-up and top-down) technique

Many clustering algorithms have been proposed with the ability to grouping data in machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

K-means clustering: K-means clustering [ 69 ] is a fast, robust, and simple algorithm that provides reliable results when data sets are well-separated from each other. The data points are allocated to a cluster in this algorithm in such a way that the amount of the squared distance between the data points and the centroid is as small as possible. In other words, the K-means algorithm identifies the k number of centroids and then assigns each data point to the nearest cluster while keeping the centroids as small as possible. Since it begins with a random selection of cluster centers, the results can be inconsistent. Since extreme values can easily affect a mean, the K-means clustering algorithm is sensitive to outliers. K-medoids clustering [ 91 ] is a variant of K-means that is more robust to noises and outliers.

Mean-shift clustering: Mean-shift clustering [ 37 ] is a nonparametric clustering technique that does not require prior knowledge of the number of clusters or constraints on cluster shape. Mean-shift clustering aims to discover “blobs” in a smooth distribution or density of samples [ 82 ]. It is a centroid-based algorithm that works by updating centroid candidates to be the mean of the points in a given region. To form the final set of centroids, these candidates are filtered in a post-processing stage to remove near-duplicates. Cluster analysis in computer vision and image processing are examples of application domains. Mean Shift has the disadvantage of being computationally expensive. Moreover, in cases of high dimension, where the number of clusters shifts abruptly, the mean-shift algorithm does not work well.

DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) [ 32 ] is a base algorithm for density-based clustering which is widely used in data mining and machine learning. This is known as a non-parametric density-based clustering technique for separating high-density clusters from low-density clusters that are used in model building. DBSCAN’s main idea is that a point belongs to a cluster if it is close to many points from that cluster. It can find clusters of various shapes and sizes in a vast volume of data that is noisy and contains outliers. DBSCAN, unlike k-means, does not require a priori specification of the number of clusters in the data and can find arbitrarily shaped clusters. Although k-means is much faster than DBSCAN, it is efficient at finding high-density regions and outliers, i.e., is robust to outliers.

GMM clustering: Gaussian mixture models (GMMs) are often used for data clustering, which is a distribution-based clustering algorithm. A Gaussian mixture model is a probabilistic model in which all the data points are produced by a mixture of a finite number of Gaussian distributions with unknown parameters [ 82 ]. To find the Gaussian parameters for each cluster, an optimization algorithm called expectation-maximization (EM) [ 82 ] can be used. EM is an iterative method that uses a statistical model to estimate the parameters. In contrast to k-means, Gaussian mixture models account for uncertainty and return the likelihood that a data point belongs to one of the k clusters. GMM clustering is more robust than k-means and works well even with non-linear data distributions.

Agglomerative hierarchical clustering: The most common method of hierarchical clustering used to group objects in clusters based on their similarity is agglomerative clustering. This technique uses a bottom-up approach, where each object is first treated as a singleton cluster by the algorithm. Following that, pairs of clusters are merged one by one until all clusters have been merged into a single large cluster containing all objects. The result is a dendrogram, which is a tree-based representation of the elements. Single linkage [ 115 ], Complete linkage [ 116 ], BOTS [ 102 ] etc. are some examples of such techniques. The main advantage of agglomerative hierarchical clustering over k-means is that the tree-structure hierarchy generated by agglomerative clustering is more informative than the unstructured collection of flat clusters returned by k-means, which can help to make better decisions in the relevant application areas.

Dimensionality Reduction and Feature Learning

In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction. The primary distinction between the selection and extraction of features is that the “feature selection” keeps a subset of the original features [ 97 ], while “feature extraction” creates brand new ones [ 98 ]. In the following, we briefly discuss these techniques.

Feature selection: The selection of features, also known as the selection of variables or attributes in the data, is the process of choosing a subset of unique features (variables, predictors) to use in building machine learning and data science model. It decreases a model’s complexity by eliminating the irrelevant or less important features and allows for faster training of machine learning algorithms. A right and optimal subset of the selected features in a problem domain is capable to minimize the overfitting problem through simplifying and generalizing the model as well as increases the model’s accuracy [ 97 ]. Thus, “feature selection” [ 66 , 99 ] is considered as one of the primary concepts in machine learning that greatly affects the effectiveness and efficiency of the target machine learning model. Chi-squared test, Analysis of variance (ANOVA) test, Pearson’s correlation coefficient, recursive feature elimination, are some popular techniques that can be used for feature selection.

Feature extraction: In a machine learning-based model or system, feature extraction techniques usually provide a better understanding of the data, a way to improve prediction accuracy, and to reduce computational cost or training time. The aim of “feature extraction” [ 66 , 99 ] is to reduce the number of features in a dataset by generating new ones from the existing ones and then discarding the original features. The majority of the information found in the original set of features can then be summarized using this new reduced set of features. For instance, principal components analysis (PCA) is often used as a dimensionality-reduction technique to extract a lower-dimensional space creating new brand components from the existing features in a dataset [ 98 ].

Many algorithms have been proposed to reduce data dimensions in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

Variance threshold: A simple basic approach to feature selection is the variance threshold [ 82 ]. This excludes all features of low variance, i.e., all features whose variance does not exceed the threshold. It eliminates all zero-variance characteristics by default, i.e., characteristics that have the same value in all samples. This feature selection algorithm looks only at the ( X ) features, not the ( y ) outputs needed, and can, therefore, be used for unsupervised learning.

Pearson correlation: Pearson’s correlation is another method to understand a feature’s relation to the response variable and can be used for feature selection [ 99 ]. This method is also used for finding the association between the features in a dataset. The resulting value is \([-1, 1]\) , where \(-1\) means perfect negative correlation, \(+1\) means perfect positive correlation, and 0 means that the two variables do not have a linear correlation. If two random variables represent X and Y , then the correlation coefficient between X and Y is defined as [ 41 ]

ANOVA: Analysis of variance (ANOVA) is a statistical tool used to verify the mean values of two or more groups that differ significantly from each other. ANOVA assumes a linear relationship between the variables and the target and the variables’ normal distribution. To statistically test the equality of means, the ANOVA method utilizes F tests. For feature selection, the results ‘ANOVA F value’ [ 82 ] of this test can be used where certain features independent of the goal variable can be omitted.

Chi square: The chi-square \({\chi }^2\) [ 82 ] statistic is an estimate of the difference between the effects of a series of events or variables observed and expected frequencies. The magnitude of the difference between the real and observed values, the degrees of freedom, and the sample size depends on \({\chi }^2\) . The chi-square \({\chi }^2\) is commonly used for testing relationships between categorical variables. If \(O_i\) represents observed value and \(E_i\) represents expected value, then

Recursive feature elimination (RFE): Recursive Feature Elimination (RFE) is a brute force approach to feature selection. RFE [ 82 ] fits the model and removes the weakest feature before it meets the specified number of features. Features are ranked by the coefficients or feature significance of the model. RFE aims to remove dependencies and collinearity in the model by recursively removing a small number of features per iteration.

Model-based selection: To reduce the dimensionality of the data, linear models penalized with the L 1 regularization can be used. Least absolute shrinkage and selection operator (Lasso) regression is a type of linear regression that has the property of shrinking some of the coefficients to zero [ 82 ]. Therefore, that feature can be removed from the model. Thus, the penalized lasso regression method, often used in machine learning to select the subset of variables. Extra Trees Classifier [ 82 ] is an example of a tree-based estimator that can be used to compute impurity-based function importance, which can then be used to discard irrelevant features.

Principal component analysis (PCA): Principal component analysis (PCA) is a well-known unsupervised learning approach in the field of machine learning and data science. PCA is a mathematical technique that transforms a set of correlated variables into a set of uncorrelated variables known as principal components [ 48 , 81 ]. Figure 8 shows an example of the effect of PCA on various dimensions space, where Fig. 8 a shows the original features in 3D space, and Fig. 8 b shows the created principal components PC1 and PC2 onto a 2D plane, and 1D line with the principal component PC1 respectively. Thus, PCA can be used as a feature extraction technique that reduces the dimensionality of the datasets, and to build an effective machine learning model [ 98 ]. Technically, PCA identifies the completely transformed with the highest eigenvalues of a covariance matrix and then uses those to project the data into a new subspace of equal or fewer dimensions [ 82 ].

An example of a principal component analysis (PCA) and created principal components PC1 and PC2 in different dimension space

Association Rule Learning

Association rule learning is a rule-based machine learning approach to discover interesting relationships, “IF-THEN” statements, in large datasets between variables [ 7 ]. One example is that “if a customer buys a computer or laptop (an item), s/he is likely to also buy anti-virus software (another item) at the same time”. Association rules are employed today in many application areas, including IoT services, medical diagnosis, usage behavior analytics, web usage mining, smartphone applications, cybersecurity applications, and bioinformatics. In comparison to sequence mining, association rule learning does not usually take into account the order of things within or across transactions. A common way of measuring the usefulness of association rules is to use its parameter, the ‘support’ and ‘confidence’, which is introduced in [ 7 ].

In the data mining literature, many association rule learning methods have been proposed, such as logic dependent [ 34 ], frequent pattern based [ 8 , 49 , 68 ], and tree-based [ 42 ]. The most popular association rule learning algorithms are summarized below.

AIS and SETM: AIS is the first algorithm proposed by Agrawal et al. [ 7 ] for association rule mining. The AIS algorithm’s main downside is that too many candidate itemsets are generated, requiring more space and wasting a lot of effort. This algorithm calls for too many passes over the entire dataset to produce the rules. Another approach SETM [ 49 ] exhibits good performance and stable behavior with execution time; however, it suffers from the same flaw as the AIS algorithm.

Apriori: For generating association rules for a given dataset, Agrawal et al. [ 8 ] proposed the Apriori, Apriori-TID, and Apriori-Hybrid algorithms. These later algorithms outperform the AIS and SETM mentioned above due to the Apriori property of frequent itemset [ 8 ]. The term ‘Apriori’ usually refers to having prior knowledge of frequent itemset properties. Apriori uses a “bottom-up” approach, where it generates the candidate itemsets. To reduce the search space, Apriori uses the property “all subsets of a frequent itemset must be frequent; and if an itemset is infrequent, then all its supersets must also be infrequent”. Another approach predictive Apriori [ 108 ] can also generate rules; however, it receives unexpected results as it combines both the support and confidence. The Apriori [ 8 ] is the widely applicable techniques in mining association rules.

ECLAT: This technique was proposed by Zaki et al. [ 131 ] and stands for Equivalence Class Clustering and bottom-up Lattice Traversal. ECLAT uses a depth-first search to find frequent itemsets. In contrast to the Apriori [ 8 ] algorithm, which represents data in a horizontal pattern, it represents data vertically. Hence, the ECLAT algorithm is more efficient and scalable in the area of association rule learning. This algorithm is better suited for small and medium datasets whereas the Apriori algorithm is used for large datasets.

