1. Kumar MR, Uyyala R, Ramchander M, Ramadevi Y, Palamakula RB, Padmalatha E, et al. (2026) Reconfigurable intelligent surface and UAV coordination for reliable THz wireless networks. PLoS One 21(3): e0345290. https://doi.org/10.1371/journal.pone.0345290 (SCI Indexed)
2. Kolla M, Madapuri RK, Kandukuri P, Salvadi S, Tadepalli S, Gajula R. Optimized cortical EEG modeling

for  Parkinson disease diagnosis with snow Shepherd Stride tuning mechanism. Cogn Neurodyn. 2026 Dec;   20(1):47. doi: 10.1007/s11571-025-10406-y. Epub 2026 Feb 6. PMID: 41657964;

PMCID: PMC12881250.        (SCI Indexed)

3. R. Kumar, P. Kandukuri, V. S. Srinivas, N. Mukkapati, and D. N. V. S. Kumar, “AI-SCAN: Advancing plant leaf disease detection with Transcendental Residual Convolutional Swin Transformer with hybrid optimizer,” Agribioinformatics, vol. 2025, no. 1, Mar. 2025, doi: 10.1079/ab.2025.0022.

4. Tumula, S., Ramadevi, Y., Rudra Kumar, M. et al. Optimizing internet of things security through blockchain enabled software defined networking. Sci Rep 15, 43744 (2025). https://doi.org/10.1038/s41598-025- 27401-2 (SCI Indexed)

5. Kumar, S.S., Kumar, M.R. (2024). Automatic rule discovery for data transformation using fusion of diversified featureformats.Computers,Materials &Continua,80(1),695-713.https://doi.org/10.32604/cmc.2024.050143  

6. Kumar,M. Rudra and Kandukuri,Prabhakar and Srinivas,V. Sesha and Mukkapati,Naveen and Syma Kumar,Dasari V., ab.2025.0022, CABI Agriculture and Bioscience, doi:10.1079/ab.2025.0022, CABI, AI- SCAN: Advancing plant leaf disease detection with Transcendental Residual Convolutional Swin Transformer With hybrid optimizer, (2025). (Web of Science)

7. Kumar, G.S.S., Kumar, M.R. (2025). Deep learning-based ETL framework for automating data transformation in big data analytics. Mathematical Modelling of Engineering Problems, Vol. 12, No. 11, pp. 3971-3979. https://doi.org/10.18280/mmep.121123 (Scopus Indexed)
8. Kumar, Sunil Santhosh. “Towards Autonomous Data Transformation: A Hybrid Deep Reinforcement Learning and Transformer Framework for ETL Automation.” International Journal of Intelligent Engineering and Systems, vol. 18, no. 11, 2025, doi:10.22266/ijies2025.1231.15. (Scopus Indexed)
9. Rudra Kumar M, Rama Vasantha Adiraju, LNC Prakash K, Mahalakshmi V, Penubaka Balaji and Jayavardhanarao Sahukaru, “Hybrid Data Driven Deep Learning Framework for Material Property Prediction”, Journal of Machine and Computing, vol.5, no.2, pp. 1068-1083, April 2025, doi: 10.53759/7669/jmc202505085 (Scopus Indexed)
10. Rudra Kumar et al., “Agriculture Crop Yield Prediction using Inertia based Cat Swarm Optimization”, International Journal of Electrical and Computer Engineering (IJECE) Vol. 99, No. 1, pp. 1~1x. DOI: 10.11591/ijece.v14i2.pp1700-1710 (Scopus Indexed)
11. Rudra Kumar et al., “A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas,” IEEE Transactions on Intelligent Transportation Systems, January 2023, pp. 1-13, 09 DOI: 10.1109/TITS.2022.3233801. (SCI Indexed)
12. Rudra Kumar et al., “Automated Cotton Leaf Identification Using Feature Selection Techniques,” International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 410–415. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3506 (Scopus Indexed)

13. Rudra Kumar et al., “Enhancing Collaborative Filtering with Multi-Model Deep Learning Approach”, Int J Intell Syst Appl Eng, vol. 11, no. 6s, pp. 01–12, May 2023. (Scopus Indexed)
14. Rudra Kumar et al., “Real-Time Hand Gesture Recognition for Improved Communication with Deaf and Hard of Hearing Individuals”, Int J Intell Syst Appl Eng, vol. 11, no. 6s, pp. 23–37, May 2023. (Scopus Indexed)
15. Vadlamaani , S. M., P. K. . Bharti, and M. R. . Kumar. “Generalized Statistical Indicators for Cloud Computing Fault Tolerance”. International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1s, Oct. 2022, pp. 269 -281. (Scopus Indexed)

16. Siraj, S.., P. K. . Singuluri, and M. R. . Kumar. “AI Intelligence-Based Gender Classification Using Biometric- Digital Signature Feature Extraction Methods”. International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1s, Oct. 2022, pp. 262-269. (Scopus Indexed)

17. Swetha, ., M. S. . Lakshmi, and M. R. . Kumar. “Chronic Kidney Disease Diagnostic Approaches Using Efficient Artificial Intelligence Methods”. International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1s, Oct. 2022, pp. 254 -261. (Scopus Indexed)

18. Thulasi , S. ., B. . Sowjanya, K. . Sreenivasulu, and M. R. . Kumar. “Knowledge Attitude and Practices of Dental Students and Dental Practitioners Towards Artificial Intelligence”. International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1s, Oct. 2022, pp. 248-53. (Scopus Indexed)

19. Rudra Kumar et al., “Crop Yield Forecasting using Long Short-Term Memory Network with Adam Optimizer and Huber Loss Function”, Concurrency and Computation: Practice and Experience. September 2022, https://doi.org/10.1002/cpe.7310 (SCI Indexed)

