1. Taneja, S., & Kumar, N., Python Programming: A Modular Approach. Pearson Education (In Press).
2. Kumar, N., Goel, A., Banati, H., Taneja, S., Badhani, S., Wassan, J.T. & Adlakha M. (2016). IT Tools. Class XI Student Handbook (Information Technology Vocational Course). CBSE. ISBN 978-81-929704-20-2.
3. Kumar, N., Goel, A., Banati, H., Saxena, R., Taneja, S. & Malik, S. (2016). Database Management Applications. Class XII Student Handbook (Information Technology Vocational Course). CBSE. ISBN 978-81-929704-20-4.
4. Kumar, N. (1994), Computer Science Concepts. Galgotia Publications.
1. Gupta, S., Taneja, S., & Kumar, N. (2015). Redefining the Classroom: Integration of Open and Classroom Learning in Higher Education. In Macro-Level Learning Through Massive Open Online Courses (MOOCs)-Strategies and Predictions for the Future. IGI Global.
2. Bhatnagar, V., Gupta, A., & Kumar, N. (2009). Algorithms for Association Rule Mining. In Encyclopedia of Artificial Intelligence, 76-84. IGI Global.
3. Gupta, A., Gupta, S., & Kumar, N. (2009). Mining Frequent
Closed Itemsets for Association Rules. In Encyclopedia of Artificial Intelligence,
537-546. IGI Global.
1. Gupta, S.; Mittal, S.; Gupta, T.; Singhal, I.; Khatri, B.; Gupta, A. K. & Kumar, N. (2017). Parallel Quantum-inspired Evolutionary Algorithms for Community Detection in Social Networks. International Journal of Applied Soft Computing, Accepted for Publication. IF 3.541, SJR 1.308
2. Puri, C., & Kumar, N (2017). Type-2 Projected Gustafson-Kessel Clustering Algorithm. International Journal of Computer Applications, 167(14):1-6.
3. Agarwal, M., Agrawal, N., Sharma S., Vig L., & Kumar, N. (2015). Parallel Multi-objective Multi-robot Coalition Formation. Expert Systems with Applications, 42(21), 7797-7811. IF 1.965, SJR 1.487
4. Agarwal, M., Kumar, N., & Vig, L. (2014). Non-additive Multi-objective Robot Coalition Formation. Expert Systems with Applications, 41(8), 3736-3747. IF 1.965, SJR 1.487
5. Verma, H., Agrawal, R. K., & Naveen, K. (2014). Improved Fuzzy Entropy Clustering Algorithm for MRI Brain Image Segmentation. Signal, Image and Video Processing (SIVP). IF 1.019, SJR 0.29
6. Aggarwal, A., & Kumar, N. (2011). SAHAM: Shared Adaptive Hypermedia Application Model. International Journal of Computer Applications in Technology, 40(1), 138-145. SJR 0.25
7. Agarwal, M., Vig, L., & Kumar, N. (2011). Multiple Objective Robot Coalition Formation. Journal of Intelligent Systems, 20(4), 395-413. SJR 0.16
8. Puri, C., & Kumar, N. (2011). Projected Gustafson-Kessel Clustering Algorithm and Its Convergence. Transactions on Rough Sets XIV, 159-182.
9. Bhatnagar, V., Al-Hegami, A. S., & Kumar, N. (2006). Novelty as a Measure of Interestingness in Knowledge Discovery. International Journal of Information Technology, 2(1).
10. Gupta, A., Kumar, N., & Bhatnagar, V. (2005). Analysis
of Medical Data Using Data Mining and Formal Concept Analysis. World Academy
of Science, Engineering and Technology, 11, 61-64. SJR 0.12
1. Gupta, S., Khatri, B., Gupta, T., & Kumar, N. (2015). Accepted for publication. Modified Partition Integration Method for Community Detection in Multidimensional Social Networks. In International Conference on Natural Computation (ICNC). H5-index 16.
2. Gupta, S., Taneja, S., & Kumar, N. (2014). Quantum Inspired Genetic Algorithm for Community Structure Detection in Social Networks. In ACM International Conference on Genetic and Evolutionary Computation Conference (GECCO), 1119-26. H5-index 31
3. Gupta, S., & Kumar, N. (2014). GPU-based Massively Parallel Quantum Inspired Genetic Algorithm for Detection of Communities in Complex Networks. In ACM International Conference on Genetic and Evolutionary Computation Conference(GECCO companion), 163-64. H5-index 31
4 . Gupta, S., & Kumar, N. (2014). Parameter Tuning in Quantum-Inspired Evolutionary Algorithms for Partitioning Complex Networks. In ACM International Conference on Genetic and Evolutionary Computation Conference (GECCO companion), 1045-48. H5-index 31
5. Gupta, A., Kumar, N., & Bhatnagar, V. (2012). Mining of Multiobjective Non-redundant Association Rules in Data Streams. International Conference on Artificial Intelligence and Soft Computing, 73-81. Springer, LNCS. H5-index 13
6. Gupta, S., & Kumar, N. (2012). Higher Education - A Paradigm Shift Towards Integration of Traditional and Online Education. Academic Congress, University of Delhi. Delhi.