FP-Growth: Another common association rule learning technique based on the frequent-pattern tree (FP-tree) proposed by Han et al. [ 42 ] is Frequent Pattern Growth, known as FP-Growth. The key difference with Apriori is that while generating rules, the Apriori algorithm [ 8 ] generates frequent candidate itemsets; on the other hand, the FP-growth algorithm [ 42 ] prevents candidate generation and thus produces a tree by the successful strategy of ‘divide and conquer’ approach. Due to its sophistication, however, FP-Tree is challenging to use in an interactive mining environment [ 133 ]. Thus, the FP-Tree would not fit into memory for massive data sets, making it challenging to process big data as well. Another solution is RARM (Rapid Association Rule Mining) proposed by Das et al. [ 26 ] but faces a related FP-tree issue [ 133 ].

ABC-RuleMiner: A rule-based machine learning method, recently proposed in our earlier paper, by Sarker et al. [ 104 ], to discover the interesting non-redundant rules to provide real-world intelligent services. This algorithm effectively identifies the redundancy in associations by taking into account the impact or precedence of the related contextual features and discovers a set of non-redundant association rules. This algorithm first constructs an association generation tree (AGT), a top-down approach, and then extracts the association rules through traversing the tree. Thus, ABC-RuleMiner is more potent than traditional rule-based methods in terms of both non-redundant rule generation and intelligent decision-making, particularly in a context-aware smart computing environment, where human or user preferences are involved.

Among the association rule learning techniques discussed above, Apriori [ 8 ] is the most widely used algorithm for discovering association rules from a given dataset [ 133 ]. The main strength of the association learning technique is its comprehensiveness, as it generates all associations that satisfy the user-specified constraints, such as minimum support and confidence value. The ABC-RuleMiner approach [ 104 ] discussed earlier could give significant results in terms of non-redundant rule generation and intelligent decision-making for the relevant application areas in the real world.

Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique that allows an agent to learn by trial and error in an interactive environment using input from its actions and experiences. Unlike supervised learning, which is based on given sample data or examples, the RL method is based on interacting with the environment. The problem to be solved in reinforcement learning (RL) is defined as a Markov Decision Process (MDP) [ 86 ], i.e., all about sequentially making decisions. An RL problem typically includes four elements such as Agent, Environment, Rewards, and Policy.

RL can be split roughly into Model-based and Model-free techniques. Model-based RL is the process of inferring optimal behavior from a model of the environment by performing actions and observing the results, which include the next state and the immediate reward [ 85 ]. AlphaZero, AlphaGo [ 113 ] are examples of the model-based approaches. On the other hand, a model-free approach does not use the distribution of the transition probability and the reward function associated with MDP. Q-learning, Deep Q Network, Monte Carlo Control, SARSA (State–Action–Reward–State–Action), etc. are some examples of model-free algorithms [ 52 ]. The policy network, which is required for model-based RL but not for model-free, is the key difference between model-free and model-based learning. In the following, we discuss the popular RL algorithms.

Monte Carlo methods: Monte Carlo techniques, or Monte Carlo experiments, are a wide category of computational algorithms that rely on repeated random sampling to obtain numerical results [ 52 ]. The underlying concept is to use randomness to solve problems that are deterministic in principle. Optimization, numerical integration, and making drawings from the probability distribution are the three problem classes where Monte Carlo techniques are most commonly used.

Q-learning: Q-learning is a model-free reinforcement learning algorithm for learning the quality of behaviors that tell an agent what action to take under what conditions [ 52 ]. It does not need a model of the environment (hence the term “model-free”), and it can deal with stochastic transitions and rewards without the need for adaptations. The ‘Q’ in Q-learning usually stands for quality, as the algorithm calculates the maximum expected rewards for a given behavior in a given state.

Deep Q-learning: The basic working step in Deep Q-Learning [ 52 ] is that the initial state is fed into the neural network, which returns the Q-value of all possible actions as an output. Still, when we have a reasonably simple setting to overcome, Q-learning works well. However, when the number of states and actions becomes more complicated, deep learning can be used as a function approximator.

Reinforcement learning, along with supervised and unsupervised learning, is one of the basic machine learning paradigms. RL can be used to solve numerous real-world problems in various fields, such as game theory, control theory, operations analysis, information theory, simulation-based optimization, manufacturing, supply chain logistics, multi-agent systems, swarm intelligence, aircraft control, robot motion control, and many more.

Artificial Neural Network and Deep Learning

Deep learning is part of a wider family of artificial neural networks (ANN)-based machine learning approaches with representation learning. Deep learning provides a computational architecture by combining several processing layers, such as input, hidden, and output layers, to learn from data [ 41 ]. The main advantage of deep learning over traditional machine learning methods is its better performance in several cases, particularly learning from large datasets [ 105 , 129 ]. Figure 9 shows a general performance of deep learning over machine learning considering the increasing amount of data. However, it may vary depending on the data characteristics and experimental set up.

Machine learning and deep learning performance in general with the amount of data

The most common deep learning algorithms are: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN, or ConvNet), Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) [ 96 ]. In the following, we discuss various types of deep learning methods that can be used to build effective data-driven models for various purposes.

A structure of an artificial neural network modeling with multiple processing layers

MLP: The base architecture of deep learning, which is also known as the feed-forward artificial neural network, is called a multilayer perceptron (MLP) [ 82 ]. A typical MLP is a fully connected network consisting of an input layer, one or more hidden layers, and an output layer, as shown in Fig. 10 . Each node in one layer connects to each node in the following layer at a certain weight. MLP utilizes the “Backpropagation” technique [ 41 ], the most “fundamental building block” in a neural network, to adjust the weight values internally while building the model. MLP is sensitive to scaling features and allows a variety of hyperparameters to be tuned, such as the number of hidden layers, neurons, and iterations, which can result in a computationally costly model.

CNN or ConvNet: The convolution neural network (CNN) [ 65 ] enhances the design of the standard ANN, consisting of convolutional layers, pooling layers, as well as fully connected layers, as shown in Fig. 11 . As it takes the advantage of the two-dimensional (2D) structure of the input data, it is typically broadly used in several areas such as image and video recognition, image processing and classification, medical image analysis, natural language processing, etc. While CNN has a greater computational burden, without any manual intervention, it has the advantage of automatically detecting the important features, and hence CNN is considered to be more powerful than conventional ANN. A number of advanced deep learning models based on CNN can be used in the field, such as AlexNet [ 60 ], Xception [ 24 ], Inception [ 118 ], Visual Geometry Group (VGG) [ 44 ], ResNet [ 45 ], etc.

LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning [ 38 ]. LSTM has feedback links, unlike normal feed-forward neural networks. LSTM networks are well-suited for analyzing and learning sequential data, such as classifying, processing, and predicting data based on time series data, which differentiates it from other conventional networks. Thus, LSTM can be used when the data are in a sequential format, such as time, sentence, etc., and commonly applied in the area of time-series analysis, natural language processing, speech recognition, etc.

An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers

In addition to these most common deep learning methods discussed above, several other deep learning approaches [ 96 ] exist in the area for various purposes. For instance, the self-organizing map (SOM) [ 58 ] uses unsupervised learning to represent the high-dimensional data by a 2D grid map, thus achieving dimensionality reduction. The autoencoder (AE) [ 15 ] is another learning technique that is widely used for dimensionality reduction as well and feature extraction in unsupervised learning tasks. Restricted Boltzmann machines (RBM) [ 46 ] can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. A deep belief network (DBN) is typically composed of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, and a backpropagation neural network (BPNN) [ 123 ]. A generative adversarial network (GAN) [ 39 ] is a form of the network for deep learning that can generate data with characteristics close to the actual data input. Transfer learning is currently very common because it can train deep neural networks with comparatively low data, which is typically the re-use of a new problem with a pre-trained model [ 124 ]. A brief discussion of these artificial neural networks (ANN) and deep learning (DL) models are summarized in our earlier paper Sarker et al. [ 96 ].

Overall, based on the learning techniques discussed above, we can conclude that various types of machine learning techniques, such as classification analysis, regression, data clustering, feature selection and extraction, and dimensionality reduction, association rule learning, reinforcement learning, or deep learning techniques, can play a significant role for various purposes according to their capabilities. In the following section, we discuss several application areas based on machine learning algorithms.

Applications of Machine Learning

In the current age of the Fourth Industrial Revolution (4IR), machine learning becomes popular in various application areas, because of its learning capabilities from the past and making intelligent decisions. In the following, we summarize and discuss ten popular application areas of machine learning technology.

Predictive analytics and intelligent decision-making: A major application field of machine learning is intelligent decision-making by data-driven predictive analytics [ 21 , 70 ]. The basis of predictive analytics is capturing and exploiting relationships between explanatory variables and predicted variables from previous events to predict the unknown outcome [ 41 ]. For instance, identifying suspects or criminals after a crime has been committed, or detecting credit card fraud as it happens. Another application, where machine learning algorithms can assist retailers in better understanding consumer preferences and behavior, better manage inventory, avoiding out-of-stock situations, and optimizing logistics and warehousing in e-commerce. Various machine learning algorithms such as decision trees, support vector machines, artificial neural networks, etc. [ 106 , 125 ] are commonly used in the area. Since accurate predictions provide insight into the unknown, they can improve the decisions of industries, businesses, and almost any organization, including government agencies, e-commerce, telecommunications, banking and financial services, healthcare, sales and marketing, transportation, social networking, and many others.

Cybersecurity and threat intelligence: Cybersecurity is one of the most essential areas of Industry 4.0. [ 114 ], which is typically the practice of protecting networks, systems, hardware, and data from digital attacks [ 114 ]. Machine learning has become a crucial cybersecurity technology that constantly learns by analyzing data to identify patterns, better detect malware in encrypted traffic, find insider threats, predict where bad neighborhoods are online, keep people safe while browsing, or secure data in the cloud by uncovering suspicious activity. For instance, clustering techniques can be used to identify cyber-anomalies, policy violations, etc. To detect various types of cyber-attacks or intrusions machine learning classification models by taking into account the impact of security features are useful [ 97 ]. Various deep learning-based security models can also be used on the large scale of security datasets [ 96 , 129 ]. Moreover, security policy rules generated by association rule learning techniques can play a significant role to build a rule-based security system [ 105 ]. Thus, we can say that various learning techniques discussed in Sect. Machine Learning Tasks and Algorithms , can enable cybersecurity professionals to be more proactive inefficiently preventing threats and cyber-attacks.

Internet of things (IoT) and smart cities: Internet of Things (IoT) is another essential area of Industry 4.0. [ 114 ], which turns everyday objects into smart objects by allowing them to transmit data and automate tasks without the need for human interaction. IoT is, therefore, considered to be the big frontier that can enhance almost all activities in our lives, such as smart governance, smart home, education, communication, transportation, retail, agriculture, health care, business, and many more [ 70 ]. Smart city is one of IoT’s core fields of application, using technologies to enhance city services and residents’ living experiences [ 132 , 135 ]. As machine learning utilizes experience to recognize trends and create models that help predict future behavior and events, it has become a crucial technology for IoT applications [ 103 ]. For example, to predict traffic in smart cities, parking availability prediction, estimate the total usage of energy of the citizens for a particular period, make context-aware and timely decisions for the people, etc. are some tasks that can be solved using machine learning techniques according to the current needs of the people.