20. Rudra Kumar et al., “Leaf Disease Classification Smart Agriculture using Deep Neural Network Architecture and IoT,” Journal of Circuits, Systems, and Computers. Vol. 31, February 2022. (SCI Indexed)

21. Rudra Kumar et al., “Dynamic Wavelength Scheduling by Multi Objectives in OBS Networks,” Journal of Mathematics. 2022. (SCI Indexed)
22. M Rudra Kumar et , “Machine Learning-Based Project Resource Allocation Fitment Analysis System (ML- PRAFS),” in Computational Intelligence in Machine Learning of EE Book series (LNEE Volume Singapore January 2022. Pp 1-14, (Scopus Indexed)
23. M Rudra Kumar et , “Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach,” in Computational Intelligence in Machine Learning of EE book series (LNEE, volume 834), Springer, Singapore. January 2022. pp 123-133(Scopus Indexed)
24. M Rudra Kumar et al., “Ensemble Learning by High Dimensional Acoustic Features for Emotion Recognition from Speech Audio Signal,” Security and Communication Networks. Volume 2022, Feb’2022, Article ID 8777026, https://doi.org/10.1155/2022/8777026 (SCI Indexed)

25. M Rudra Kumar et , “Early detection of Cognitive Decline using Machine Learning algorithm and Cognitive Ability Test,” Security and Communication Networks, Volume 2022, Jan’2022, |Article ID 4190023 | https://doi.org/10.1155/2022/4190023. (SCI Indexed)
26. M Rudra Kumar et al. “Data mining and deep learning-based hybrid health care application,” Applied Nanoscience (2022). https://doi.org/10.1007/s13204-021-02333-1 (SCI Indexed)

27. Kumar, G. Sunil Santhosh, and M. Rudra Kumar. “Dimensions of Automated ETL Management: A Contemporary Literature Review.” Helix-The Scientific Explorer| Peer Reviewed Bimonthly International Journal 11.5 (2021): 47-54

28. Ramana, Kadiyala, Madapuri Rudra Kumar, K. Sreenivasulu, Thippa Reddy Gadekallu, Surbhi Bhatia, Parul Agarwal, and Sheikh Mohammad Idrees. “Early prediction of lung cancers using deep saliency capsule and pre-trained deep learning frameworks.” Frontiers in Oncology 12 (2022). https://www.frontiersin.org/articles/10.3389/fonc.2022.886739/full

29. Lakshmi, Naga, S. Jyothi, and M. Rudra Kumar. “Image Encryption Algorithms Using Machine Learning and Deep Learning Techniques—A Survey.” Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Springer, Cham, 2021. 507-515. (Scopus Indexed)

30. Madapuri Rudra Kumar, Vinit Kumar Gunjan, Mohd Dilshad Ansari, “Learning-Based HR Appraisal System (ML- APS),” International Journal of Applied Management Science. (Scopus Indexed)

31. Kumar, Madapuri Rudra, et , “Personal finance transaction index scoring using machine learning model.”Materials Today: Proceedings (2021). (Scopus Indexed)

32. Gunjan, Vinit Kumar, and Madapuri Rudra Kumar. “Predictive Analytics for OSA Detection Using Non- Conventional ” International Journal of Knowledge-Based Organizations (IJKBO) 10.4 (2020): 13-23. (IET Inspec, ACM Digital Library Indexed) (Scopus Indexed)

33. Kumar, Madapuri Rudra, and Vinit Kumar Gunjan, “Review of machine learning models for credit scoring analysis.” Ingeniería Solidaria 16.1 (2020). (Web of Science)

34. Mahesh, PC Senthil, K. Sasikala, and M. Rudra Kumar. “Face Recognition Based Automated Student Attendance System using Deep Learning.” International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), Vol. 9, No. 3, June 2020. (Scopus Indexed)

35. MRK K Uday Kumar Reddy, G Chennakessava Reddy, “Estimating Available Bandwidth Using End- to-End Delay Increase Rate,” International Journal of Innovative Technology and Exploring Engineering (IJITEE),11 (2019).

36. GIKDB M Rudra Kumar, T N Ranganatham, “Optimal Integrity Policy for Secure Storage of Encrypted Data Using Cloud Computing,” International Journal of Innovative Technology and Exploring Engineering (IJITEE).
37. 11 (2019). (Scopus Indexed)

37. M Rudra Kumar et , “Reciprocal Repository for decisive Data Access in Tolerant Networks,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 9, Issue 1,2019. (Scopus Indexed)

38. Madapuri, Rudra Kumar, and PC Senthil Mahesh. “HBS-CRA: scaling impact of change request towards fault proneness: defining a heuristic and biases scale (HBS) of change request artifacts (CRA).” Cluster Computing

22.5 (2019): 11591-11599. (SCI Indexed)

39. M Rudra Kumar et , “Assessment of Change Request Artifacts Impact towards Fault Proneness,” International Journal of Computer Science Issues, Vol 11, Issue 5, 2014.

40. M Rudra Kumar et , “Assessing the Fault Proneness Degree (DFP) of Change Request Artifacts by Estimating the Impact of Change Request Artifacts Correlation,” International Journal of Information Processing (IJIP), Vol 8, Issue 4, 2014.

41. M Rudra Kumar et , “Assessing the Fault Proneness Degree (DFP) of Change Request Artifacts: A Statistical Bipartite Graph Strategy,” Eighth International Conference on Data Mining and Warehousing, 2014.

42. Madapudi Rudra Kumar, A. Ananda Rao, and Gopichand Merugu, “Change requests artifacts to assess the impact on the structural design of SDLC phases.” Int’l J. Computer Applications 54.18 (2012): 21-26.