Journal Articles

• Y. Vivek, V. Ravi, and P. R. Krishna, “Quantum-inspired evolutionary algorithms for feature subset selection: A comprehensive survey,” Quantum Information Processing, vol. 24, no. 7, p. 196, Jun. 2025, issn: 1573-1332. doi: 10.1007/s11128-025-04787-6, (Springer, SCIE, Q2, IF: 2.2).
• S. Kumar, Y. Vivek, V. Ravi, and I. Bose, “A comprehensive review of causal inference in banking, finance, and insurance,” ACM Comput. Surv., vol. 57, no. 12, Jul. 2025, issn: 0360-0300. doi: 10.1145/3736752, (ACM, SCIE, Q1, IF: 23.8).
• Y. Vivek, V. Ravi, and P. R. Krishna, “Online feature streaming using feature streams via parallel bare bones particle swarm optimization,” Swarm and Evolutionary Computation, 2025, (Elsevier, Accepted 2025,SCIE, Q1, IF: 8.2).
• Y. Vivek, V. Ravi, and P. Radha Krishna, “Feature subset selection for big data via parallel chaotic binary differential evolution and feature-level elitism,” Computers and Electrical Engineering, vol. 123, p. 110 232, 2025, issn: 0045-7906. doi: https://doi.org/10.1016/j.compeleceng.2025.110232, (Elsevier, SCIE, Q1, IF: 4.0).
• Y. Vivek, V. Ravi, P. N. Suganthan, and P. R. Krishna, “Parallel fractional dominance moeas for feature subset selection in big data,” Swarm and Evolutionary Computation, vol. 91, p. 101 687, 2024, issn: 22106502. doi: https://doi.org/10.1016/j.swevo.2024.101687, (Elsevier, SCIE, Q1, IF: 8.2).
• Y. Vivek, P. S. K. Prasad, V. Madhav, R. Lal, and V. Ravi, “Optimal technical indicator based trading strategies using evolutionary multi objective optimization algorithms,” Computational Economics, Sep. 2024, issn: 1572-9974. doi: 10.1007/s10614-024-10701-6, (Springer, SCIE, ABDC-B, Q2, IF: 1.9).
• A. A. Ram, S. Yadav, Y. Vivek, and V. Ravi, “Deep reinforcement learning for financial forecasting in static and streaming cases,” Journal of Information & Knowledge Management, vol. 23, no. 06, p. 2 450 080, 2024. doi: 10.1142/S0219649224500801, (World Scientific, ESCI, ABDC-C, Q2, IF: 0.9).
• Y. Vivek, V. Ravi, and P. R. Krishna, “Scalable feature subset selection for big data using parallel hybrid evolutionary algorithm based wrapper under apache spark environment,” Cluster Computing, vol. 26, no. 3, pp. 1949–1983, Jun. 2023, issn: 1573-7543. doi: 10.1007/s10586-022-03725-w, (Springer, SCIE, Q1, IF: 4.4).
• V. Sarveswararao, V. Ravi, and Y. Vivek, “Atm cash demand forecasting in an indian bank with chaos and hybrid deep learning networks,” Expert Systems with Applications, vol. 211, p. 118 645, Jan. 2023, issn:0957-4174. doi: https://doi.org/10.1016/j.eswa.2022.118645, (Elsevier, SCIE, ABDC-C, Q1, IF: 8.5).
• H. V. Eduru, Y. Vivek, V. Ravi, and O. S. Shankar, “Parallel and streaming wavelet neural networks for classification and regression under apache spark,” Cluster Computing, 2023. doi: 10.1007/s10586023-04150-3, (Springer, SCIE, Q1, IF: 4.4).

Conference Proceedings

• K. S. N. V. K. Gangadhar, B. A. Kumar, Y. Vivek, and V. Ravi, “Chaotic variational auto encoder based one class classifier for insurance fraud detection,” 2022. arXiv: 2212.07802[cs.LG], (Accepted, ICETCI 2025 conference, Scopus).
• Y. Vivek, V. Ravi, A. A. Mane, and L. R. Naidu, “Atm fraud detection using streaming data analytics,” 2024. arXiv: 2303.04946[cs.LG], (Accepted Analytics Global Conference(AGC) 2025 conference, Scopus).
• P. D. Prasad, Y. Vivek, and V. Ravi, “Fedelf: A privacy preserving federated classification using an ensemble extreme learning machines,” 2023, ( Accepted, Analytics Global Conference(AGC) 2025 conference, Scopus).
• Y. Vivek, V. Ravi, A. Mane, and L. R. Naidu, “Profiling based one class classification for atm fraud detection,” 2024, (Accepted, Communication systems and networks (COMSNETS) 2024, Core/ERA Rank B).
• Y. Vivek, V. Ravi, A. Mane, and L. R. Naidu, “Explainable artificial intelligence and causal inference based atm fraud detection,” in 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), 2024, pp. 1–7. doi: 10.1109/CIFEr62890.2024.10772906, (Core/ERA Rank C).
• P. D. Prasad, Y. Vivek, and V. Ravi, “Fedpnn: One-shot federated classifier to predict credit card fraud and bankruptcy in banks,” in 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), 2024, pp. 1–8. doi: 10.1109/CIFEr62890.2024.10772751, (Core/ERA Rank C).
• V. Yelleti, V. Ravi, and P. R. Krishna, “Novelty detection and feedback based online feature subset selection for data streams via parallel hybrid particle swarm optimization algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, ser. GECCO ’24 Companion, Melbourne,VIC, Australia: Association for Computing Machinery, 2024, pp. 227–230, isbn: 9798400704956. doi: 10.1145/3638530.3654298, (Core/ERA Rank A; Qualis A1).
• V. Yelleti, V. Ravi, and P. Radha Krishna, “Online feature subset selection in streaming features by parallel evolutionary algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, ser. GECCO ’24 Companion, Melbourne, VIC, Australia: Association for Computing Machinery, 2024, pp. 113–114, isbn: 9798400704956. doi: 10.1145/3638530.3664092, (Core/ERA Rank A; Qualis A1).
• C. Priyanka, Y. Vivek, and V. Ravi, “Benchmarking one class classification in fraud detection and other problems in banking financial services and insurance,” 12th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2024 - Presented, Best Paper Award, (Springer, Scopus).
• V. Yelleti and P. S. V. S. S. Prasad, “Mrmr feature selection to handle high dimensional datasets: Vertical partitioning based iterative mapreduce framework,” in Intelligent Systems Design and Applications (ISDA), A. Abraham, A. Bajaj, T. Hanne, P. Siarry, and K. Ma, Eds., Cham: Springer Nature Switzerland, 2024, pp. 78–89, isbn: 978-3-031-64847-2, (Core/ERA Rank C).
• P. D. Prasad, Y. Vivek, and V. Ravi, “Op-fedelm: One-pass privacy-preserving federated classification via evolving clustering method and extreme learning machine hybrid,” in Intelligent Systems Design and Applications (ISDA), A. Abraham, A. Bajaj, T. Hanne, and T.-P. Hong, Eds., Cham: Springer Nature Switzerland, 2024, pp. 45–57, (Core/ERA Rank C).
• V. Yelleti and P. S. V. S. Sai Prasad, “Stateful mapreduce framework for mrmr feature selection using horizontal partitioning,” in Pattern Recognition and Machine Intelligence (Premi), A. Ghosh, I. King, M.Bhattacharyya, S. Sankar Ray, and S. K. Pal, Eds., Cham: Springer International Publishing, 2024, pp. 317– 327, (Springer, Scopus).