Traffic prediction and transportation: Transportation systems have become a crucial component of every country’s economic development. Nonetheless, several cities around the world are experiencing an excessive rise in traffic volume, resulting in serious issues such as delays, traffic congestion, higher fuel prices, increased CO \(_2\) pollution, accidents, emergencies, and a decline in modern society’s quality of life [ 40 ]. Thus, an intelligent transportation system through predicting future traffic is important, which is an indispensable part of a smart city. Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [ 17 , 30 , 31 ]. For example, based on the travel history and trend of traveling through various routes, machine learning can assist transportation companies in predicting possible issues that may occur on specific routes and recommending their customers to take a different path. Ultimately, these learning-based data-driven models help improve traffic flow, increase the usage and efficiency of sustainable modes of transportation, and limit real-world disruption by modeling and visualizing future changes.

Healthcare and COVID-19 pandemic: Machine learning can help to solve diagnostic and prognostic problems in a variety of medical domains, such as disease prediction, medical knowledge extraction, detecting regularities in data, patient management, etc. [ 33 , 77 , 112 ]. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus, according to the World Health Organization (WHO) [ 3 ]. Recently, the learning techniques have become popular in the battle against COVID-19 [ 61 , 63 ]. For the COVID-19 pandemic, the learning techniques are used to classify patients at high risk, their mortality rate, and other anomalies [ 61 ]. It can also be used to better understand the virus’s origin, COVID-19 outbreak prediction, as well as for disease diagnosis and treatment [ 14 , 50 ]. With the help of machine learning, researchers can forecast where and when, the COVID-19 is likely to spread, and notify those regions to match the required arrangements. Deep learning also provides exciting solutions to the problems of medical image processing and is seen as a crucial technique for potential applications, particularly for COVID-19 pandemic [ 10 , 78 , 111 ]. Overall, machine and deep learning techniques can help to fight the COVID-19 virus and the pandemic as well as intelligent clinical decisions making in the domain of healthcare.

E-commerce and product recommendations: Product recommendation is one of the most well known and widely used applications of machine learning, and it is one of the most prominent features of almost any e-commerce website today. Machine learning technology can assist businesses in analyzing their consumers’ purchasing histories and making customized product suggestions for their next purchase based on their behavior and preferences. E-commerce companies, for example, can easily position product suggestions and offers by analyzing browsing trends and click-through rates of specific items. Using predictive modeling based on machine learning techniques, many online retailers, such as Amazon [ 71 ], can better manage inventory, prevent out-of-stock situations, and optimize logistics and warehousing. The future of sales and marketing is the ability to capture, evaluate, and use consumer data to provide a customized shopping experience. Furthermore, machine learning techniques enable companies to create packages and content that are tailored to the needs of their customers, allowing them to maintain existing customers while attracting new ones.

NLP and sentiment analysis: Natural language processing (NLP) involves the reading and understanding of spoken or written language through the medium of a computer [ 79 , 103 ]. Thus, NLP helps computers, for instance, to read a text, hear speech, interpret it, analyze sentiment, and decide which aspects are significant, where machine learning techniques can be used. Virtual personal assistant, chatbot, speech recognition, document description, language or machine translation, etc. are some examples of NLP-related tasks. Sentiment Analysis [ 90 ] (also referred to as opinion mining or emotion AI) is an NLP sub-field that seeks to identify and extract public mood and views within a given text through blogs, reviews, social media, forums, news, etc. For instance, businesses and brands use sentiment analysis to understand the social sentiment of their brand, product, or service through social media platforms or the web as a whole. Overall, sentiment analysis is considered as a machine learning task that analyzes texts for polarity, such as “positive”, “negative”, or “neutral” along with more intense emotions like very happy, happy, sad, very sad, angry, have interest, or not interested etc.

Image, speech and pattern recognition: Image recognition [ 36 ] is a well-known and widespread example of machine learning in the real world, which can identify an object as a digital image. For instance, to label an x-ray as cancerous or not, character recognition, or face detection in an image, tagging suggestions on social media, e.g., Facebook, are common examples of image recognition. Speech recognition [ 23 ] is also very popular that typically uses sound and linguistic models, e.g., Google Assistant, Cortana, Siri, Alexa, etc. [ 67 ], where machine learning methods are used. Pattern recognition [ 13 ] is defined as the automated recognition of patterns and regularities in data, e.g., image analysis. Several machine learning techniques such as classification, feature selection, clustering, or sequence labeling methods are used in the area.

Sustainable agriculture: Agriculture is essential to the survival of all human activities [ 109 ]. Sustainable agriculture practices help to improve agricultural productivity while also reducing negative impacts on the environment [ 5 , 25 , 109 ]. The sustainable agriculture supply chains are knowledge-intensive and based on information, skills, technologies, etc., where knowledge transfer encourages farmers to enhance their decisions to adopt sustainable agriculture practices utilizing the increasing amount of data captured by emerging technologies, e.g., the Internet of Things (IoT), mobile technologies and devices, etc. [ 5 , 53 , 54 ]. Machine learning can be applied in various phases of sustainable agriculture, such as in the pre-production phase - for the prediction of crop yield, soil properties, irrigation requirements, etc.; in the production phase—for weather prediction, disease detection, weed detection, soil nutrient management, livestock management, etc.; in processing phase—for demand estimation, production planning, etc. and in the distribution phase - the inventory management, consumer analysis, etc.

User behavior analytics and context-aware smartphone applications: Context-awareness is a system’s ability to capture knowledge about its surroundings at any moment and modify behaviors accordingly [ 28 , 93 ]. Context-aware computing uses software and hardware to automatically collect and interpret data for direct responses. The mobile app development environment has been changed greatly with the power of AI, particularly, machine learning techniques through their learning capabilities from contextual data [ 103 , 136 ]. Thus, the developers of mobile apps can rely on machine learning to create smart apps that can understand human behavior, support, and entertain users [ 107 , 137 , 140 ]. To build various personalized data-driven context-aware systems, such as smart interruption management, smart mobile recommendation, context-aware smart searching, decision-making that intelligently assist end mobile phone users in a pervasive computing environment, machine learning techniques are applicable. For example, context-aware association rules can be used to build an intelligent phone call application [ 104 ]. Clustering approaches are useful in capturing users’ diverse behavioral activities by taking into account data in time series [ 102 ]. To predict the future events in various contexts, the classification methods can be used [ 106 , 139 ]. Thus, various learning techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can help to build context-aware adaptive and smart applications according to the preferences of the mobile phone users.

In addition to these application areas, machine learning-based models can also apply to several other domains such as bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.

Challenges and Research Directions

Our study on machine learning algorithms for intelligent data analysis and applications opens several research issues in the area. Thus, in this section, we summarize and discuss the challenges faced and the potential research opportunities and future directions.

In general, the effectiveness and the efficiency of a machine learning-based solution depend on the nature and characteristics of the data, and the performance of the learning algorithms. To collect the data in the relevant domain, such as cybersecurity, IoT, healthcare and agriculture discussed in Sect. “ Applications of Machine Learning ” is not straightforward, although the current cyberspace enables the production of a huge amount of data with very high frequency. Thus, collecting useful data for the target machine learning-based applications, e.g., smart city applications, and their management is important to further analysis. Therefore, a more in-depth investigation of data collection methods is needed while working on the real-world data. Moreover, the historical data may contain many ambiguous values, missing values, outliers, and meaningless data. The machine learning algorithms, discussed in Sect “ Machine Learning Tasks and Algorithms ” highly impact on data quality, and availability for training, and consequently on the resultant model. Thus, to accurately clean and pre-process the diverse data collected from diverse sources is a challenging task. Therefore, effectively modifying or enhance existing pre-processing methods, or proposing new data preparation techniques are required to effectively use the learning algorithms in the associated application domain.

To analyze the data and extract insights, there exist many machine learning algorithms, summarized in Sect. “ Machine Learning Tasks and Algorithms ”. Thus, selecting a proper learning algorithm that is suitable for the target application is challenging. The reason is that the outcome of different learning algorithms may vary depending on the data characteristics [ 106 ]. Selecting a wrong learning algorithm would result in producing unexpected outcomes that may lead to loss of effort, as well as the model’s effectiveness and accuracy. In terms of model building, the techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can directly be used to solve many real-world issues in diverse domains, such as cybersecurity, smart cities and healthcare summarized in Sect. “ Applications of Machine Learning ”. However, the hybrid learning model, e.g., the ensemble of methods, modifying or enhancement of the existing learning techniques, or designing new learning methods, could be a potential future work in the area.

Thus, the ultimate success of a machine learning-based solution and corresponding applications mainly depends on both the data and the learning algorithms. If the data are bad to learn, such as non-representative, poor-quality, irrelevant features, or insufficient quantity for training, then the machine learning models may become useless or will produce lower accuracy. Therefore, effectively processing the data and handling the diverse learning algorithms are important, for a machine learning-based solution and eventually building intelligent applications.

In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications. According to our goal, we have briefly discussed how various types of machine learning methods can be used for making solutions to various real-world issues. A successful machine learning model depends on both the data and the performance of the learning algorithms. The sophisticated learning algorithms then need to be trained through the collected real-world data and knowledge related to the target application before the system can assist with intelligent decision-making. We also discussed several popular application areas based on machine learning techniques to highlight their applicability in various real-world issues. Finally, we have summarized and discussed the challenges faced and the potential research opportunities and future directions in the area. Therefore, the challenges that are identified create promising research opportunities in the field which must be addressed with effective solutions in various application areas. Overall, we believe that our study on machine learning-based solutions opens up a promising direction and can be used as a reference guide for potential research and applications for both academia and industry professionals as well as for decision-makers, from a technical point of view.

Canadian institute of cybersecurity, university of new brunswick, iscx dataset, http://www.unb.ca/cic/datasets/index.html/ (Accessed on 20 October 2019).

Cic-ddos2019 [online]. available: https://www.unb.ca/cic/datasets/ddos-2019.html/ (Accessed on 28 March 2020).

World health organization: WHO. http://www.who.int/ .

Google trends. In https://trends.google.com/trends/ , 2019.

Adnan N, Nordin Shahrina Md, Rahman I, Noor A. The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices. World J Sci Technol Sustain Dev. 2018.

Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the 1998 ACM SIGMOD international conference on Management of data. 1998; 94–105

Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. In: ACM SIGMOD Record. ACM. 1993;22: 207–216

Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Fast algorithms for mining association rules. In: Proceedings of the International Joint Conference on Very Large Data Bases, Santiago Chile. 1994; 1215: 487–499.

