Kuldeep Kumar

Designation:    Assistant Professor
Department:    Computer Engg
Qualification:    PhD (NUS Singapore) | MTech (NIT Kurukshetra) | BTech (UIET Kurukshetra)
Email:    kuldeepkumar@nitkkr.ac.in
Phone No:    9461210063
Area of Interest:    

Software Security, Software Engineering, Natural Language Processing, Applied Machine Learning, Big Data Analytics,

Erdos Number: 4 (Kuldeep Kumar – Stanislaw Jarzabek – Tomasz Krawczyk – William T. Trotter – Jr-Paul Erdos).
– PhD Thesis listed in PhD Dissertations in the Area of Software Engineering repository maintained by ACM.
– Recipient of the Early Adopter Award by CDER USA and the Early Career Research Award by DST, Govt of India.
– Involved in the development of tools and techniques in-use by Software Industry.


About: Born and Brought up in Holy land of Dharmakshetra Kurukshetra, Dr Kuldeep Kumar Garg holds a Ph.D. degree from the School of Computing, National University of Singapore, Singapore (Leading the World with Asia’s Best, QS World Rank 2023 #06). His research interests include Software Engineering, Applied Machine Learning, and Data Analytics. With multiple IPRs and externally funded sponsored research projects, he has 50+ publications in international journals and conferences of repute. He is a recipient of the Early Adopter Award by CDER USA and the Early Career Research Award by DST, Govt of India.

Externally Funded Sponsored Projects: 04
Tools and Technology Development : 01 (In use in Software Industry)
IPRs: 04
Publications: 50+

Individual interested in collaboration or pursuing PhD/project/product development may contact via email at kuldeepkumar@nitkkr.ac.in

Recent Publications:


[1] R Verma, Kuldeep Kumar, HK Verma, “Prioritizing God Class Code Smells in Object-Oriented Software Using Fuzzy Inference System”, Arabian Journal of Science and Engineering (2024), https://doi.org/10.1007/s13369-024-08826-9.

[2] R. Singh, Kuldeep Kumar, “Hybrid optimization-enabled deep Q network for fault prediction in service-oriented architecture”. The Journal of Supercomputing, (2024), https://doi.org/10.1007/s11227-023-05659-5.

[3] K Bhandari, Kuldeep Kumar, AL Sangal, “Alleviating Class Imbalance Issue in Software Fault Prediction Using DBSCAN-Based Induced Graph Under-Sampling Method”, Arabian Journal of Science and Engineering (2024), https://doi.org/10.1007/s13369-024-08740-0.

[4] R. Singh and K. Kumar, “Software Fault Prediction in Service-Oriented Based Systems,” 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Greater Noida, India, 2024, pp. 1131-1136, doi: 10.1109/IC2PCT60090.2024.10486778.

[5] P Bagla & Kuldeep Kumar (2024) AHBO-DMN: Autoregressive Honey Badger Optimization Deep Maxout Network for Credibility Assessment of Web-Based Health Information, IETE Journal of Research, DOI: 10.1080/03772063.2023.2301653.

[6] Singh, R., & Kumar, K. (2024). Counterfeit Medicine Detection Using Blockchain Technology. In Future of AI in Medical Imaging (pp. 76-91). IGI Global.


[1] P Bagla, K Kumar, “Breaking down health fakes: a hybrid DNN model for multi-class classification on a self-constructed dataset”, Sādhanā 48, 233 (2023). https://doi.org/10.1007/s12046-023-02300-2.

[2] P Bagla, K Kumar, “A rule-based fuzzy ant colony improvement (ACI) approach for automated disease diagnoses”. Multimedia Tools and Applications (2023). https://doi.org/10.1007/s11042-023-15115-4.

[3] Ravi Khatri and Kuldeep Kumar. “Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress.” In International Conference on Computing, Communication and Learning, pp. 27-38. Cham: Springer Nature Switzerland, 2023.

[4] K Bhandari, Kuldeep Kumar, AL Sangal, “Data quality issues in software fault prediction: a systematic literature review”, Artificial Intelligence Review, 56, 7839–7908 (2023). https://doi.org/10.1007/s10462-022-10371-6.

[5] S Singh, Kuldeep Kumar, “Software Cost Estimation: A Literature Review and Current Trends”, 2023 Third IEEE International Conference on Secure Cyber Computing and Communication (ICSCCC-2023), pp. 469-474,

[6] K Bhandari, Kuldeep Kumar, AL Sangal, “Artificial Intelligence in Software Engineering: Perspectives and Challenges”, 2023 Third IEEE International Conference on Secure Cyber Computing and Communication (ICSCCC-2023), pp. 133-137.

[7] R Verma, Kuldeep Kumar, HK Verma, “Code smell prioritization in object-oriented software systems: A systematic literature review.” Journal of Software: Evolution and Process, Wiley (2023), e2536. doi:10.1002/smr.2536

[8] Sanchita Pandey and Kuldeep Kumar, (2023). Analysis of Different Sampling Techniques for Software Fault Prediction. In: Rao, U.P., Alazab, M., Gohil, B.N., Chelliah, P.R. (eds) Security, Privacy and Data Analytics. ISPDA 2022. Lecture Notes in Electrical Engineering, vol 1049. Springer, Singapore.

[9] Sanchita Pandey, Kuldeep Kumar, “Software Fault Prediction for Imbalanced Data: A Survey on Recent Developments”, International Conference on Machine Learning and Data Engineering (ICMLDE), Procedia Computer Science, Volume 218, 2023, Pages 1815-1824, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.01.159.

[10] Bagla, Kuldeep Kumar, “TA-WHI: Text Analysis of Web-Based Health Information”, International Journal of Software Science and Computational Intelligence (2023), 15(1), 1-14.