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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

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Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing

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DiagSet: a dataset for prostate cancer histopathological image classification

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A comparison of human and GPT-4 use of probabilistic phrases in a coordination game

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News and Comment

AI hears hidden X factor in zebra finch love songs

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Three reasons why AI doesn’t model human language

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Generative artificial intelligence in chemical engineering

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Why scientists trust AI too much — and what to do about it

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M Tech Thesis Topics In Computer Science

M.Tech thesis topics in computer science is required for academic post graduate students. M.TECH / M.E / MS / M.PHIL computer science, information technology, communication, networking department students can be benefit for this topics. All M TECH THESIS TOPICS IN COMPUTER SCIENCE based on above mention respective department subjects.

M.Tech thesis topics in computer science in classified to many divisions, guidance is provided to all kinds regarding M.Tech thesis topics in computer science, below we speak about some of its types.

Knowledge discovery, bio medical engineering, image processing, wireless communication, wireless sensor networks, medical imaging, grid computing, ubiquitous computing, web service, semantic web, mobile computing, cloud computing, networks, communication, electronics, software engineering.

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MATLAB / SCILAB / IMAGE J / OPEN CV / JAVA / C++ / VC++

Data mining:

JAVA / WEKA / RAPIDMINER / WORDNET / SETIWORDNET / RTOOL / CPAN

cloud computing :

cloudsim / cloud analyst / cloud reports / java

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• Avatar: Mobile Distributed Computing in the Cloud
• Distributed denial of service attacks in software-defined networking with cloud computing
• Challenges and opportunities of resource allocation in cloud computing: A survey
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• Implementation and performance evaluation of sentiment analysis web application in cloud computing using IBM Blue mix
• A distributed approach towards trusted cloud computing platform
• Stealthy Denial of Service Strategy in Cloud Computing
• Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment
• HMCC: A Hybrid Mobile Cloud Computing Framework Exploiting Heterogeneous Resources
• Analysis of Process Assignment in Multi-tier mobile Cloud Computing and Application to Edge Accelerated Web Browsing
• Managing Identities in Cloud Computing Environments
• A Bi-Criteria Algorithm for Low-Carbon and QoS-Aware Routing in Cloud Computing Infrastructures
• Security and Privacy in Cloud Computing: Vision, Trends, and Challenges
• A comparative study into energy efficient techniques for Cloud computing
• A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing
• EMC: Emotion-aware mobile cloud computing in 5G
• A survey on resource allocation techniques in cloud computing
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• Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing
• A universal fairness evaluation framework for resource allocation in cloud computing

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mtech-project

Here are 30 public repositories matching this topic..., vatshayan / final-year-project-cryptographic-technique-for-communication-system.

Top B.tech/M.tech Final Year Project "Design and Analysis of Cryptographic Technique for Communication System" with Project Code, Report, PPT, Synopsis, IEEE Research Paper and HD Video Explanation

• Updated Dec 22, 2022
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Vatshayan / Face-recognition-Attendance-System-Project

Final Year Btech Face recognition Attendance System Project with code and Documents. Video Implementation with explanation too. Base IEEE paper Implementation

• Updated Aug 12, 2022

Vatshayan / Final-Year-Blockchain-Voting-System

Blockchain based electronic voting system with Code, PPT, synopsis, Report, Research papers and full video explanation. Blockchain Final Year Project

• Updated Jan 20, 2023

Vatshayan / Final-Year-Machine-Learning-Stock-Price-Prediction-Project

Final Year B.tech Project on Machine Learning Stock Prediction through Deep Learning

• Updated Aug 30, 2022

Vatshayan / Malware-Detection-Using-Deep-Learning-Project

Malware-Detection-System-Using-Deep-Learning-Project. Project Includes PPT. Code, Explanation Video and Documents

Vatshayan / Final-year-Project-steganography

Steganography is the technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid detection; the secret data is then extracted at its destination.

• Updated Aug 14, 2022

Vatshayan / Network-Intrusion-Detection-Project

Network Intrusion Detection System Project using Machine Learning with code and Documents

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Spam Detection Project for college students with PPT, Report, Code, Datasets and Research papers

Vatshayan / Steganography-Website-Project

Final Year Steganography Project with Code and Project report

Vatshayan / Blockchain-and-Cryptography-Communication-System

Final Year Blockchain Project for Security of communication. [Security of Communication Increase through Use of Combination of Cryptography and Blockchain technology]

Projects-Developer / Drowsiness-Detector-College-Project

Drowsiness Detector using Python. College Project with all Documents

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Final year College Project with Project Report, PPT, Synopsis and Code

Vatshayan / Heart-disease-prediction-system-Project

Heart disease prediction system Project using Machine Learning with Code and Report

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Diseases Prediction System though Machine Learning with code and documents

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Final year Co2 Emission Prediction Using Machine Learning Project with source code and documents. Btech Final Year Project

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Beautiful Image recognition and Classification Project for final year college students.