Aha DW, Kibler D, Albert M. Instance-based learning algorithms. Mach Learn. 1991;6(1):37–66.

Article Google Scholar

Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict covid-19 infection. Chaos Solit Fract. 2020;140:

Amit Y, Geman D. Shape quantization and recognition with randomized trees. Neural Comput. 1997;9(7):1545–88.

Ankerst M, Breunig MM, Kriegel H-P, Sander J. Optics: ordering points to identify the clustering structure. ACM Sigmod Record. 1999;28(2):49–60.

Anzai Y. Pattern recognition and machine learning. Elsevier; 2012.

MATH Google Scholar

Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM. Covid-19 outbreak prediction with machine learning. Algorithms. 2020;13(10):249.

Article MathSciNet Google Scholar

Baldi P. Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, 2012; 37–49 .

Balducci F, Impedovo D, Pirlo G. Machine learning applications on agricultural datasets for smart farm enhancement. Machines. 2018;6(3):38.

Boukerche A, Wang J. Machine learning-based traffic prediction models for intelligent transportation systems. Comput Netw. 2020;181

Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123–40.

Article MATH Google Scholar

Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. CRC Press; 1984.

Cao L. Data science: a comprehensive overview. ACM Comput Surv (CSUR). 2017;50(3):43.

Google Scholar

Carpenter GA, Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process. 1987;37(1):54–115.

Chiu C-C, Sainath TN, Wu Y, Prabhavalkar R, Nguyen P, Chen Z, Kannan A, Weiss RJ, Rao K, Gonina E, et al. State-of-the-art speech recognition with sequence-to-sequence models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 pages 4774–4778. IEEE .

Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017.

Cobuloglu H, Büyüktahtakın IE. A stochastic multi-criteria decision analysis for sustainable biomass crop selection. Expert Syst Appl. 2015;42(15–16):6065–74.

Das A, Ng W-K, Woon Y-K. Rapid association rule mining. In: Proceedings of the tenth international conference on Information and knowledge management, pages 474–481. ACM, 2001.

de Amorim RC. Constrained clustering with minkowski weighted k-means. In: 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI), pages 13–17. IEEE, 2012.

Dey AK. Understanding and using context. Person Ubiquit Comput. 2001;5(1):4–7.

Eagle N, Pentland AS. Reality mining: sensing complex social systems. Person Ubiquit Comput. 2006;10(4):255–68.

Essien A, Petrounias I, Sampaio P, Sampaio S. Improving urban traffic speed prediction using data source fusion and deep learning. In: 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE. 2019: 1–8. .

Essien A, Petrounias I, Sampaio P, Sampaio S. A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. In: World Wide Web, 2020: 1–24 .

Ester M, Kriegel H-P, Sander J, Xiaowei X, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. 1996;96:226–31.

Fatima M, Pasha M, et al. Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl. 2017;9(01):1.

Flach PA, Lachiche N. Confirmation-guided discovery of first-order rules with tertius. Mach Learn. 2001;42(1–2):61–95.

Freund Y, Schapire RE, et al. Experiments with a new boosting algorithm. In: Icml, Citeseer. 1996; 96: 148–156

Fujiyoshi H, Hirakawa T, Yamashita T. Deep learning-based image recognition for autonomous driving. IATSS Res. 2019;43(4):244–52.

Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inform Theory. 1975;21(1):32–40.

Article MathSciNet MATH Google Scholar

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT Press; 2016.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems. 2014: 2672–2680.

Guerrero-Ibáñez J, Zeadally S, Contreras-Castillo J. Sensor technologies for intelligent transportation systems. Sensors. 2018;18(4):1212.

Han J, Pei J, Kamber M. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.

Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: ACM Sigmod Record, ACM. 2000;29: 1–12.

Harmon SA, Sanford TH, Sheng X, Turkbey EB, Roth H, Ziyue X, Yang D, Myronenko A, Anderson V, Amalou A, et al. Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets. Nat Commun. 2020;11(1):1–7.

He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2015;37(9):1904–16.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 770–778.

Hinton GE. A practical guide to training restricted boltzmann machines. In: Neural networks: Tricks of the trade. Springer. 2012; 599-619

Holte RC. Very simple classification rules perform well on most commonly used datasets. Mach Learn. 1993;11(1):63–90.

Hotelling H. Analysis of a complex of statistical variables into principal components. J Edu Psychol. 1933;24(6):417.

Houtsma M, Swami A. Set-oriented mining for association rules in relational databases. In: Data Engineering, 1995. Proceedings of the Eleventh International Conference on, IEEE.1995:25–33.

Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, La Spada L, Mirmozafari M, Dehghani M, et al. Artificial intelligence and covid-19: deep learning approaches for diagnosis and treatment. IEEE Access. 2020;8:109581–95.

John GH, Langley P. Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc. 1995; 338–345

Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res. 1996;4:237–85.

Kamble SS, Gunasekaran A, Gawankar SA. Sustainable industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. Process Saf Environ Protect. 2018;117:408–25.

Kamble SS, Gunasekaran A, Gawankar SA. Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications. Int J Prod Econ. 2020;219:179–94.

Kaufman L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons; 2009.

Keerthi SS, Shevade SK, Bhattacharyya C, Radha Krishna MK. Improvements to platt’s smo algorithm for svm classifier design. Neural Comput. 2001;13(3):637–49.

Khadse V, Mahalle PN, Biraris SV. An empirical comparison of supervised machine learning algorithms for internet of things data. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE. 2018; 1–6

Kohonen T. The self-organizing map. Proc IEEE. 1990;78(9):1464–80.

Koroniotis N, Moustafa N, Sitnikova E, Turnbull B. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: bot-iot dataset. Fut Gen Comput Syst. 2019;100:779–96.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012: 1097–1105

Kushwaha S, Bahl S, Bagha AK, Parmar KS, Javaid M, Haleem A, Singh RP. Significant applications of machine learning for covid-19 pandemic. J Ind Integr Manag. 2020;5(4).

Lade P, Ghosh R, Srinivasan S. Manufacturing analytics and industrial internet of things. IEEE Intell Syst. 2017;32(3):74–9.

Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for covid-19 (sars-cov-2) pandemic: a review. Chaos Sol Fract. 2020:110059 .

LeCessie S, Van Houwelingen JC. Ridge estimators in logistic regression. J R Stat Soc Ser C (Appl Stat). 1992;41(1):191–201.

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.

Liu H, Motoda H. Feature extraction, construction and selection: A data mining perspective, vol. 453. Springer Science & Business Media; 1998.

López G, Quesada L, Guerrero LA. Alexa vs. siri vs. cortana vs. google assistant: a comparison of speech-based natural user interfaces. In: International Conference on Applied Human Factors and Ergonomics, Springer. 2017; 241–250.

Liu B, HsuW, Ma Y. Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining, 1998.

MacQueen J, et al. Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967;volume 1, pages 281–297. Oakland, CA, USA.

Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP. Machine learning for internet of things data analysis: a survey. Digit Commun Netw. 2018;4(3):161–75.

Marchand A, Marx P. Automated product recommendations with preference-based explanations. J Retail. 2020;96(3):328–43.

McCallum A. Information extraction: distilling structured data from unstructured text. Queue. 2005;3(9):48–57.

Mehrotra A, Hendley R, Musolesi M. Prefminer: mining user’s preferences for intelligent mobile notification management. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September, 2016; pp. 1223–1234. ACM, New York, USA. .

Mohamadou Y, Halidou A, Kapen PT. A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of covid-19. Appl Intell. 2020;50(11):3913–25.

Mohammed M, Khan MB, Bashier Mohammed BE. Machine learning: algorithms and applications. CRC Press; 2016.

Book Google Scholar

Moustafa N, Slay J. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 military communications and information systems conference (MilCIS), 2015;pages 1–6. IEEE .

Nilashi M, Ibrahim OB, Ahmadi H, Shahmoradi L. An analytical method for diseases prediction using machine learning techniques. Comput Chem Eng. 2017;106:212–23.

Yujin O, Park S, Ye JC. Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans Med Imaging. 2020;39(8):2688–700.

Otter DW, Medina JR , Kalita JK. A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst. 2020.

Park H-S, Jun C-H. A simple and fast algorithm for k-medoids clustering. Expert Syst Appl. 2009;36(2):3336–41.

Liii Pearson K. on lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci. 1901;2(11):559–72.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

MathSciNet MATH Google Scholar

Perveen S, Shahbaz M, Keshavjee K, Guergachi A. Metabolic syndrome and development of diabetes mellitus: predictive modeling based on machine learning techniques. IEEE Access. 2018;7:1365–75.

Santi P, Ram D, Rob C, Nathan E. Behavior-based adaptive call predictor. ACM Trans Auton Adapt Syst. 2011;6(3):21:1–21:28.

Polydoros AS, Nalpantidis L. Survey of model-based reinforcement learning: applications on robotics. J Intell Robot Syst. 2017;86(2):153–73.

Puterman ML. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons; 2014.

Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.

Quinlan JR. C4.5: programs for machine learning. Mach Learn. 1993.

Rasmussen C. The infinite gaussian mixture model. Adv Neural Inform Process Syst. 1999;12:554–60.

Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Syst. 2015;89:14–46.

Rokach L. A survey of clustering algorithms. In: Data mining and knowledge discovery handbook, pages 269–298. Springer, 2010.

Safdar S, Zafar S, Zafar N, Khan NF. Machine learning based decision support systems (dss) for heart disease diagnosis: a review. Artif Intell Rev. 2018;50(4):597–623.

Sarker IH. Context-aware rule learning from smartphone data: survey, challenges and future directions. J Big Data. 2019;6(1):1–25.

Sarker IH. A machine learning based robust prediction model for real-life mobile phone data. Internet Things. 2019;5:180–93.

Sarker IH. Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Comput Sci. 2021.

Sarker IH. Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Comput Sci. 2021.

Sarker IH, Abushark YB, Alsolami F, Khan A. Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry. 2020;12(5):754.

Sarker IH, Abushark YB, Khan A. Contextpca: predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry. 2020;12(4):499.

Sarker IH, Alqahtani H, Alsolami F, Khan A, Abushark YB, Siddiqui MK. Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling. J Big Data. 2020;7(1):1–23.

Sarker IH, Alan C, Jun H, Khan AI, Abushark YB, Khaled S. Behavdt: a behavioral decision tree learning to build user-centric context-aware predictive model. Mob Netw Appl. 2019; 1–11.

Sarker IH, Colman A, Kabir MA, Han J. Phone call log as a context source to modeling individual user behavior. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp): Adjunct, Germany, pages 630–634. ACM, 2016.

Sarker IH, Colman A, Kabir MA, Han J. Individualized time-series segmentation for mining mobile phone user behavior. Comput J Oxf Univ UK. 2018;61(3):349–68.

Sarker IH, Hoque MM, MdK Uddin, Tawfeeq A. Mobile data science and intelligent apps: concepts, ai-based modeling and research directions. Mob Netw Appl, pages 1–19, 2020.

