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Artificial intelligence and machine learning research: towards digital transformation at a global scale
- Published: 17 April 2021
- Volume 13 , pages 3319–3321, ( 2022 )
Cite this article
- Akila Sarirete 1 ,
- Zain Balfagih 1 ,
- Tayeb Brahimi 1 ,
- Miltiadis D. Lytras 1 , 2 &
- Anna Visvizi 3 , 4
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Artificial intelligence (AI) is reshaping how we live, learn, and work. Until recently, AI used to be a fanciful concept, more closely associated with science fiction rather than with anything else. However, driven by unprecedented advances in sophisticated information and communication technology (ICT), AI today is synonymous technological progress already attained and the one yet to come in all spheres of our lives (Chui et al. 2018 ; Lytras et al. 2018 , 2019 ).
Considering that Machine Learning (ML) and AI are apt to reach unforeseen levels of accuracy and efficiency, this special issue sought to promote research on AI and ML seen as functions of data-driven innovation and digital transformation. The combination of expanding ICT-driven capabilities and capacities identifiable across our socio-economic systems along with growing consumer expectations vis-a-vis technology and its value-added for our societies, requires multidisciplinary research and research agenda on AI and ML (Lytras et al. 2021 ; Visvizi et al. 2020 ; Chui et al. 2020 ). Such a research agenda should oscilate around the following five defining issues (Fig. 1 ):
Source: The Authors
An AI-Driven Digital Transformation in all aspects of human activity/
Integration of diverse data-warehouses to unified ecosystems of AI and ML value-based services
Deployment of robust AI and ML processing capabilities for enhanced decision making and generation of value our of data.
Design of innovative novel AI and ML applications for predictive and analytical capabilities
Design of sophisticated AI and ML-enabled intelligence components with critical social impact
Promotion of the Digital Transformation in all the aspects of human activity including business, healthcare, government, commerce, social intelligence etc.
Such development will also have a critical impact on government, policies, regulations and initiatives aiming to interpret the value of the AI-driven digital transformation to the sustainable economic development of our planet. Additionally the disruptive character of AI and ML technology and research will required further research on business models and management of innovation capabilities.
This special issue is based on submissions invited from the 17th Annual Learning and Technology Conference 2019 that was held at Effat University and open call jointly. Several very good submissions were received. All of them were subjected a rigorous peer review process specific to the Ambient Intelligence and Humanized Computing Journal.
A variety of innovative topics are included in the agenda of the published papers in this special issue including topics such as:
Stock market Prediction using Machine learning
Detection of Apple Diseases and Pests based on Multi-Model LSTM-based Convolutional Neural Networks
ML for Searching
Machine Learning for Learning Automata
Entity recognition & Relation Extraction
Intelligent Surveillance Systems
Activity Recognition and K-Means Clustering
Distributed Mobility Management
Review Rating Prediction with Deep Learning
Cybersecurity: Botnet detection with Deep learning
Self-Training methods
Neuro-Fuzzy Inference systems
Fuzzy Controllers
Monarch Butterfly Optimized Control with Robustness Analysis
GMM methods for speaker age and gender classification
Regression methods for Permeability Prediction of Petroleum Reservoirs
Surface EMG Signal Classification
Pattern Mining
Human Activity Recognition in Smart Environments
Teaching–Learning based Optimization Algorithm
Big Data Analytics
Diagnosis based on Event-Driven Processing and Machine Learning for Mobile Healthcare
Over a decade ago, Effat University envisioned a timely platform that brings together educators, researchers and tech enthusiasts under one roof and functions as a fount for creativity and innovation. It was a dream that such platform bridges the existing gap and becomes a leading hub for innovators across disciplines to share their knowledge and exchange novel ideas. It was in 2003 that this dream was realized and the first Learning & Technology Conference was held. Up until today, the conference has covered a variety of cutting-edge themes such as Digital Literacy, Cyber Citizenship, Edutainment, Massive Open Online Courses, and many, many others. The conference has also attracted key, prominent figures in the fields of sciences and technology such as Farouq El Baz from NASA, Queen Rania Al-Abdullah of Jordan, and many others who addressed large, eager-to-learn audiences and inspired many with unique stories.
While emerging innovations, such as Artificial Intelligence technologies, are seen today as promising instruments that could pave our way to the future, these were also the focal points around which fruitful discussions have always taken place here at the L&T. The (AI) was selected for this conference due to its great impact. The Saudi government realized this impact of AI and already started actual steps to invest in AI. It is stated in the Kingdome Vision 2030: "In technology, we will increase our investments in, and lead, the digital economy." Dr. Ahmed Al Theneyan, Deputy Minister of Technology, Industry and Digital Capabilities, stated that: "The Government has invested around USD 3 billion in building the infrastructure so that the country is AI-ready and can become a leader in AI use." Vision 2030 programs also promote innovation in technologies. Another great step that our country made is establishing NEOM city (the model smart city).