7. Agarwal, M., Vig, L., & Kumar, N. (2011). MORCFA: A Multiple Objective Robot Coalition Formation Algorithm. 5th Indian International Conference on Artificial Intelligence (IICAI), 268-279. H5-index 6
8. Agarwal, M., Vig, L., & Kumar, N. (2011). Multi-objective Robot Coalition Formation for Non-additive Environments. 4th International Conference on Intelligent Robotics and Applications (ICIRA), 346-355. H5-index 9
9. Puri, C., & Kumar, N. (2011). Projected Rough Fuzzy c-means clustering. International Conference on Intelligent Systems Design and Applications (ISDA), 530-536. H5-index 15
10. Gupta, A., Bhatnagar, V., & Kumar, N. (2010). Mining Closed Itemsets in Data Stream Using Formal Concept Analysis. International Conference on Data Warehousing and Knowledge Discovery, 285-296. Springer, LNCS. H5-index 14
11. Kant, S., Kumar, N., Gupta, S., Singhal, A., & Dhasmana, R. (2009). Impact of Machine Learning Algorithms on Analysis of Stream Ciphers. International Conference on Methods and Models in Computer Science (ICM2CS), 251-258.
12. Kumar, N., Ojha, S., Jain, K., & Lal, S. (2009). BEAN: A Lightweight Stream Cipher. In 2nd ACM International Conference on Security of Information and Networks, 168-171. H5-index 11
13. Kumar, N., & Puri, C. (2009). Projected Gustafson Kessel Clustering. International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 431-438. Springer, LNCS. H5-index 10
14. Ojha, S. K., Kumar, N., Jain, K., & others. (2009). TWIS--A Lightweight Block Cipher. International Conference on Information Systems Security, 280-291. Springer, LNCS. H5-index 12
15. Puri, C., & Kumar, N. (2009). A Type-2 Projected FCM. International Conference on Methods and Models in Computer Science (ICM2CS), 1-8.
16. Agarwal, M., Agrawal, R. K., & Kumar, N. (2006). Identification of Relevant Feature Sets for Multi-class Intrusion Detection Problem. National Conference on Methods and Models in Computing, 145-151.
17. Kumar, N., Gupta, A., & Bhatnagar, V. (2006). Fast Construction of Concept Lattice. 4th International Conference on Concept Lattices and their Applications, 10, 315-316.
18. Aggarwal, A., Grover, P., & Kumar, N. (2005). Applying ISO 9126 for Quality Evaluation of Adaptive Hypermedia Systems. World Conference on Educational Multimedia, Hypermedia and Telecommunications.
19. Bhatnagar, V., Al-Hegami, A. S., & Kumar, N. (2005). A Hybrid Approach for Quantification of Novelty in Rule Discovery. 2nd World Enformatika Conference, 39-42.
20. Gupta, A., Kumar, N., & Bhatnagar, V. (2005). Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis. International Conference on Machine Learning and Data Mining in Pattern Recognition, 11-20. Springer, LNCS. H5-index 10
21. Kumar, N., & Narang, V. (2005). Mining Positive and Negative Association Rules Based on Closed Itemsets: An Approach for Generalized Rules. International Conference on Data Mining (DMIN), 104-118. H5-index 8
22. Al-Hegami, A. S., Bhatnagar, V., & Kumar, N. (2004). Novelty Framework for Knowledge Discovery in Databases. International Conference on Data Warehousing and Knowledge Discovery, 48-57. Springer, LNCS. H5-index 14
23. Mital, N., Kumar, N., & Bhatnagar, V. (2004). Mining Multiple Table Databases With Multiple Minimum Support Constraints. Data Mining and Knowledge Discovery: Theory, Tools, and Technology, 190-200.
24. Narang, V., & Kumar, N. (2004). Mining Fuzzy Conceptual
Clusters and Constructing the Fuzzy Conceptual Frame Lattices. Data Mining
and Knowledge Discovery: Theory, Tools, and Technology, 201-208.