Sarker IH, Kayes ASM. Abc-ruleminer: user behavioral rule-based machine learning method for context-aware intelligent services. J Netw Comput Appl. 2020; page 102762

Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big Data. 2020;7(1):1–29.

Sarker IH, Watters P, Kayes ASM. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data. 2019;6(1):1–28.

Sarker IH, Salah K. Appspred: predicting context-aware smartphone apps using random forest learning. Internet Things. 2019;8:

Scheffer T. Finding association rules that trade support optimally against confidence. Intell Data Anal. 2005;9(4):381–95.

Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput Oper Res. 2020;119:

Shengli S, Ling CX. Hybrid cost-sensitive decision tree, knowledge discovery in databases. In: PKDD 2005, Proceedings of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Lecture Notes in Computer Science, volume 3721, 2005.

Shorten C, Khoshgoftaar TM, Furht B. Deep learning applications for covid-19. J Big Data. 2021;8(1):1–54.

Gökhan S, Nevin Y. Data analysis in health and big data: a machine learning medical diagnosis model based on patients’ complaints. Commun Stat Theory Methods. 2019;1–10

Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, et al. Mastering the game of go with deep neural networks and tree search. nature. 2016;529(7587):484–9.

Ślusarczyk B. Industry 4.0: Are we ready? Polish J Manag Stud. 17, 2018.

Sneath Peter HA. The application of computers to taxonomy. J Gen Microbiol. 1957;17(1).

Sorensen T. Method of establishing groups of equal amplitude in plant sociology based on similarity of species. Biol Skr. 1948; 5.

Srinivasan V, Moghaddam S, Mukherji A. Mobileminer: mining your frequent patterns on your phone. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13-17 September, pp. 389–400. ACM, New York, USA. 2014.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015; pages 1–9.

Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the kdd cup 99 data set. In. IEEE symposium on computational intelligence for security and defense applications. IEEE. 2009;2009:1–6.

Tsagkias M. Tracy HK, Surya K, Vanessa M, de Rijke M. Challenges and research opportunities in ecommerce search and recommendations. In: ACM SIGIR Forum. volume 54. NY, USA: ACM New York; 2021. p. 1–23.

Wagstaff K, Cardie C, Rogers S, Schrödl S, et al. Constrained k-means clustering with background knowledge. Icml. 2001;1:577–84.

Wang W, Yang J, Muntz R, et al. Sting: a statistical information grid approach to spatial data mining. VLDB. 1997;97:186–95.

Wei P, Li Y, Zhang Z, Tao H, Li Z, Liu D. An optimization method for intrusion detection classification model based on deep belief network. IEEE Access. 2019;7:87593–605.

Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. J Big data. 2016;3(1):9.

Witten IH, Frank E. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann; 2005.

Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ. Weka: practical machine learning tools and techniques with java implementations. 1999.

Wu C-C, Yen-Liang C, Yi-Hung L, Xiang-Yu Y. Decision tree induction with a constrained number of leaf nodes. Appl Intell. 2016;45(3):673–85.

Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Philip SY, et al. Top 10 algorithms in data mining. Knowl Inform Syst. 2008;14(1):1–37.

Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C. Machine learning and deep learning methods for cybersecurity. IEEE Access. 2018;6:35365–81.

Xu D, Yingjie T. A comprehensive survey of clustering algorithms. Ann Data Sci. 2015;2(2):165–93.

Zaki MJ. Scalable algorithms for association mining. IEEE Trans Knowl Data Eng. 2000;12(3):372–90.

Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of things for smart cities. IEEE Internet Things J. 2014;1(1):22–32.

Zhao Q, Bhowmick SS. Association rule mining: a survey. Singapore: Nanyang Technological University; 2003.

Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform. 2017;97:120–7.

Zheng Y, Rajasegarar S, Leckie C. Parking availability prediction for sensor-enabled car parks in smart cities. In: Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on. IEEE, 2015; pages 1–6.

Zhu H, Cao H, Chen E, Xiong H, Tian J. Exploiting enriched contextual information for mobile app classification. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012; pages 1617–1621

Zhu H, Chen E, Xiong H, Kuifei Y, Cao H, Tian J. Mining mobile user preferences for personalized context-aware recommendation. ACM Trans Intell Syst Technol (TIST). 2014;5(4):58.

Zikang H, Yong Y, Guofeng Y, Xinyu Z. Sentiment analysis of agricultural product ecommerce review data based on deep learning. In: 2020 International Conference on Internet of Things and Intelligent Applications (ITIA), IEEE, 2020; pages 1–7

Zulkernain S, Madiraju P, Ahamed SI. A context aware interruption management system for mobile devices. In: Mobile Wireless Middleware, Operating Systems, and Applications. Springer. 2010; pages 221–234

Zulkernain S, Madiraju P, Ahamed S, Stamm K. A mobile intelligent interruption management system. J UCS. 2010;16(15):2060–80.

Download references

Author information

Authors and affiliations.

Swinburne University of Technology, Melbourne, VIC, 3122, Australia

Iqbal H. Sarker

Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, 4349, Chattogram, Bangladesh

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iqbal H. Sarker .

Ethics declarations

Conflict of interest.

The author declares no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

Rights and permissions

Reprints and permissions

About this article

Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2 , 160 (2021). https://doi.org/10.1007/s42979-021-00592-x

Download citation

Received : 27 January 2021

Accepted : 12 March 2021

Published : 22 March 2021

DOI : https://doi.org/10.1007/s42979-021-00592-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Machine learning
Deep learning
Artificial intelligence
Data science
Data-driven decision-making
Predictive analytics
Intelligent applications
Find a journal
Publish with us
Track your research

Machine Learning Project Proposal Scaling Up Deep Networks Melissa Queen and Piotr Teterwak

Proposal: Deep Neural Networks (DNNs) are used in a variety of applications, such as object recognition in images and acoustic processing for speech recognition [1]. There is significant motivation to use large training sets, as performance depends highly on input size. Banko and Brill [2] found "that simple, classical models can outperform newer, more complex models, just because the simple models can be tractably learnt using orders of magnitude more input data." However, training DNNs is a time intensive process, so the size of the training set is often limited by resources. In [3], Raina et al., noted that "parameter learning can take weeks using a conventional implementation on a single CPU." Dean et al., [1] have investigated ways to use distributed networks to reduce the training time, and thus allow the use of much larger training sets leading to much more effective networks. We believe that by exploited possible parallelism, we can improve the performance of a traditional DNN.

Methods: We will implement a deep neural network on both a cluster of computers and a sequential machine, and compare performance on varying sizes of training data. We will rely heavily on the work done by Dean et al., [1] in our development of a distributed algorithm. In particular, they outline methods of distributing stochastic gradient descent (SGD) and limited-memory BFGS. We expect to implement and test only one of these procedures. A traditional, sequential deep network will be run as a control. Comparisions between the two will be based on both object recognition accuracy and the time taken to train the network.

Dataset: We will use images from ImageNet ( http://www.image-net.org/ ) to train and test the neural networks. Image recognition is a common task for deep neural networks, and we expect the large data set available from ImageNet to highlight potential advantages of the distributed algorithm.

Milestone: We will have a traditional DNN working by the milestone deadline. We will also have the parallel algorithm coded and be in the process of debugging and optimizing. We expect to be very close to running data on the parallel network.

Reference: [1] Dean, Jeffrey, et al. Large Scale Distributed Deep Networks. NIPS, 2012. [2] Banko, M., & Brill, E. (2001). Scaling to very very large corpora for natural language disambiguation. Annual Meeting of the Association for Computational Linguistics (pp. 26 - 33). [3] R. Raina, A. Madhavan, and A. Y. Ng. Large-scale deep unsupervised learning using graphics processors. In ICML , 2009

Sources for background learning: [1] Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen. UFLDL Tutorial, http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial [2] Hinton, Geoffrey. Video Lecture: http://videolectures.net/jul09_hinton_deeplearn/

Office Hours

One of CS230's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions.

Instructors

Time and Location

Wednesday 9:30AM-11:20AM Zoom

Getting Started

Project starter package.

The teaching team has put together a

github repository with project code examples, including a computer vision and a natural language processing example (both in Tensorflow and Pytorch).
A series of posts to help you familiarize yourself with the project code examples, get ideas on how to structure your deep learning project code, and to setup AWS. The code examples posted are optional and are only meant to help you with your final project. The code can be reused in your projects, but the examples presented are not complex enough to meet the expectations of a quarterly project.
A sheet of resources to get started with project ideas in several topics

Project Topics

This quarter in CS230, you will learn about a wide range of deep learning applications. Part of the learning will be online, during in-class lectures and when completing assignments, but you will really experience hands-on work in your final project. We would like you to choose wisely a project that fits your interests. One that would be both motivating and technically challenging.

Most students do one of three kinds of projects:

Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it.
Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.
Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.) Some projects will also combine elements of applications and algorithms.

Many fantastic class projects come from students picking either an application area that they’re interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you’re excited about. (Just be sure to ask us for help if you’re uncertain how to best get started.) Alternatively, if you’re already working on a research or industry project that deep learning might apply to, then you may already have a great project idea.

Project Hints

A very good CS230 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS230, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent deep learning research papers. Two of the main machine learning conferences are ICML and NeurIPS . You may also want to look at class projects from previous years of CS230 ( Fall 2017 , Winter 2018 , Spring 2018 , Fall 2018 ) and other machine learning/deep learning classes ( CS229 , CS229A , CS221 , CS224N , CS231N ) is a good way to get ideas. Finally, we crowdsourced and curated a list of ideas that you can view here , and an older one here , and a (requires Stanford login).

Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com . Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.

Notes on a few specific types of projects:

Computation power. Amazon Web Services is sponsoring the CS230 projects by providing you with GPU credits to run your experiments! We will update regarding how to retrieve your GPU credits. Alternatively Google Cloud and Microsoft Azure offer free academic units which you can apply to.
Preprocessed datasets. While we don’t want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets (e.g. from Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem.
Replicating results. Replicating the results in a paper can be a good way to learn. However, we ask that instead of just replicating a paper, also try using the technique on another application, or do some analysis of how each component of the model contributes to final performance.

Project Deliverables

This section contains the detailed instructions for the different parts of your project.

Groups: The project is done in groups of 1-3 people; teams are formed by students.

Submission: We will be using Gradescope for submission of all four parts of the final project. We’ll announce when submissions are open for each part. You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.

Evaluation: We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the video and final report will combine to be the majority of the grade. Attendance and participation during your TA meetings will also be considered. Projects will be evaluated based on:

The technical quality of the work. (I.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?)
Significance. (Did the authors choose an interesting or a “real” problem to work on, or only a small “toy” problem? Is this work likely to be useful and/or have impact?)
The novelty of the work. (Is this project applying a common technique to a well-studied problem, or is the problem or method relatively unexplored?)

In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.