• Updated Aug 25, 2021

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Final Year Project Heart Disease Prediction Project with all Documents.

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Projects-Developer / AI-Chatbot-Final-Year-Project

AI chatbot used to communication with End user through online on platforms such websites and application. Btech college project.

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Decentrilized Blockchain Blog System Project with code and Documents

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Research Areas

Theoretical computer science.

• Bharat Adsul
• Ajit A. Diwan
• Rohit Gurjar
• Mrinal Kumar
• Nutan Limaye
• Manoj Prabhakaran
• Abhiram Ranade
• Milind Sohoni
• Sundar Vishwanathan
• Sujoy Bhore

Artificial Intelligence and Machine Learning

• Pushpak Bhattacharya
• Soumen Chakrabarti
• Preethi Jyothi
• Shivaram Kalyanakrishnan
• Ganesh Ramakrishnan
• Sunita Sarawagi
• Virendra Singh
• Swaprava Nath

Formal Methods

• Supratik Chakrabarti
• Ashutosh Gupta

Computer Networks and Systems

• Varsha Apte
• Kameswari Chebrolu
• Ashwin Gumaste
• Purushottam Kulkarni
• Bhaskaran Raman
• G. Sivakumar
• Mythili Vutukuru

Distributed Systems and Cloud Computing

• Umesh Bellur
• Supratim Biswas
• Sridher Iyer
• Rushikesh K. Joshi
• R.K. Shyamasundar
• Sriram Srinivasan

Programming Languages and Compilers

• Uday Khedker
• Amitabha Sanyal
• Manas Thakur

Database and Information Systems

• Krithi Ramamritham
• S. Sudarshan

Software Engineering

• Om P. Damani

Data Mining

Visual computing.

• Suyash P. Awate
• Sharat Chandran
• Parag Chaudhuri
• Siddhartha Chaudhuri
• Ajit Rajwade

Real-Time and Embedded Systems

Security and cryptography.

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• Vinay Ribeiro

Formal Languages and Bio-inspired Computing

Computer architecture.

• Biswabandan Panda

Assistant Professor Aloni Cohen Receives Prestigious Award for Groundbreaking Research in Machine Learning Complexity

Assistant Professor Aloni Cohen from the University of Chicago’s Department of Computer Science and Data Science Institute has been honored with an Outstanding Paper Award at the 35th International Conference on Algorithmic Learning Theory (ALT 2024) for his research on the computational complexity of differentially private PAC learning. The conference, renowned for showcasing cutting-edge advancements in machine learning theory, recognized Cohen’s work as a significant contribution to the field.

The paper that garnered Cohen the prestigious award examines a basic question: how hard is private machine learning? The task, more formally called private PAC learning, is to learn to classify data given many examples as training data while also hiding the specifics of any individual training example.  At the heart of this work are two fundamental measures of hardness of machine learning: the number of examples and the compute time needed to learn the function.

Titled “ Exploring the Computational Boundaries of Differentially Private PAC Learning ,” Cohen’s research builds upon the connection between private PAC learning and a different, non-private model of learning called mistake-bounded online learning, or online learning for short. Prior work showed that any function of the data can be online learned, can be privately learned with not too many more examples. But it left open whether the same can be said for compute time.

Cohen and his co-authors, Rathin Desai and Mark Bun, resolve this fundamental question. The answer, they show, is no. Some functions can be easily learned online, but cannot be privately learned without breaking certain cryptographic schemes.

“Modern AI systems like Chat-GPT are trained on simply staggering amounts of data, including data about all of us,” said Cohen. “Privacy is only getting harder. Though still very far from the practice of AI, our work helps illuminate the fundamental limits of privacy in the age of machine learning.”

Cohen’s accolade underscores the University of Chicago’s commitment to fostering groundbreaking research in computer science and machine learning. His innovative contributions serve as a beacon for future endeavors, inspiring researchers to explore new frontiers in the quest for knowledge and technological advancement.

“Controlling data disclosure, which is the goal of differentially private machine learning, is just one aspect of a much larger set of problems,” Cohen said. “There’s still so much we don’t understand about how to make good use of data while respecting the subjects and creators of that data. We mostly don’t even understand what the right questions are, which is part of what makes this research area so interesting.”

Republished from the University of Chicago, Department of Computer Science website .

Asst. Prof. Aloni Cohen Receives Award For Revealing Flaws in Deidentifying Data

Dsi continues to grow with six new faculty joining 2023-24, more on this topic, spring 2024 distinguished speaker series, uchicago graduate students contribute data skills to nonprofits, new joint degree: business administration and applied data science, ms in applied data science students present research at national conference.