Effat University realized this ambition and started working to make it a reality by offering academic programs that support the different sectors needed in such projects. For example, the master program in Energy Engineering was launched four years ago to support the energy sector. Also, the bachelor program of Computer Science has tracks in Artificial Intelligence and Cyber Security which was launched in Fall 2020 semester. Additionally, Energy & Technology and Smart Building Research Centers were established to support innovation in the technology and energy sectors. In general, Effat University works effectively in supporting the KSA to achieve its vision in this time of national transformation by graduating skilled citizen in different fields of technology.
The guest editors would like to take this opportunity to thank all the authors for the efforts they put in the preparation of their manuscripts and for their valuable contributions. We wish to express our deepest gratitude to the referees, who provided instrumental and constructive feedback to the authors. We also extend our sincere thanks and appreciation for the organizing team under the leadership of the Chair of L&T 2019 Conference Steering Committee, Dr. Haifa Jamal Al-Lail, University President, for her support and dedication.
Our sincere thanks go to the Editor-in-Chief for his kind help and support.
Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):2869
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Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Human Behav 107:105584
Lytras MD, Visvizi A, Daniela L, Sarirete A, De Pablos PO (2018) Social networks research for sustainable smart education. Sustainability 10(9):2974
Lytras MD, Visvizi A, Sarirete A (2019) Clustering smart city services: perceptions, expectations, responses. Sustainability 11(6):1669
Lytras MD, Visvizi A, Chopdar PK, Sarirete A, Alhalabi W (2021) Information management in smart cities: turning end users’ views into multi-item scale development, validation, and policy-making recommendations. Int J Inf Manag 56:102146
Visvizi A, Jussila J, Lytras MD, Ijäs M (2020) Tweeting and mining OECD-related microcontent in the post-truth era: A cloud-based app. Comput Human Behav 107:105958
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Effat College of Engineering, Effat Energy and Technology Research Center, Effat University, P.O. Box 34689, Jeddah, Saudi Arabia
Akila Sarirete, Zain Balfagih, Tayeb Brahimi & Miltiadis D. Lytras
King Abdulaziz University, Jeddah, 21589, Saudi Arabia
Miltiadis D. Lytras
Effat College of Business, Effat University, P.O. Box 34689, Jeddah, Saudi Arabia
Anna Visvizi
Institute of International Studies (ISM), SGH Warsaw School of Economics, Aleja Niepodległości 162, 02-554, Warsaw, Poland
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Sarirete, A., Balfagih, Z., Brahimi, T. et al. Artificial intelligence and machine learning research: towards digital transformation at a global scale. J Ambient Intell Human Comput 13 , 3319–3321 (2022). https://doi.org/10.1007/s12652-021-03168-y
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Frequently Asked Questions
JMLR Papers
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Volume 23 (January 2022 - Present)
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Volume 3 (Jul 2002 - Mar 2003)
Volume 2 (Oct 2001 - Mar 2002)
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Special Topics
Bayesian Optimization
Learning from Electronic Health Data (December 2016)
Gesture Recognition (May 2012 - present)
Large Scale Learning (Jul 2009 - present)
Mining and Learning with Graphs and Relations (February 2009 - present)
Grammar Induction, Representation of Language and Language Learning (Nov 2010 - Apr 2011)
Causality (Sep 2007 - May 2010)
Model Selection (Apr 2007 - Jul 2010)
Conference on Learning Theory 2005 (February 2007 - Jul 2007)
Machine Learning for Computer Security (December 2006)
Machine Learning and Large Scale Optimization (Jul 2006 - Oct 2006)
Approaches and Applications of Inductive Programming (February 2006 - Mar 2006)
Learning Theory (Jun 2004 - Aug 2004)
Special Issues
In Memory of Alexey Chervonenkis (Sep 2015)
Independent Components Analysis (December 2003)
Learning Theory (Oct 2003)
Inductive Logic Programming (Aug 2003)
Fusion of Domain Knowledge with Data for Decision Support (Jul 2003)
Variable and Feature Selection (Mar 2003)
Machine Learning Methods for Text and Images (February 2003)
Eighteenth International Conference on Machine Learning (ICML2001) (December 2002)
Computational Learning Theory (Nov 2002)
Shallow Parsing (Mar 2002)
Kernel Methods (December 2001)
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of the basics of machine learning, it might be better understood as a collection of tools that can be applied to a speci c subset of problems. 1.2 What Will This Book Teach Me? The purpose of this book is to provide you the reader with the following: a framework with which to approach problems that machine learning learning might help solve ...
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and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models.
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A variety of innovative topics are included in the agenda of the published papers in this special issue including topics such as: Stock market Prediction using Machine learning. Detection of Apple Diseases and Pests based on Multi-Model LSTM-based Convolutional Neural Networks. ML for Searching. Machine Learning for Learning Automata
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