Deadline: April 19, Wednesday 11:59 PM

First, make sure to submit the following Google form so that we can match you to a TA mentor. In the form you will have to provide your project title, team members and relevant research area(s).

In the project proposal, you’ll pick a project idea to work on early and receive feedback from the TAs. If your proposed project will be done jointly with a different class’ project, you should obtain approval from the other instructor and approval from us. Please come to the project office hours to discuss with us if you would like to do a joint project. You should submit your proposals on Gradescope. All students should already be added to the course page on Gradescope via your SUNet IDs. If you are not, please create a private post on Ed and we will give you access to Gradescope.

In the proposal, below your project title, include the project category. The category can be one of:

Computer Vision
Natural Language Processing
Generative Modeling
Speech Recognition
Reinforcement Learning
Others (Please specify!)

Your project proposal should include the following information:

What is the problem that you will be investigating? Why is it interesting?
What are the challenges of this project?
What dataset are you using? How do you plan to collect it?
What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?
What reading will you examine to provide context and background? If relevant, what papers do you refer to?
How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?

Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition. We link one past example of a good project proposal here and a latex template .

Deadline: May 19, Friday 11:59 PM

The milestone will help you make sure you’re on track, and should describe what you’ve accomplished so far, and very briefly say what else you plan to do. You should write it as if it’s an “early draft” of what will turn into your final project. You can write it as if you’re writing the first few pages of your final project report, so that you can re-use most of the milestone text in your final report. Please write the milestone (and final report) keeping in mind that the intended audience is Profs. Ng and Katanforoosh and the TAs. Thus, for example, you should not spend two pages explaining what logistic regression is. Your milestone should include the full names of all your team members and state the full title of your project. Note: We will expect your final writeup to be on the same topic as your milestone. In order to help you the most, we expect you to submit your running code. Your code should contain a baseline model for your application. Along with your baseline model, you are welcome to submit additional parts of your code such as data pre-processing, data augmentation, accuracy matric(s), and/or other models you have tried. Please clean your code before submitting, comment on it, and cite any resources you used. Please do not submit your dataset . However, you may include a few samples of your data in the report if you wish.

Submission Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

Your video is required to be a 3-4 minute summary of your work. There is a hard limit of 4 minutes, and TAs will not watch a video beyond the 4 minute mark. Include diagrams, figures and charts to illustrate the highlights of your work. The video needs to be visually appealing, but also illustrate technical details of your project.

If possible, try to come up with creative visualizations of your project. These could include:

System diagrams
More detailed examples of data that don’t fit in the space of your report
Live demonstrations for end-to-end systems

We recommend searching for conference presentation sessions (AAAI, Neurips, ECCV, ICML, ICLR etc) and following those formats.

You can find a sample video from a previous iteration of the class here

Final Report

Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

The final report should contain a comprehensive account of your project. We expect the report to be thorough, yet concise. Broadly, we will be looking for the following:

Good motivation for the project and an explanation of the problem statement
A description of the data
Any hyperparameter and architecture choices that were explored
Presentation of results
Analysis of results
Any insights and discussions relevant to the project

After the class, we will post all the final writeups online so that you can read about each other’s work. If you do not want your write-up to be posted online, then please create a private Piazza post.

Deep Learning Research Proposal

The word deep learning is the study and analysis of deep features that are hidden in the data using some intelligent deep learning models . Recently, it turns out to be the most important research paradigm for advanced automated systems for decision-making . Deep learning is derived from machine learning technologies that learn based on hierarchical concepts . So, it is best for performing complex and long mathematical computations in deep learning .

This page describes to you the innovations of deep learning research proposals with major challenges, techniques, limitations, tools, etc.!!!

One most important thing about deep learning is the multi-layered approach . It enables the machine to construct and work the algorithms in different layers for deep analysis . Further, it also works on the principle of artificial neural networks which functions in the same human brain. Since it got inspiration from the human brain to make machines automatically understand the situation and make smart decisions accordingly. Here, we have given you some of the important real-time applications of deep learning.

Deep Learning Project Ideas

Natural Language Processing
Pattern detection in Human Face
Image Recognition and Object Detection
Driverless UAV Control Systems
Prediction of Weather Condition Variation
Machine Translation for Autonomous Cars
Medical Disorder Diagnosis and Treatment
Traffic and Speed Control in Motorized Systems
Voice Assistance for Dense Areas Navigation
Altitude Control System for UAV and Satellites

Now, we can see the workflow of deep learning models . Here, we have given you the steps involved in the deep learning model. This assists you to know the general procedure of deep learning model execution . Similarly, we precisely guide you in every step of your proposed deep learning model . Further, the steps may vary based on the requirement of the handpicked deep learning project idea. Anyway, the deep learning model is intended to grab deep features of data by processing through neural networks . Then, the machine will learn and understand the sudden scenarios for controlling systems.

Top 10 Interesting Deep Learning Research Proposal

Process Flow of Deep Learning

Step 1 – Load the dataset as input
Step 2 – Extraction of features
Step 3 – Process add-on layers for more abstract features
Step 4 – Perform feature mapping
Step 5 –Display the output

Although deep learning is more efficient to automatically learn features than conventional methods, it has some technical constraints. Here, we have specified only a few constraints to make you aware of current research. Beyond these primary constraints, we also handpicked more number of other constraints. To know other exciting research limitations in deep learning , approach us. We will make you understand more from top research areas.

Deep Learning Limitations

Test Data Variation – When the test data is different from training data, then the employed deep learning technique may get failure. Further, it also does not efficiently work in a controlled environment.
Huge Dataset – Deep learning models efficiently work on large-scale datasets than limited data

Our research team is highly proficient to handle different deep learning technologies . To present you with up-to-date information, we constantly upgrade our research knowledge in all advanced developments. So, we are good not only at handpicking research challenges but also more skilled to develop novel solutions. For your information, here we have given you some most common data handling issues with appropriate solutions.

What are the data handling techniques?

Variables signifies the linear combo of factors with errors
Depends on the presence of different unobserved variables (i.e., assumption)
Identify the correlations between existing observed variables
If the data in a column has fixed values, then it has “0” variance.
Further, these kinds of variables are not considered in target variables
If there is the issue of outliers, variables, and missing values, then effective feature selection will help you to get rid out of it.
So, we can employ the random forest method
Remove the unwanted features from the model
Repeat the same process until attaining maximum error rate
At last, define the minimum features
Remove one at a time and check the error rate
If there are dependent values among data columns, then may have redundant information due to similarities.
So, we can filter the largely correlated columns based on coefficients of correlation
Add one at a time for high performance
Enhance the entire model efficiency
Addresses the possibility where data points are associated with high-dimensional space
Select low-dimensional embedding to generate related distribution
Identify the missing value columns and remove them by threshold
Present variable set is converted to a new variable set
Also, referred to as a linear combo of new variables
Determine the location of each point by pair-wise spaces among all points which are represented in a matrix
Further, use standard multi-dimensional scaling (MDS) for determining low-dimensional points locations

In addition, we have also given you the broadly utilized deep learning models in current research . Here, we have classified the models into two major classifications such as discriminant models and generative models . Further, we have also specified the deep learning process with suitable techniques. If there is a complex situation, then we design new algorithms based on the project’s needs . On the whole, we find apt solutions for any sort of problem through our smart approach to problems.

Deep Learning Models

CNN and NLP (Hybrid)
Domain-specific
Image conversion
Meta-Learning

Furthermore, our developers are like to share the globally suggested deep learning software and tools . In truth, we have thorough practice on all these developing technologies. So, we are ready to fine-tuned guidance on deep learning libraries, modules, packages, toolboxes , etc. to ease your development process. By the by, we will also suggest you best-fitting software/tool for your project . We ensure you that our suggested software/tool will make your implementation process of deep learning projects techniques more simple and reliable .

Deep Learning Software and Tools

Caffe & Caffe2
Deep Learning 4j
Microsoft Cognitive Toolkit

So far, we have discussed important research updates of deep learning . Now, we can see the importance of handpicking a good research topic for an impressive deep learning research proposal. In the research topic, we have to outline your research by mentioning the research problem and efficient solutions . Also, it is necessary to check the future scope of research for that particular topic.

The topic without future research direction is not meant to do research!!!

For more clarity, here we have given you a few significant tips to select a good deep learning research topic.

How to write a research paper on deep learning?

Check whether your selected research problem is inspiring to overcome but not take more complex to solve
Check whether your selected problem not only inspires you but also create interest among readers and followers
Check whether your proposed research create a contribution to social developments
Check whether your selected research problem is unique

From the above list, you can get an idea about what exactly a good research topic is. Now, we can see how a good research topic is identified.

To recognize the best research topic, first undergo in-depth research on recent deep learning studied by referring latest reputed journal papers.
Then, perform a review process over the collected papers to detect what are the current research limitations, which aspect not addressed yet, which is a problem is not solved effectively, which solution is needed to improve, what the techniques are followed in recent research, etc.
This literature review process needs more time and effort to grasp knowledge on research demands among scholars.
If you are new to this field, then it is suggested to take the advice of field experts who recommend good and resourceful research papers.
Majorly, the drawbacks of the existing research are proposed as a problem to provide suitable research solutions.
Usually, it is good to work on resource-filled research areas than areas that have limited reference.
When you find the desired research idea, then immediately check the originality of the idea. Make sure that no one is already proved your research idea.
Since, it is better to find it in the initial stage itself to choose some other one.
For that, the search keyword is more important because someone may already conduct the same research in a different name. So, concentrate on choosing keywords for the literature study.

How to describe your research topic?

One common error faced by beginners in research topic selection is a misunderstanding. Some researchers think topic selection means is just the title of your project. But it is not like that, you have to give detailed information about your research work on a short and crisp topic . In other words, the research topic is needed to act as an outline for your research work.

For instance: “deep learning for disease detection” is not the topic with clear information. In this, you can mention the details like type of deep learning technique, type of image and its process, type of human parts, symptoms , etc.

The modified research topic for “deep learning for disease detection” is “COVID-19 detection using automated deep learning algorithm”

For your awareness, here we have given you some key points that need to focus on while framing research topics. To clearly define your research topic, we recommend writing some text explaining:

Research title
Previous research constraints
Importance of the problem that overcomes in proposed research
Reason of challenges in the research problem
Outline of problem-solving possibility

To the end, now we can see different research perspectives of deep learning among the research community. In the following, we have presented you with the most demanded research topics in deep learning such as image denoising, moving object detection, and event recognition . In addition to this list, we also have a repository of recent deep learning research proposal topics, machine learning thesis topics . So, communicate with us to know the advanced research ideas of deep learning.

