School of Computer Science Engg and Technology, Bennett University, India

22/05/2026

Round 3 Applications Closing Soon.

The future belongs to those who are ready to lead it.

Join Bennett University and choose from future-focused UG & PG programs designed to prepare you for tomorrow’s opportunities.

With the strong legacy of Times Group, industry-led learning, global exposure, and a vibrant campus experience, your journey towards success starts here.

Apply before 31st May and move one step closer to your dream career.
Apply Now: https://bit.ly/4m3Q2Mz

22/05/2026

Congratulations to Dr. Umesh Gupta (Associate Professor, scsetbennett) and Akshaj Singh (B.Tech. CSE 2022-2026) for acceptance of the paper, “SmartFace: An ML-Based System for Criminal Identification” for in the 5th ICCCNet UK MANCHESTER.

This study presents an advanced criminal detection system that combines traditional identification methods with modern biometric technologies. It leverages an enhanced ensemble approach integrating eight machine learning and deep learning models, including k-NN, SVM, Random Forest, XGBoost, Vision Transformer, EfficientNet, ResNet, and DenseNet. By combining these models, the system improves accuracy and overcomes individual limitations. Experimental results demonstrate high performance, achieving accuracies of 96.7%, 94.1%, and 94.8% across three datasets, highlighting its effectiveness for real-world criminal identification.

https://link.springer.com/chapter/10.1007/978-3-032-21499-7_17

21/05/2026

Congratulations to Dr. Shahnawaz Ahmad (Assistant Professor, ) for the publication of paper, “A holistic strategy for choosing software requirements through multi-criteria intuitionistic fuzzy TOPSIS” in the International Journal of System Assurance Engineering and Management, Springer
Authors proposed a novel decision-making framework that integrates Intuitionistic Fuzzy Sets (IFS) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach effectively models membership, non-membership, and hesitation degrees, thereby providing a richer representation of uncertainty. A structured methodology is presented, including criteria definition, weight determination, intuitionistic fuzzy decision matrix construction, and TOPSIS-based ranking. A case study on an Institute Examination System demonstrates the framework’s applicability and effectiveness, showing consistent ranking results with reduced ex*****on time compared to Fuzzy TOPSIS. The proposed method offers a more accurate and efficient mechanism for software requirements selection, supporting informed and reliable decision-making.
https://link.springer.com/article/10.1007/s13198-026-03153-8

21/05/2026

We are incredibly proud to celebrate the exceptional students from for successfully earning their AWS Certified Solutions Architect - Associate certification. Achieving this prestigious global credential recognizes their expertise in designing and deploying secure, robust, and scalable cloud technologies on AWS. This is a major milestone that highlights their technical dedication, forward-thinking mindset, and readiness to lead the future of cloud computing.

A special shoutout to our newly certified achievers: Yash Kumar (B.Tech. 2022-26), Siddhartha Jain, Vaishnavi, Venugopal Chepuri, Abhijeet Agrawal, Brajraj Mahajan, Nehanth Karnati, and Sachit Dadwal (B.Tech. 2024-28). At Bennett University, we consistently encourage our students to look beyond the classroom, bridge the industry-academia gap, and proactively acquire industry-demanded skills. Way to set the bar high, team! We cannot wait to see how you leverage this incredible milestone to transform the tech landscape in your future endeavors.

20/05/2026

Huge congratulations to our brilliant students from the for successfully achieving the prestigious AWS Certified AI Practitioner certification! By earning this globally recognized credential, these future tech leaders from our B.Tech. 2023-27 and 2024-28 batches (Devansh Ansh, Hari Ram Varma, Aman Swamy, Shivendra Singh, Aniket Pandit, Brajraj Mahajan, Lavanya Sharma, Prakhar Garg, and Riwitika Gupta) have shown an outstanding foundation in cloud-based artificial intelligence and machine learning concepts. Your dedication to continuous upskilling and staying ahead in today's rapidly evolving technological landscape is truly inspiring, and we cannot wait to see how you leverage these advanced skills to build innovative, real-world solutions. Keep pushing boundaries and leading the way!

