Anjali Bansal

Address

Room No. 510-A, Academic Block - 4, Delhi Technological University, Shahbad, Daulatpur, Main Bawana Road, Delhi-110042, India.

Profile Summary

Anjali Bansal is an assistant professor in the Department of Computer Science and Engineering at Delhi Technological University (DTU) and is pursuing a part-time Ph.D. in the Department of Software Engineering at DTU, where her research focuses on the development and evaluation of methodologies for maintaining software containers. She has completed her M.Tech in Software Engineering from DTU and her B.Tech in Computer Science and Engineering from Noida Institute of Engineering and Technology. She has qualified UGC-NET JRF in 2022 and GATE in 2019. Her academic research interests include Machine Learning, Deep Learning, Federated Learning, Predictive Analytics, Software Engineering, Image Processing, and Cloud Computing. She has published research papers in SCI-indexed journals such as ACM Computing Surveys and Cluster Computing and has presented her work at various national and international conferences.

• Object Oriented Programming
• Object Oriented Software Engineering
• Research Paper Writing
• Design and Analysis of Algorithms
• Programming Fundamentals
• Compiler Design
• Discrete Structure
• Cloud Administration

• Malhotra, R., Bansal, A., & Kessentini, M. (2026). Assessing Security Vulnerabilities in a Docker enabled Federated Learning Framework with Hyperparameter Tuning for Software Bug Prediction. International Journal of Information Security. 25, 49. https://doi.org/10.1007/s10207-026-01213-5. (Impact Factor: 3.2)
• Malhotra, R., Bansal, A., & Kessentini, M. (2024). A Systematic Literature Review on Maintenance of Software Containers. ACM Computing Surveys, 56(8), 1-38. https://doi.org/10.1145/3645092. (Impact Factor: 28.0)
• Malhotra, R., Bansal, A. & Kessentini, M. (2024). Deployment and Performance Monitoring of Docker based Federated Learning Framework for Software Defect Prediction. Cluster Computing, 27, 6039–6057.  https://doi.org/10.1007/s10586-024-04266-0. (Impact Factor: 4.1)
• Malhotra, R., Bansal, A., & Kessentini, M. (2026) A Hybrid Metaheuristic Approach: Whale Optimization and Grey Wolf for Deep Learning in Software Bug Prediction. In: Swaroop, A., Virdee, B., Correia, S.D., Polkowski, Z. (eds) Proceedings of Data Analytics and Management. ICDAM 2025. Lecture Notes in Networks and Systems, vol 1615. Springer, Cham. https://doi.org/10.1007/978-3-032-04222-4_36.
• Malhotra, R., Bansal, A., Kessentini, M. (2025). Toward Federated Learning Approach for Workload Prediction in Cloud Computing. In: Swaroop, A., Virdee, B., Correia, S.D., Polkowski, Z. (eds) Proceedings of Data Analytics and Management. ICDAM 2024. Lecture Notes in Networks and Systems, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-96-3358-6_7.
• Malhotra, R., Bansal, A., & Kessentini, M. (2023, July). Vulnerability analysis of docker hub official images and verified images. In 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE) (pp. 150-155) IEEE, Athens, Greece.  https://doi.org/10.1109/SOSE58276.2023.00025.
• Malhotra, R., Bansal, A. (2022). Use of Support Vector Machine to Check Whether Process Metrics are as Good as Static Code Metrics. In: Mandal, J.K., Hsiung, PA., Sankar Dhar, R. (eds) Topical Drifts in Intelligent Computing. ICCTA 2021. Lecture Notes in Networks and Systems, vol 426. Springer, Singapore. https://doi.org/10.1007/978-981-19-0745-6_4.

• Commendable Research Award at "RIEA 2025, DTU".