M.Tech Computer Science & Engineering

School of Computing Science and Engineering motivates our students to pursue the Research Based Learning in the following research domains 1. Theoretical Computer Science 2. Data Analytics 3. Semantic Web Technology 4. Computational Intelligence 5. Network and Security 6. Digital Image and Video Processing 7. Cloud Computing 8. Software Engineering 9. Computer Architecture & Embedded System 10. E-Learning

To encourage and motivate our M.Tech Students, a open house poster presentation session was done under the name Innovative Expo on Challenge the Challenges(IECC) 2014. The motto of IECC 2014 is to explore the industrial problem and providing the possible solutions to address those issues. Open House to showcase innovative poster presentation done by the students in various domains like web semantics, Computational intelligence, Computer Vision, Architecture, Software Engineering, Big Data Analytics and Cloud Computing.

Research Papers

• Shreyansh Kumar,R.Parvathi,” Back-End Forwarding Scheme in Server Load Balancing using Client Virtualization” International Journal of Computer Applications (0975 – 8887), Volume 95– No.18, June 2014.
• Teenu Rose Jacob, M. Priyaadharshini,” Enhancing Identity Based Authentication Using Partial Key Mechanism in Cloud Computing, International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 26-33, © IASTER 2013.
• Twinkle Rachel Thomas,Sathish Kumar B,”UCPNet: Enhancing the Use Case Points Method for Software Cost Estimation Using Petri Nets”, International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 63-70, © IASTER 2013.
• Anvesh Kumar.B, Nisha.V.M, “Algorithm for Sequence Alignment in Regular Expression Signature Generation” International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 71-75, © IASTER 2013.
• M. Priyaadharshini,Parvathy R, “ Enhancing Performance of Learning Objects using Mapreduce Techniques”, International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 79-84, © IASTER 2013.
• D.M.Ajay, Aparna.V,”A Survey of Tools for Automotive Software Development”, International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 85-89, © IASTER 2013.
• Allada Sivashaswat, B. Saleena ,“Applying Semantic Web Technologies to Enhance the Learning of Contemporary Dance”, International Journal of Research in Computer Applications & Information Technology ,Volume 1, Issue 2,October-December, 2013, pp. 90-95, © IASTER 2013
• K.Swetha Harshini, Tamarana Rohini, Rupin Bansal, R Jagadeesh Kannan ,”NEURAL MODEL FOR INGREDIENT LEVEL BASED MULTIMODAL CUISINE RECOGNITION”, International Journal of Pharmacy & Technology, Sep-2016 , Vol. 8 , Issue No.3 ,pp. 18734-18741
• Apurva Waghmare, Neetika Verma, Astha Gaur, R Jagadeesh Kannan ,”NEURAL NETWORK BASED FEATURE ANALYSIS OF MORTALITY RISK BY HEART FAILURE”, International Journal of Pharmacy & Technology, Sep-2016 ,Vol. 8 ,Issue No.3 ,pp. 18742-18754
• Supriya S Shinde, Rutuja P Manusmare, Akshay Lawange, R Jagadeesh Kannan,”NEURAL NETWORK BASED BIOMETRIC TECHNOLOGY FOR FINGERPRINT CLASSIFICATION”, International Journal of Pharmacy & Technology, Sep-2016 ,Vol. 8 ,Issue No.3 ,pp.18755-18762
• Jayasandeep Reddy ( 15MCS1010) has won the best RBL Paper award in NCSET’15 conference . His title of research work is “SMART SENSOR NETWORKS FOR SMART GRID FOCUSED ON CLOUD BASED BIG DATA ANALYTICS”.
• Bhuye Gurjeetkaur Sukhvinder Singh(16MCS1002) has won the best paper award in NCSET’16 Conference. Her title of the research work is “Automated Vehicle Collision Avoidance System using Wireless Network”

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Computer Science > Computer Vision and Pattern Recognition

Title: mtp: advancing remote sensing foundation model via multi-task pretraining.

Abstract: Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP.

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Thesis and Research Topics in Computer Science

Completing a masters Thesis in computer science is the most challenging task faced by research scholars studying in universities all across the world. As computer science is one of the most vast fields opted by research scholars so finding a new thesis topic in computer science becomes more difficult. With each passing day, new and innovative developments are coming out in this era of mechanization. These developments tend to make human life much easier and better. Technology is the forerunner of this new change. Today our life is incomplete without this technology. Cell phones, laptops and all that have become an integral part of our life. Computer Science is the seed to this technical development. There are a number of good topics in computer science for project, thesis, and research for M.Tech and Ph.D. students.