Research Topics in Deep Learning

Continuous Network Monitoring and Pipeline Representation in Temporal Segment Networks
Dynamic Image Networks and Semantic Image Networks
Advance Non-uniform denoising verification based on FFDNet and DnCNN
Efficient image denoising based on ResNets and CNNs
Accurate object recognition in deep architecture using ResNeXts, Inception Nets and Squeeze and Excitation Networks
Improved object detection using Faster R-CNN, YOLO, Fast R-CNN, and Mask-RCNN

Novel Deep Learning Research Proposal Implementation

Overall, we are ready to support you in all significant and new research areas of deep learning . We guarantee you that we provide you novel deep learning research proposal in your interested area with writing support. Further, we also give you code development , paper writing, paper publication, and thesis writing services . So, create a bond with us to create a strong foundation for your research career in the deep learning field.

Services we offer.

Mathematical proof

Pseudo code

Conference Paper

Research Proposal

System Design

Literature Survey

Data Collection

Thesis Writing

Data Analysis

Rough Draft

Paper Collection

Code and Programs

Paper Writing

Course Work

Machine Learning Project Proposal Template

Great for beginners
Ready-to-use, fully customizable Subcategory
Get started in seconds

Machine learning projects can be complex and time-consuming, requiring careful planning and organization. But fear not! ClickUp's Machine Learning Project Proposal Template is here to simplify the process and set you up for success.

With this template, you can:

Clearly define project goals, objectives, and deliverables
Outline the scope and timeline of your machine learning project
Identify the necessary resources, team members, and stakeholders
Break down tasks and allocate responsibilities for efficient collaboration
Track progress, monitor milestones, and stay on top of deadlines

Whether you're a seasoned data scientist or just starting your machine learning journey, ClickUp's template will guide you through the proposal process, ensuring your project gets off to a flying start. Get started today and bring your machine learning ideas to life!

Benefits of Machine Learning Project Proposal Template

When it comes to machine learning projects, having a solid project proposal is essential. With the Machine Learning Project Proposal Template, you can:

Clearly outline project objectives, scope, and deliverables
Identify key stakeholders and their roles in the project
Define project timelines and milestones for efficient project management
Outline the data collection and preprocessing methods to ensure accurate results
Specify the machine learning algorithms and models to be used
Detail the evaluation metrics to measure project success
Provide a comprehensive budget and resource allocation plan
Streamline the approval process by presenting a professional and well-structured proposal.

Main Elements of Machine Learning Project Proposal Template

ClickUp's Machine Learning Project Proposal template is designed to help you effectively plan and manage your machine learning projects. Here are the main elements of this Whiteboard template:

Custom Statuses: Use the Open and Complete statuses to track the progress of your machine learning project proposal, ensuring that all tasks are completed on time.
Custom Fields: Utilize custom fields to capture important information about your project, such as project objectives, data sources, algorithms used, and key stakeholders.
Project Proposal View: This view allows you to outline your project proposal, including goals, deliverables, timelines, and resource allocation. It provides a comprehensive overview of your machine learning project.
Getting Started Guide View: This view provides a step-by-step guide on how to get started with your machine learning project, including setting up the necessary tools, data collection, and model development.

With ClickUp's Machine Learning Project Proposal template, you can streamline your project planning process and ensure successful execution of your machine learning projects.

How to Use Project Proposal for Machine Learning

If you're embarking on a machine learning project and need to create a project proposal, follow these steps to effectively use the Machine Learning Project Proposal Template in ClickUp:

1. Define the problem and objective

Clearly state the problem you're trying to solve with your machine learning project. Identify the objective you want to achieve, whether it's improving accuracy, optimizing processes, or making predictions.

Use a Doc in ClickUp to outline the problem statement and define the project objective.

2. Gather and analyze data

Collect relevant data that will be used for your machine learning project. This can include structured data, unstructured data, or even data from external sources. Clean and preprocess the data to ensure its quality and suitability for your project.

Use the Table view in ClickUp to organize and analyze your data, and create custom fields to track data quality and preprocessing steps.

3. Select the appropriate machine learning algorithm

Based on the problem and objective, choose the most suitable machine learning algorithm for your project. Consider factors such as the type of data, the desired outcome, and the available resources.

Create tasks in ClickUp to research and evaluate different machine learning algorithms, and assign team members to explore and choose the most appropriate one.

4. Design the model architecture

Define the architecture of your machine learning model, including the layers, nodes, and connections. Decide on the input and output layers, as well as any hidden layers, activation functions, and optimization algorithms.

Use a Whiteboard in ClickUp to visually design and iterate on your model architecture, and collaborate with your team to gather feedback.

5. Train and evaluate the model

Train your machine learning model using the collected and preprocessed data. Split the data into training and testing sets, and use appropriate evaluation metrics to assess the model's performance.

Use Automations in ClickUp to set up recurring tasks for training and evaluating the model, and create custom fields to track performance metrics and model versions.

6. Create a project timeline and budget

Develop a timeline that outlines the various stages of your machine learning project, including data collection, model development, training, and evaluation. Estimate the resources and budget required for each stage.

Use a Gantt chart in ClickUp to visualize and manage your project timeline, and create custom fields to track resource allocation and budgeting.

By following these steps and leveraging the Machine Learning Project Proposal Template in ClickUp, you'll be well-equipped to plan and execute your machine learning project successfully.

Get Started with ClickUp's Machine Learning Project Proposal Template

Data scientists and machine learning enthusiasts can use this Machine Learning Project Proposal Template to streamline the process of pitching and executing machine learning projects.

First, hit “Get Free Solution” to sign up for ClickUp and add the template to your Workspace. Make sure you designate which Space or location in your Workspace you’d like this template applied.

Next, invite relevant members or guests to your Workspace to start collaborating.

Now you can take advantage of the full potential of this template to propose and execute machine learning projects:

Use the Project Proposal View to outline your project details, including objectives, deliverables, and timelines
The Getting Started Guide View will provide step-by-step instructions and resources to get your project up and running smoothly
Assign tasks to team members and designate an open status to keep track of ongoing tasks
Update tasks as they are completed to mark them as complete
Collaborate with team members to brainstorm ideas, share resources, and discuss project progress
Set up notifications to stay updated on task updates and project milestones
Track and analyze project progress to ensure successful execution

Related Templates

Logo Design Project Proposal Template
Library Management System Project Proposal Template
Vegetable Production Project Proposal Template
Vehicle Tracking System Project Proposal Template
Consulting Project Proposal Template

Template details

Free forever with 100mb storage.

Free training & 24-hours support

Serious about security & privacy

Highest levels of uptime the last 12 months

Product Roadmap
Affiliate & Referrals
On-Demand Demo
Integrations
Consultants
Gantt Chart
Native Time Tracking
Automations
Kanban Board
vs Airtable
vs Basecamp
vs MS Project
vs Smartsheet
Software Team Hub
PM Software Guide

reason.town

How to Write a Machine Learning Project Report

Introduction, methodology, future work, acknowledgements.

If you’re working on a machine learning project, then you’ll need to write a project report at some point. This guide will show you how to write a great report that will help you communicate your findings to others.

Checkout this video:

A machine learning project report is a document that describes the process and results of your machine learning project. It should include a description of your data, your models, your results, and your conclusions. The report should be clear and concise, and it should be written in a professional tone.

Here are some tips for writing a machine learning project report:

– Start with an introduction that describes what you did and why you did it. – Be sure to include a description of your data, your models, and your results. – Conclude with a discussion of your findings and their implications. – Be sure to proofread your report carefully before submitting it.

Every machine learning project seeks to answer a question. In this post, we will take a look at what a machine learning project report should contain in order to answer the question effectively. We will also provide a template for creating a machine learning project report.

A machine learning project report should contain the following sections:

-Title -Outline -Introduction -Related Work -Methods -Experimental Setup -Results and Discussion -Conclusion and Future Work -Acknowledgements

When it comes to writing a machine learning project report, data is king. After all, a machine learning project is all about the data. Without data, there is no machine learning. Consequently, the first step in writing a machine learning project report is to gather all of the data that will be used in the project. This data can come from a variety of sources, such as online databases, surveys, and experiments. Once all of the data has been gathered, it should be organized into a format that can be easily read and understood by the reader.

The next step in writing a machine learning project report is to choose a model. This model will be used to make predictions based on the data that was gathered in the first step. There are many different types of models that can be used for machine learning, so it is important to choose one that is well suited for the task at hand. After the model has been chosen, it should be trained on the data that was gathered in the first step. This training process will teach the model how to make predictions based on the data.

Finally, once the model has been trained, it can be used to make predictions on new data. This new data can be used to evaluate how accurate the predictions made by the model are. Additionally, this new data can also be used to improve the model by increasing its accuracy.

Your methodology section should concisely describe the steps you took in your project and why you took them. It should answer the following questions: -What data did you use? -How did you preprocess it (if at all)? -What models did you try? -What hyperparameter settings did you use? -How did you evaluate your model(s)? This section should be no more than a few paragraphs long. For more complex projects, consider adding a subsection for each major step in your project.

If you’ve completed a machine learning project, congratulations! You’ve accomplished a lot. But your work isn’t quite done yet. In order to communicate your findings to others (and to yourself), you need to write a project report.

A project report is different from a research paper. In a research paper, you focus on presenting the results of your research. In a project report, you focus on describing what you did and what you learned from doing it. Therefore, a project report should:

– Be clear and concise – Be organized around the steps you took in your project – Describe what you did at each step – Describe what you learned at each step – Include code snippets and figures – Include any important resources that you used

A well-written project report will not only help you communicate your findings, but also help you reflect on your own learning process. It can be helpful to think of a project report as a journal article about your own work. And like any journal article, it should have an introduction, body, and conclusion.

The discussion section is where you talk about what you did in your project, how you did it, and what the results mean. This is also where you interpret your results and draw conclusions. The discussion section should be well-organized and clearly presented. It should flow smoothly from one point to the next, and each point should be supported with evidence from your data. Be sure to describe any limitations of your study and discuss the implications of your findings.

After all of your hard work, it’s time to conclude your machine learning project report. In this section, you will want to briefly summarize your findings and discuss the implications of your work. You should also include a discussion of any limitations in your study and suggest areas for future research. Finally, be sure to thank your reader for their time and interest in your work.

There is always room for improvement, and no machine learning project is ever truly finished. In your report, you should discuss any possible areas of future work that could be done to improve the performance of your model. For example, you might want to try a different machine learning algorithm, or fine-tune the parameters of the algorithm that you used. You might also want to collect more data, or pre-process the data in a different way.

Whatever direction you choose to take your project in the future, make sure to document your plans in your report so that others can pick up where you left off!

I would like to express my gratitude to my supervisor, Dr XXX, for her continuous support and encouragement during my research. I would also like to thank the other members of my supervisory panel, Professors XXX and YYY, for their valuable comments and suggestions. I am grateful to my family and friends who have supported me throughout my studies. Finally, I would like to thank the funding agency, XXX, for their financial support.

This guide will explain how to write a machine learning project report.

First, you need to understand what a machine learning project report is. A machine learning project report is a document that explains what machine learning is, how it works, and why it is important.