19/05/2026

Congratulations to Dr. Reshu Chaudhary (Assistant Professor, ), for acceptance of the paper, “Five Decades of Genetic Algorithms: A Systematic and Bibliometric Review (1975–2025)” for in Archives of Computational Methods in Engineering, Springer.

This paper presents a comprehensive bibliometric and thematic review of GA research spanning five decades (1975–2025). Based on the analysis of over 26,707 publications indexed in the Scopus, Web of Science (WoS), and IEEE Xplore databases, the study captures the historical development, methodological advances, and widespread applications of GA’s. The review uses advanced visualization techniques such as Sankey diagrams (to trace country-institution-keyword linkages), Choropleth maps (to illustrate global research contributions), coauthorship and citation networks, and cross-domain heatmaps, the review identifies leading scholars, dominant themes, and emerging research trends. Coverage includes publication patterns, high-impact contributors, core methodological innovations, and the algorithm’s deployment across 12 major domains, including artificial intelligence, engineering design, robotics, bioinformatics, and energy systems. The study highlights the evolution of GA variants, the rise of hybrid and memetic models, integration with deep learning and quantum paradigms, and the increasing prominence of Asia, particularly China, India, and Iran, as global leaders in GA research. Through keyword co-occurrence analysis, thematic clustering, and citation dynamics, this review synthesizes the trajectory of GA scholarship and presents a forward-looking roadmap for future exploration and interdisciplinary growth.

https://link.springer.com/article/10.1007/s11831-025-10487-2

19/05/2026

Big milestones call for a major celebration! We are incredibly proud to spotlight our brilliant students from the for successfully earning their AWS Certified Cloud Practitioner certifications. Validating foundational cloud knowledge, architecture, and security practices early in their academic journey is no small feat. Huge congratulations go out to our talented B.Tech. Class of 2024–28 achievers: Shubhangi Singh, Siddhartha Jain, Tanishka Vashishth, Tanmay Maliwal, Tanya Gupta, Twinkle Sati, Ashay Jain, Shikhar Verma, Vikalp Chaudhary, Utkarsh Gupta, Vaibhav Agarwal, Vaibhav Gupta, Vaishnavi, and Vansh Pasricha. At Bennett University, we work to connect classroom theory with industry expertise, and seeing our students build skills for cloud computing careers is very rewarding. Join us in celebrating their hard work and wishing them the absolute best as they continue to reach higher milestones!

19/05/2026

is proud to celebrate the achievements of our B.Tech. 2023–27 students (Anish Sharma, Divyansh Vishwakarma, Gazula Mahaswi Shankar, Kriti Tripathi, Shivam, Sidhdesh Jindal, Navya Gupta, and Nimish Lohia) who have successfully earned the prestigious AWS Certified Solutions Architect Associate certification! This globally recognized milestone highlights their technical dedication, expertise in cloud architecture, and readiness to build robust, scalable solutions on AWS. Kudos to their hard work and commitment to staying ahead in the ever-evolving tech landscape. The future of cloud computing looks incredibly bright, and we cannot wait to see where their skills take them next!

18/05/2026

18/05/2026

Congratulations to Dr. Yogendra Kumar (Assistant Professor, ) for the publication of the paper, “A novel Grey Wolf optimizer-based IDS framework in IoMT” in Wireless Networks, Springer.
This paper presents a novel Grey Wolf Optimizer (GWO) based Intrusion Detection System framework for the IoMT architecture. A GWO is employed to extract the optimal set of features. The K-Nearest Neighbour classifier is used to detect malicious activities in the IoMT architecture. The proposed IDS framework is evaluated over the WUSTL-EHMS-2020 dataset. The proposed framework significantly decreases the input data’s dimensionality while maintaining a minimum false alarm rate. The experimental results show that the proposed framework outperforms the existing methods with an increase in accuracy and AUC scores by 3.11% and 4.53%, respectively.
https://link.springer.com/article/10.1007/s11276-026-04098-3