In the field of academics, we need to get rid of obsolete ideas and focus on new innovative topics which are fast spreading their arms among the vast global audience. Computer Science students both in bachelors and in masters are studying the same topics and subjects from the past few years. Students don’t even have knowledge about new masters research topics. For project and thesis work also they are relying on outdated topics. Projects like school management system, library management system etc. are now out of date. Students should shift their focus to latest technologies which are highly in demand these days and future depend upon these. Here is the list of latest topics in Computer Science that you can choose and work for your project work or thesis and research:

List of few latest thesis topics in computer science is below:

• Thesis topics in data mining
• Thesis topics in machine learning
• Thesis topics in digital image processing
• Latest thesis topics in Internet of things (IOT)
• Research topics in Artificial Intelligence
• Networking can be chosen as a  thesis topic in computer science
• Trending thesis topics in cloud computing
• Data aggregation as a  thesis topics  in Big Data
• Research topics  in Software Engineering

Data Warehousing

Data Warehousing is the process of analyzing data for business purposes. Data warehouse store integrated data from multiple sources at a single place which can later be retrieved for making reports. The data warehouse in simple terms is a type of database different and kept isolated from organization’s run-time database. The data in the warehouse is historical data which is helpful in understanding business goals and make decisions for future prospects. It is a relatively new concept and have high growth in future. Data Warehouse provides Online Analytical Processing(OLAP) tools for the systematic and effective study of data in a multidimensional view. Data Warehouse finds its application in the following areas:

• Financial Sector
• Banking Sector
• Retail Services
• Consumer goods
• Manufacturing

So start working on it if you have knowledge of database and data modeling.

INTERNET OF THINGS(IOT)

Internet of Things(IoT)  is a concept of interconnection of various devices, a vehicle to the internet. IOT make use of actuators and sensors for transferring data to and from the devices. This technology is developed for better efficiency and accuracy apart from minimizing human interaction with the devices. The example for this is home heating in some countries when the temperature drops done through motion sensors which automatically detect the weather conditions. Another example for this is the traffic lights which changes its colors depending upon the traffic. Following are the application areas of Internet of Things(IoT):

• Home Automation
• Agriculture
• Transportation
• Environment

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH  TOPICS IN IOT :-

• The secure and energy efficient data routing in the IOT based networks
• The secure channel establishment algorithm for the isolation of misdirection attack in the IOT
• The clock synchronization of IOT devices of energy efficient data communication in IOT
• The adaptive learning scheme to increase fault tolerance of IOT
• Mobility aware energy efficient routing protocol for Internet of Things
• To propose energy efficient multicasting routing protocol for Internet of Things
• The novel scheme to maintain quality of service in internet of Things
• Link reliable and trust aware RPL routing protocol for Internet of Things
• The energy efficient cluster based routing in Internet of Things
• Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of Things

Many people are not aware of this concept so you can choose for your project work and learn something new.

Big Data is a term to denote the large volume of data which is complex to handle. The data may be structured or unstructured. Structured data is an organized data while unstructured data is an unorganized data.  Big data  can be examined for the intuition that can give way to better decisions and schematic business moves. The definition of big data is termed in terms of three Vs. These vs are:

• Volume: Volume defines large volume of data from different sources
• Velocity: It refers to the speed with which the data is generated
• Variety: It refers to the varied amount of data both structured and unstructured.

Application areas:

BELOW IS THE LIST OF FEW LATEST AND TRENDING  RESEARCH TOPICS IN BIG DATA :-

• Privacy preserving big data publishing: a scalable k-anonymization approach using MapReduce.
• Nearest Neighbour Classification for High-Speed Big Data Streams Using Spark.
• Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems.
• Disease Prediction by Machine Learning Over Big Data From Healthcare Communities.
• A Parallel Multi-classification Algorithm for Big Data Using an Extreme Learning Machine.

Thus you can prepare your project report or thesis report on this.

Cloud Computing

Cloud Computing is a comparatively new technology. It is an internet-based service that creates a shared pool of resources for consumers. There are three service models of  cloud computing  namely:

• Software as a Service(SaaS)
• Platform as a Service(PaaS)
• Infrastructure as a Service(IaaS)

Characteristics of cloud computing are:

• On-demand self-service
• Broad network access
• Shared pool of resources
• Scalability
• Measured service

Below is the list of few latest and trending research topics in Cloud Computing :-

• To isolate the virtual side channel attack in cloud computing
• Enhancement in homomorphic encryption for key management and key sharing
• To overcome load balancing problem using weight based scheme in cloud computing
• To apply watermarking technique in cloud computing to enhance cloud data security
• To propose improvement green cloud computing to reduce fault in the network
• To apply stenography technique in cloud computing to enhance cloud data security
• To detect and isolate Zombie attack in cloud computing

The common examples of cloud computing include icloud from Apple, Google-based Services like Google Drive and many more. The field is very demanding and is growing day by day. You can focus on it if you have interest in innovation.

Semantic Web

Semantic Web is also referred to as Web 3.0 and is the next big thing in the field of communication. It is standardized by World Wide Web Consortium(W3C) to promote common data formats and exchange protocols over the web. It is machine-readable information based and is built on XML technology. It is an extension to Web 2.0. In the semantic web, the information is well defined to enable better cooperation between the computers and the people. In the semantic web, the data is interlinked for better understanding. It is different from traditional data sharing technologies.

It can be a good topic for your thesis or project.

MANET stands for mobile ad hoc network. It is an infrastructure-less network with mobile devices connected wirelessly and is self-configuring. It can change locations independently and can link to other devices through a wireless connection. Following are the various types of  MANETS :

• Vehicular ad hoc network(VANET)
• Smartphone ad-hoc network(SPANET)
• Internet-based mobile ad hoc network(iMANET)

You can use various simulation tools to study the functionality and working of MANET like OPNET,  NS2 , NETSIM, NS3 etc.

In MANET there is no need of central hub to receive and send messages. Instead, the nodes directly send packets to each other.

MANET finds its applications in the following areas:

• Environment sensors
• Vehicular ad hoc communication
• Road Safety

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN MANET :-

• Evaluate and propose scheme for the link recovery in mobile ad hoc networks
• To propose hybrid technique for path establishment using bio-inspired techniques in MANET’s
• To propose secure scheme for the isolation of black hole attack in mobile ad hoc networks
• To propose trust based mechanism for the isolation of wormhole attack in mobile ad hoc networks
• The novel approach for the congestion avoidance in mobile ad hoc networks
• To propose scheme for the detection of selective forwarding attack in mobile ad hoc networks
• To propose localization scheme which reduce faults in mobile ad hoc network
• The energy efficient scheme for multicasting routing in wireless ad hoc network
• The scheme for secure localization aided routing in wireless ad hoc networks
• The cross-layer scheme for opportunistic routing in mobile ad hoc networks

Just go for it if you have interest in the field of networking and make a project on it.

Machine Learning

It is also a relatively new concept in the field of computer science and is a technique of guiding computers to act in a certain way without programming. It makes use of certain complex algorithms to receive an input and predict an output for the same. There are three types of learning;

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Machine Learning  is closely related to statistics. If you are good at statistics then you should opt this topic.

Data Mining

Data Mining is the process of identifying and establishing a relationship between large datasets for finding a solution to a problem through analysis of data. There are various tools and techniques in Data Mining which gives enterprises and organizations the ability to predict futuristic trends.  Data Mining  finds its application in various areas of research, statistics, genetics, and marketing. Following are the main techniques used in the process of Data Mining:

• Decision Trees
• Genetic Algorithm
• Induction method
• Artificial Neural Network
• Association

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN DATA MINING :-

• Performance enhancement of DBSCAN density based clustering algorithm in data mining
• The classification scheme for sentiment analysis of twitter data
• To increase accuracy of min-max k-mean clustering in Data mining
• To evaluate and improve apriori algorithm to reduce execution time for association rule generation
• The classification scheme for credit card fraud detection in Data mining
• To propose novel technique for the crime rate prediction in Data Mining
• To evaluate and propose heart disease prediction scheme in Data Mining
• Software defect prediction analysis using machine learning algorithms
• A new data clustering approach for data mining in large databases
• The diabetes prediction technique for Data mining using classification
• Novel Algorithm for the network traffic classification in Data Mining

Advantages of Data Mining

• Data Mining helps marketing and retail enterprises to study customer behavior.
• Organizations into banking and finance business can get information about customer’s historical data and financial activities.
• Data Mining help manufacturing units to detect faults in operational parameters.
• Data Mining also helps various governmental agencies to track record of financial activities to curb on criminal activities.

Disadvantages of Data Mining

• Privacy Issues
• Security Issues
• Information extracted from data mining can be misused
• Artificial Intelligence

Artificial Intelligence is the intelligence shown by  machines  and it deals with the study and creation of intelligent systems that can think and act like human beings. In  Artificial Intelligence , intelligent agents are studied that can perceive its environment and take actions according to its surrounding environment.

Goals of Artificial Intelligence

Following are the main goals of Artificial Intelligence:

• Creation of expert systems
• Implementation of human intelligence in machines
• Problem-solving through reasoning

Application of Artificial Intelligence

Following are the main applications of Artificial Intelligence:

• Expert Systems
• Natural Language Processing
• Artificial Neural Networks
• Fuzzy Logic Systems

Strong AI –  It is a type of artificial intelligence system with human thinking capabilities and can find a solution to an unfamiliar task.

Weak AI –  It is a type of artificial intelligence system specifically designed for a particular task. Apple’s Siri is an example of Weak AI.