Next, you need to understand the structure of a machine learning project report. A machine learning project report should have the following sections:

– Background – Objectives – Methods – Results – Discussion – Conclusion – References

Finally, you need to understand how to write each section of a machine learning project report. Background: The background section should explain what machine learning is and why it is important. Objectives: The objectives section should explain the goals of the project. Methods: The methods section should describe the approach taken in the project. Results: The results section should present the results of the project. Discussion: The discussion section should discuss the implications of the results. Conclusion: The conclusion section should summarize the findings of the project. References: The references section should list all of the sources used in the project.

Programming for Machine Learning

ContentsIntroductionWhat is Machine Learning?Why is programming important for Machine Learning?What programming languages are most popular for Machine Learning?What are the most important libraries for Machine Learning?What are the most important concepts in Machine Learning?How can I get started with Machine Learning?What are some common issues that arise when programming for Machine Learning?How can I improve…

Check out the Great Learning Machine Learning Quiz Answers!

ContentsIntroductionWhat is Machine Learning?Types of Machine LearningBenefits of Machine LearningApplications of Machine LearningMachine Learning AlgorithmsSupervised LearningUnsupervised LearningReinforcement LearningDeep Learning Check out the Great Learning Machine Learning Quiz Answers! You will find the answers to the most popular machine learning quizzes, plus tips and resources for further learning. Checkout this video: Introduction In this post, we’ll…

How to Host Your Machine Learning Model

ContentsIntroductionWhy host your machine learning model?What are the benefits of hosting your machine learning model?How to host your machine learning modelWhat are the different ways to host your machine learning model?Which is the best way to host your machine learning model?How to deploy your machine learning model?What are the different ways to deploy your machine…

A Harsh Guide to Machine Learning

ContentsIntroductionWhat is Machine Learning?The Three Types of Machine LearningThe Five Components of Machine LearningThe Seven Steps of Machine LearningThe Benefits of Machine LearningThe Risks of Machine LearningThe Future of Machine LearningConclusionFurther Reading If you’re considering a career in machine learning, you’ll need to be prepared for some tough realities. In this blog post, we’ll give…

The Essentials of Machine Learning in Finance and Accounting

ContentsIntroduction to Machine Learning in Finance and AccountingThe Benefits of Machine Learning in Finance and AccountingThe Applications of Machine Learning in Finance and AccountingThe Challenges of Machine Learning in Finance and AccountingThe Future of Machine Learning in Finance and AccountingThe Impact of Machine Learning in Finance and AccountingThe Pros and Cons of Machine Learning in…

How to Write a Machine Learning Research Proposal

ContentsIntroductionWhat is a machine learning research proposal?The structure of a machine learning research proposalTips for writing a machine learning research proposalHow to get started with writing a machine learning research proposalThe importance of a machine learning research proposalWhy you should take the time to write a machine learning research proposalHow to make your machine learning…

machine learning Recently Published Documents

Total documents.

Latest Documents
Most Cited Documents
Contributed Authors
Related Sources
Related Keywords

An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile

A comparison of machine learning- and regression-based models for predicting ductility ratio of rc beam-column joints, alexa, is this a historical record.

Digital transformation in government has brought an increase in the scale, variety, and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are struggling to deal with this shift. This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. The project AI for Selection evaluated a range of commercial solutions varying from off-the-shelf products to cloud-hosted machine learning platforms, as well as a benchmarking tool developed in-house. Suitability of tools depended on several factors, including requirements and skills of transferring bodies as well as the tools’ usability and configurability. This article also explores questions around trust and explainability of decisions made when using AI for sensitive tasks such as selection.

Automated Text Classification of Maintenance Data of Higher Education Buildings Using Text Mining and Machine Learning Techniques

Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: a case study in queensland, australia, modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models, big five personality prediction based in indonesian tweets using machine learning methods.

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>

Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation

Temperature prediction of flat steel box girders of long-span bridges utilizing in situ environmental parameters and machine learning, computer-assisted cohort identification in practice.

The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.

Export Citation Format

Share document.

IMAGES

Machine Learning Project Proposal Template
machine learning thesis proposal
FREE 13+ Sample Research Proposals in MS Word
Machine Learning Research Proposal
Machine Learning Project Proposal Template
FREE 10+ Scientific Research Proposal Samples in MS Word

VIDEO

Machine Teaching Demo
Why you should read Research Papers in ML & DL? #machinelearning #deeplearning
Machine Learning Research Explained to a 5 Year Old #AI #musicgeneration
Azure Machine Learning
Active Learning for Support Vector Machines (SVMs)
Understanding Data, Project Graduate Admission Prediction Using Machine Learning

COMMENTS

Machine Learning Research Proposal Template by PandaDoc
Utilize a machine learning proposal template when presenting a detailed plan for implementing machine learning solutions within a specific business or organizational context. The proposal outlines what the project will entail and covers how the company will complete it. This machine learning research proposal contains all the information ...
PDF 6.891 Machine Learning: Project Proposal
4. The project can be a theoretical or more applied survey of a branch of machine learning that we didn't go through in detail. For example, you may write about the use of machine learning in understanding neural systems or sample complexity of machine learning algorithms. The project can be related to your research area (if you have one).
Proposal on Implementing Machine Learning with Highway Datasets
This. provides an opportunity for SHA to implement machine. learning (ML) for large datasets in materials an d testing. including pavement data, construction history, slope. stability, and ...
PDF Thesis Proposal
Thesis Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design Mu Li October 2016 ... large scale machine learning has led to a surge of research interest in both academia and industry. 2. October 13, 2016 ... new machine learning models, and new optimization methods with theoretical guarantees to ...
PDF A Machine Learning Proposal for Predicting the Success Rate of IT
A Machine Learning Proposal for Predicting the Success Rate of IT-Projects Based on Project Metrics Before Initiation Author: Nathalie Esmée Janssen University of Twente ... matter of fact, research has shown that approximately 1 out of 3 IT-projects fail. Over the years, a number of researchers started to examine predictive techniques ...
PHD Research Proposal Topics in Machine Learning 2022| S-Logix
Research Proposal on Deep Learning for Compressed Sensing in Compressed Speech. Research Proposal on Transfer learning for feature engineering. Research Proposal in Sentiment analysis on multimodal data. Research Proposal on Online Extreme Learning Machines. Research Proposal on Deep Learning for Face Anti-spoofing.
PDF Phd Proposal in Artificial Intelligence and Machine Learning
RESEARCH TOPIC. Generative Adversarial Networks (GANs) are a class of unsupervised machine learning techniques to estimate a distribution from high-dimensional data and to sample elements that mimic the observations (Goodfellow et al., 2014). They use a zero-sum dynamic game be- tween two neural networks: a generator, which generates new ...
PDF Work Sample of Research Proposal
Machine learning offers a wide range of tools, techniques, and frameworks to address these challenges (Nithya, 2016). For this purpose, artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data (Cao et al., 2018). ... Research Proposal ...
PDF Research proposal
objective of this proposal is to leverage the control and probabilistic reasoning literature to improve reinforcement learning agents. In recent years, machine learning has shown tremendous success in a wide variety of tasks such as computer vision (Krizhevsky et al., 2012), healthcare (Miotto et al., 2017; Faust et al., 2018),
Machine Learning: Algorithms, Real-World Applications and Research
Supervised: Supervised learning is typically the task of machine learning to learn a function that maps an input to an output based on sample input-output pairs [].It uses labeled training data and a collection of training examples to infer a function. Supervised learning is carried out when certain goals are identified to be accomplished from a certain set of inputs [], i.e., a task-driven ...
PDF A Proposal for Performance-based Assessment of the Learning of Machine
tion, the results may support the progress of learning ML by providing feedback to students and teachers. Keywords: assessment, education, rubric, machine learning, K-12. 1. Introduction. Machine Learning (ML) has become part of our everyday life deeply impacting our . society. Different from Artificial Intelligence, focusing on theory and ...
PDF PhD Proposal in Artificial Intelligence and Machine Learning
ANITI core tracks have direct application for this PhD proposal, co-funded by CS: 1. ... Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 1481-1490, Lille, France, 07-09 Jul 2015. PMLR. [3]Julien Mairal. Large-Scale Machine Learning and Applications.
Machine Learning Project Proposal
Proposal: Deep Neural Networks (DNNs) are used in a variety of applications, such as object recognition in images and acoustic processing for speech recognition [1]. There is significant motivation to use large training sets, as performance depends highly on input size. Banko and Brill [2] found "that simple, classical models can outperform ...
Project
Project. One of CS230's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions. Past Projects.
PDF Research Methods in Machine Learning
Choosing and Solving a Research Problem. Research Life Cycle. Exercise 1: What is the position of your project in the life cycle? Corresponding Skills. Exercise 2: Skills inventory. Write a Successful (NeurIPS) Paper. Process.
Novel Deep Learning Research Proposal [High Quality Proposal]
Deep Learning Research Proposal. The word deep learning is the study and analysis of deep features that are hidden in the data using some intelligent deep learning models. Recently, it turns out to be the most important research paradigm for advanced automated systems for decision-making. Deep learning is derived from machine learning ...
Machine Learning Project Proposal Template
ClickUp's Machine Learning Project Proposal Template is here to simplify the process and set you up for success. With this template, you can: Clearly define project goals, objectives, and deliverables. Outline the scope and timeline of your machine learning project. Identify the necessary resources, team members, and stakeholders.
PDF PhD Proposal AI Machine Learning Bioinformatics
The approach will be decomposed into four main tasks: (1) Obtain and curate data based on datasets provided at the beginning of the work, coming from DREAM Challenges (a popular reverse engineering challenge); (2) Learn the model (and its dynamics) based on time series data input. It will be necessary to assess the quality of its components ...
PDF Master Thesis Using Machine Learning Methods for Evaluating the ...
These questions are answered using state-of-the-art machine learning algorithms and translation evaluation metrics in the context of a knowledge discovery process. The eval-uations are done on a sentence level and recombined on a document level by binarily clas-sifying sentences as automated translation and professional translation. The research is
PDF R e ddi t C orpus, C om m on C ra wl , a nd Wi ki pe di a , whi c h ha
neighbourhoods of townships next to gated wealthyneighbourhoods. This proposal seeks to work on the first publicly available computer vision dataset tostudy the evolution of spatial apartheid in SouthAfrica. The dataset will consist of satellite images of SouthAfrica from 2006-2017, some of which will be
How to Write a Machine Learning Project Report
Here are some tips for writing a machine learning project report: - Start with an introduction that describes what you did and why you did it. - Be sure to include a description of your data, your models, and your results. - Conclude with a discussion of your findings and their implications.
Research proposals and thesis in Machine Learning / Data Mining?
Nanjing University of Aeronautics & Astronautics. @Ali khosravi. My target is gps trajectory data mining. Cite. Dr Santhosh Kumar. Guru Nanak Institute of Technology. Refer this article: https ...
machine learning Latest Research Papers
Find the latest published documents for machine learning, Related hot topics, top authors, the most cited documents, and related journals ... This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection ...