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Sureshkumar Pemmada

Sureshkumar Pemmada

Assistant Professor

Department of Computer Science Engineering

GITAM School of Technology

Visakhapatnam

Education

Ph. D.

Dr. Suresh Kumar Pemmada, Asst. Prof. at GITAM (Deemed to be University), VSKP, holds an M.Tech in CSE from JNTUK and a Ph.D. from VSSUT. With over a decade of academic experience, his expertise encompasses ML, DL, and Optimization. He is a prolific author, with contributions to esteemed journals and conferences by Elsevier, Springer, and IEEE. Dr. Pemmada also plays an active role as a reviewer for prestigious publications and conferences, including ICCIDM, CIPR, etc..

Research Publications

  • Suresh Kumar Pemmada, H. S. Behera, Anisha Kumari. K, J. Nayak, and B. Naik, "Advancement from neural networks to deep learning in software effort estimation : Perspective of two decades," Computer Science Review, vol. 38, pp. 100288, 2020, doi: 10.1016/j.cosrev.2020.100288. [Impact Factor: 12.9] [Indexing: SCIE, SCOPUS] [ELSEVIER] [h-Index: 60]
  • Suresh Kumar Pemmada, J. Nayak, and B.naik, " A deep intelligent framework for software risk prediction using improved firefly optimization, "Neural Computing and Applications (2023), doi: 10.1007/s00521-023-08756-x. [Indexing: SCIE, SCOPUS] [Impact Factor: 5.102] [h-Index: 111]
  • Suresh Kumar Pemmada, H. S. Behera, J. Nayak, and B. Naik, "Correlation-based modified long short-term memory network approach for software defect prediction," Evolving Systems, Feb. 2022, doi: 10.1007/s12530-022-09423-7. [Indexing: SCIE, SCOPUS] [SPRINGER] [Impact Factor: 1.9] [h-Index: 31]
  • Suresh Kumar Pemmada, H. S. Behera, J. Nayak, and B. Naik, "Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature," Innovations in Systems and Software Engineering, vol 17, pp. 1–22, May 2021, doi: 10.1007/s11334-021-00399-2. [Indexing: ESCI, SCOPUS] [SPRINGER] [Impact Factor: 1.2] [h-Index:30]
  • Suresh Kumar Pemmada, H. S. Behera, J. Nayak, and B. Naik, "A pragmatic ensemble learning approach for effective software effort estimation," Innovations in Systems and Software Engineering, Jan. 2021, doi: 10.1007/s11334-020-00379-y. [Indexing: ESCI, SCOPUS] [SPRINGER] [Impact Factor: 1.2] [h-Index:30]

Ongoing Research Projects

  • Check Icon 1. Title of the Project: A Photoplethysmography-based Mental Workload Evaluation Using Deep Convolutional Recurrent Neural Network. Sanctioning Authority: Research Seed Grants (RSG) by Gandhi Institute of Technology and Management (GITAM) Deemed to be University.

Expertise

  • Check Icon Machine Learning, Neural Networks, Deep Learning
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