Turing Test is used to check whether a system is intelligent or not. Machine Learning is a part of Artificial Intelligence. Following are the types of agents in Artificial Intelligence systems:

• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Simple Reflex Agents

Natural Language Processing –  It is a method to communicate with the intelligent systems using human language. It is required to make intelligent systems work according to your instructions. There are two processes under Natural Language Processing – Natural Language Understanding, Natural Language Generation.

Natural Language Understanding involves creating useful representations from the natural language. Natural Language Generation involves steps like Lexical Analysis, Syntactic Analysis, Semantic Analysis, Integration and Pragmatic Analysis to generate meaningful information.

Image Processing

Image Processing is another field in Computer Science and a popular topic for a thesis in Computer Science. There are two types of image processing – Analog and Digital Image Processing. Digital Image Processing is the process of performing operations on digital images using computer-based algorithms to alter its features for enhancement or for other effects. Through Image Processing, essential information can be extracted from digital images. It is an important area of research in computer science. The techniques involved in image processing include transformation, classification, pattern recognition, filtering, image restoration and various other processes and techniques.

Main purpose of Image Processing

Following are the main purposes of  image processing :

• Visualization
• Image Restoration
• Image Retrieval
• Pattern Measurement
• Image Recognition

Applications of Image Processing

Following are the main applications of Image Processing:

• UV Imaging, Gamma Ray Imaging and CT scan in medical field
• Transmission and encoding
• Robot Vision
• Color Processing
• Pattern Recognition
• Video Processing

BELOW IS THE LIST OF FEW LATEST AND TRENDING RESEARCH TOPICS IN IMAGE PROCESSING :-

• To propose classification technique for plant disease detection in image processing
• The hybrid bio-inspired scheme for edge detection in image processing
• The HMM classification scheme for the cancer detection in image processing
• To propose efficient scheme for digital watermarking of images in image processing
• The propose block wise image compression scheme in image processing
• To propose and evaluate filter based on internal and external features of an image for image de noising
• To improve local mean filtering scheme for de noising of MRI images
• To propose image encryption base d on textural feature analysis and chaos method
• The classification scheme for the face spoof detection in image processing
• The automated scheme for the number plate detection in image processing

Bioinformatics

Bioinformatics is a field that uses various computational methods and software tools to analyze the biological data. In simple words, bioinformatics is the field that uses computer programming for biological studies. It is the current topic of research in computer science and is also a good topic of choice for the thesis. This field is a combination of computer science, biology, statistics, and mathematics. It uses image and signal processing techniques to extract useful information from a large amount of data. Following are the main applications of bioinformatics:

• It helps in observing mutations in the field of genetics
• It plays an important role in text mining and organization of biological data
• It helps to study the various aspects of genes like protein expression and regulation
• Genetic data can be compared using bioinformatics which will help in understanding molecular biology
• Simulation and modeling of DNA, RNA, and proteins can be done using bioinformatics tools

Quantum Computing

Quantum Computing is a computing technique in which computers known as quantum computers use the laws of quantum mechanics for processing information. Quantum Computers are different from digital electronic computers in the sense that these computers use quantum bits known as qubits for processing. A lot of experiments are being conducted to build a powerful quantum computer. Once developed, these computers will be able to solve complex computational problems which cannot be solved by classical computers. Quantum is the current and the latest topic for research and thesis in computer science.

Quantum Computers work on quantum algorithms like Simon’s algorithm to solve problems. Quantum Computing finds its application in the following areas:

The list is incomplete as there are a number of topics to choose from. But these are the trending fields these days. Whether you have any presentation, thesis project or a seminar you can choose any topic from these and prepare a good report.

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Assistant Professor Aloni Cohen Receives Prestigious Award for Groundbreaking Research in Machine Learning Complexity

The paper that garnered Cohen the prestigious award examines a basic question: how hard is private machine learning? The task, more formally called private PAC learning, is to learn to classify data given many examples as training data while also hiding the specifics of any individual training example.  At the heart of this work are two fundamental measures of hardness of machine learning: the number of examples and the compute time needed to learn the function.

Titled “ Exploring the Computational Boundaries of Differentially Private PAC Learning ,” Cohen’s research builds upon the connection between private PAC learning and a different, non-private model of learning called mistake-bounded online learning, or online learning for short. Prior work showed that any function of the data can be online learned, can be privately learned with not too many more examples. But it left open whether the same can be said for compute time.

Cohen and his co-authors, Rathin Desai and Mark Bun , resolve this fundamental question. The answer, they show, is no. Some functions can be easily learned online, but cannot be privately learned without breaking certain cryptographic schemes.

“Modern AI systems like Chat-GPT are trained on simply staggering amounts of data, including data about all of us,” said Cohen. “Privacy is only getting harder. Though still very far from the practice of AI, our work helps illuminate the fundamental limits of privacy in the age of machine learning.”

Cohen’s accolade underscores the University of Chicago’s commitment to fostering groundbreaking research in computer science and machine learning. His innovative contributions serve as a beacon for future endeavors, inspiring researchers to explore new frontiers in the quest for knowledge and technological advancement.

“Controlling data disclosure, which is the goal of differentially private machine learning, is just one aspect of a much larger set of problems,” Cohen said. “There’s still so much we don’t understand about how to make good use of data while respecting the subjects and creators of that data. We mostly don’t even understand what the right questions are, which is part of what makes this research area so interesting.”

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• Best Student Paper Award for my research paper with Shashank Kumar Singh titled as “Applying Deep Learning for discovery and analysis of software vulnerabilities: A brief survey” published in International Conference on Soft Computing: Theories and Applications (SoCTA 2019), Springer Advances in Intelligent Systems and Computing (AISC) held at NIT Patna, India in December 2019.
• Best Paper Award for my research paper with Diganta Misra and Anurag Tiwari titled as “Large-Scale Meta-Analysis of Genes Encoding Pattern in Wilson's Disease” published in International Conference on Computer Communication and Computational Sciences (IC4S 2018), Springer Advances in Intelligent Systems and Computing held at Bangkok, Thailand in October 2018
• ACM SIGAPP Award 2014
• Best Paper Award for my research paper with Dr. T. V. Prabhakar (Professor, Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur) titled as “Ontology – Driven MVC: A Variant of MVC Architectural Style” published in the proceedings of International Conference on Software Engineering and New Technologies (ICSENT 2012) held in December 2012 in Hammamet, Tunisia
• Selected as the first female student from Asia under EURECA (European Research and Educational Collaboration with Asia) project 2009 to conduct research at Vrije University, Amsterdam, The Netherlands for 6 months.
• Qualified GATE (Graduate Aptitude Test in Engineering) 2005.
• Selected in Physics Olympiad conducted by Indian Association of Physics Teachers, 1999.
• Awarded the first prize in All India Radio (AIR), Lucknow Akashvani Annual Awards Choral Singing Competition in 1993.
• For further details and application form Click Here
• Prof. Sanjay Kumar Singh takes over as the new Head of the Department w.e.f 01.01.2021.
• Dr. Sukarn Agarwal joined to the post of Assistant Professor in the Department on 03.12.2020.
• Dr. Prasenjit Chanak joined to the post of Assistant Professor in the Department on 13.08.2020.
• Academic Session, 2020-21, ODD Semester, commenced from 10.8.2020, Online Classes for BTech/IDD, Semseter-III Onwards Started
• Dr. Mayank Swarnkar joined to the post of Assistant Professor in the Department on 07.07.2020.
• Dr. Ajay Pratap joined to the post of Assistant Professor in the Department on 30.6.2020.

Challenges and opportunities with Edge Computing Vs Cloud Computing by Balaji Krishnan, Extreme Networks, Chennai and an alumunus of 1995 Batch CSE Dept on 02.01.2020.

Discussion on possible academic and research collaborations by Prof. Sandeep Gupta,​ Director, School of Computing, Arizona State University, USA and an Alumnus of First Batch (1987) of CSE Dept. on 24.12.2019 ​

Bayesian Optimisation and its Applications​ By Dr. Sunil Kumar Gupta, Associate Professor in Artificial Intelligence at Deakin University, Australia​ on 4.1. 2019.

• We are organizing an International conference titled International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021 in collaboration with IIT Patna, California State University, San Bernardino – USA and Emlyon Business School, France. It will be held in online mode on March 29-30, 2021. Conference proceedings will be published in SPRINGER NATURE.
• The topics include Big Data Analytics, Data Mining for Big Data, Data Types for Big Data, Deep Learning for Big Data, Distributed Computing for Big Data/Large Scale systems, AI/Machine Learning for softwares and large scale Applications, Deep learning for softwares, Intelligent systems for Emerging Applications. We have launched the website at the following address
• http://icmlbda.iaasse.org/
• Please consider submitting to this conference. We are interested in the entire range of concepts from theory to practice, including case studies, works - in - progress and conceptual explorations.

The Department of Computer Engineering was established in July 1983. The department offers a 4 year course, B.Tech. in Computer Sc. & Engineering, 5 year Integrated Dual Degree (B.Tech. and M.Tech.) in Computer Sc. & Engineering from 2005-2006, and Ph.D. degree in various specializations of Computer Sc. and Engineering. Computer Sc. & Engineering is the most sought- after branch for the JEE (Advanced) selected students that come to the Institute. Our graduates have distinguished themselves in higher studies at the top Universities. They also occupy positions of eminence in the computer industry. Our Alumni remain in constant touch with us and are contributing in the development of the department. Placements for our graduates are the best in the Institute. Read More

Department of Computer Science & Engineering

Digital image processing is the use of computer algorithms to perform image processing on digital images. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing.

Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

Network group works in different areas of network viz. Network Security, Social Networks, Peer to Peer networks, Wireless Sensor Networks etc.. One interesting problem on which group worked on is Delay Tolerant Proactive Transmission Protocol in collabration with ISRO, SAC, Ahmedabad. The proposed protocol is variation of TCP protocol for deep space (interplanetary internet).

Machine learning is an area of computer science in which algorithms learn and improve from experience. But systems are not explicitly programmed to do so. It comprises of various sub-fields like supervised learning, unsupervised learning, semi-supervised and reinforcement learning. From web search to bioinformatics machine learning has wide applications.

Our research group mostly focuses on the area of dependable and smart software development for various types of end-users. We intend to set up a dependable and smart systems development lab that will provide a facility for learning the concepts by researchers, and the same can be used for conducting purposeful research in the area of software development.Our research group mostly focuses on the area of dependable and smart software development for various types of end-users. We intend to set up a dependable and smart systems development lab that will provide a

HPC Group is providing training, assistance, and support for researchers and students in high performance computing in the institute.

• MyU : For Students, Faculty, and Staff

ML Seminar: Scientific Innovations in the Age of Generative AI

The  UMN Machine Learning Seminar Series  brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the Spring 2024 semester.

This week's speaker,  James Zou  ( Stanford University ), will be giving a talk titled " Scientific Innovations in the Age of Generative AI ".

This talk will explore how we can develop and use generative AI to help researchers and clinicians to enable scientific innovations. I will first discuss how we use AI to generate recipes for making and validating new drugs. Then I will present how we developed visual-language AI to help clinicians interpret histology images. Finally, I will discuss how we use large language models (LLM) to help all of us to write better papers. I will also discuss perspectives on what’s on the new horizon for generative AI. 

James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He is also the faculty director of Stanford AI4Health. He works on both improving the foundations of ML–-by making models more trustworthy and reliable–-as well as in-depth scientific and clinical applications. Many of his innovations are widely used in tech and biotech industries. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe. His research has also been profiled in popular press including the NY Times, WSJ, and WIRED.  

Keller Hall 3-180 and via Zoom .

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Publishing MTech Research

Writing and Publishing MTech Research Paper is an essential part of the M.Tech. program in almost every academic institution throughout the world. However, spotting an International Journal with the free publication for such master degree students might be challenging.

Advanced Journal of Graduate Research published by AIJR is an international and refereed journal where MTech students can publish their research work free of cost. AJGR is inviting M.Tech. students of all disciplines to submit their original research work carried out under the supervision of a faculty member for a possible publication. This specific journal is a peer-reviewed and run by the same academicians as any other regular journal. Publishing M.Tech. research in AJGR will give specific recognition to all students by publishing their academic detail on the abstract page. The only condition is that research work should be mentored by a faculty member and author should follow author guideline for manuscript formatting and providing relevant info.

Important info for Publishing MTech Research

• Article submitted to the AJGR should not be submitted/published/under consideration with any other journal.
• The author should read  guideline  carefully and follow it precisely (Article submitted without following author guideline might be rejected without reviewing). The author should not forget to add students and supervisors detail as described in the guideline.
• The supervisor’s detail of the MTech student should be provided as described in the author guideline.
• The author should consult their supervisor and show manuscript before submission (Supervisor might get contacted by the journal for their explicit consent).
• The author is suggested to watch Youtube Playlist “ Resources for the Authors ” which might be helpful in manuscript writing.
• The author should cite references within the text at the appropriate place. Only listing all references at the end of the manuscript without citing within the text is not acceptable.
• All figures and tables should have appropriate figure/table number with a descriptive title and each figure/table should be explained in the text by referring to the corresponding figure/table number.
• The author should submit manuscript online and add all author(s) with complete detail in step 3 of the submission (In the same order as present in the manuscript). Article submitted without adding all authors in online submission system might get rejected without further reviewing.

Important links for MTech research submission

Journal homepage: Click  here Author Guideline: Click  here Aims and Scope: Click  here Online Submission: Click  here Reviewing Time and Rapid Review Option: Click  here

Beside M.Tech. students, AJGR is also publishing research work written by other Bachelor/Master Degree students of scientific discipline, for example, students of the M.E./M.S./M.Sc./M.Phil./B.E./B.S./B.Sc./B.Tech./MCA Program. Normal publication is free for all students. For rapid publication kindly read rapid review options.

Materials Science Journal

Call for Papers on Chemical Sciences

Publish Review Article

Call for Papers: Nanoscience and Nanotechnology

Call for papers: composite materials.

• Publish with AIJR
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• Proceedings vs Book of Abstracts
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MTech Project Topics for Final Year 2023-24

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