Leema Nelson | Computer Science | Research Excellence Award

Published on by Book of Award

Dr. Leema Nelson | Computer Science | Research Excellence Award

Chitkara University | India 

Dr. Leema Nelson is an accomplished researcher whose scholarly contributions span machine learning, clinical decision support systems, composite materials, signal processing, and intelligent diagnostic frameworks. With a total of 1206 citations, an h-index of 17, and 29 i10-index publications, her research demonstrates both depth and sustained impact across interdisciplinary domains. She has produced numerous high-quality peer-reviewed articles, many in leading Elsevier journals such as Applied Soft Computing and Materials & Design, focusing on neural network optimization, characterization of metal-matrix composites, wear modelling, and advanced computational methods. Her work in clinical data classification, including diabetes and PCOS diagnosis, highlights the integration of artificial intelligence into healthcare decision-making. In recent years, she expanded her research into video smoke detection, cyber-security–based email filtering, audio source separation, and welding parameter optimization using intelligent algorithms. Her studies in deepfake detection, text recognition, and clinical support systems reflect her continuing advancements in data-driven AI models. She has also contributed extensively to IEEE conferences, presenting innovations in masked face detection, ultrasound image analysis, mobile app frameworks, and disease prediction models. Overall, her scientific output reflects strong productivity, interdisciplinary expertise, and meaningful contributions to both computational intelligence and applied engineering research.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Jibinsingh, B. R., & Nelson, L. (2025). FL-WOSP: Federated learning with Walrus Optimization for sepsis prediction using MIMIC-III physiological and clinical data. Pattern Recognition. Advance online publication.

Batra, H., & Nelson, L. (2024). ESD: E-mail spam detection using cybersecurity-driven header analysis and machine learning-based content analysis. International Journal of Performability Engineering, 20(4).

Nelson, L. (2024). Data-driven clinical decision support system using neural network topology optimization for PCOS diagnosis. Journal of Soft Computing and Data Mining.

Batra, H., & Nelson, L. (2024). A three-stage deepfake detection framework using deep learning models with multimedia data. International Journal of Intelligent Systems and Applications.

Shanmuga Priya, M., Pavithra, A., & Leema, N. (2024). Character/word modelling: A two-step framework for text recognition in natural scene images. Computer Science.

Batra, H., & Nelson, L. (2023). DCADS: Data-driven computer aided diagnostic system using machine learning techniques for polycystic ovary syndrome. International Journal of Performability Engineering, 19(3).

Kumar, V. A., Rao, C. V. R., & Leema, N. (2023). Audio source separation by estimating the mixing matrix in underdetermined condition using successive projection and volume minimization. International Journal of Information Technology, 15(4), 1831–1844.

Ramesh, A., Sivapragash, M., Ajith Kumar, K. K., & Leema, N. (2023). Investigating the quality of TIG-welded aluminium alloy 5086 using the online acoustic emission and optimization of welding parameters using global best-based modified artificial bee colony algorithm. Transactions of the Indian Institute of Metals, 1–14.

Pranshu Kumar Soni, & Leema, N. (2023). PCP: Profit-driven churn prediction using machine learning techniques in banking sector. International Journal of Performability Engineering, 19(5), 303–311.

Vettum Perumal, S., Suyamburajan, V., Chidambaranathan, V. S., & Nelson, L. (2023). Characterization of microstructure and mechanical behaviour in activated tungsten inert gas welded dissimilar AA joint of AA 5083 and AA 6061 alloys. Journal of the Institution of Engineers (India): Series D, 1–9.

Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

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Mr. Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

FTD Infocom Pvt Ltd | India

Mr. Patruni Rajshekhar Rao is an avionics research professional whose work integrates test and verification engineering, data analysis, and safety-critical system evaluation across aerospace platforms. His contributions span functional RTL verification, aerospace data analysis, and reliability assessment of embedded systems. His early work involved functional verification of ARINC818 protocol IP cores, where he designed assertion-based test benches using VHDL and file-driven debugging to enhance precision in timing-sensitive validation. He later expanded into flight data analysis for advanced aircraft systems such as the SARAS platform, performing hardware–software integration testing, developing low-level test cases, and analyzing stall-warning system performance. His research also includes pioneering efforts in software health management, where he explored self-healing software systems using AI-driven methods to automate fault detection and recovery in avionics architectures. He has contributed to safety-critical processes aligned with DO-178B and DO-254 standards, including MCDC-level testing for auto-generated code in A-FADEC systems and performing dynamic and static analysis to identify and mitigate software defects. Across conferences and journals, he has published studies on verification methodologies, safety criteria, IP-core validation procedures, and AI-based static analysis, reinforcing his role in advancing dependable avionics engineering.

Profile : Scopus

Featured Publications

Nanda, M., & Rao, P. R. (2018, May 17). Implementation and verification of an asynchronous FIFO under boundary conditions (Paper ID: NCESC18-181). National Conference on Electronics, Signals and Communication (NCESC-2018), GSSS Institute of Engineering & Technology for Women, Mysore.

Nanda, M., Jayanthi, J., & Rao, P. R. (2018, May 18–19). Aerospace compliant test bench to verify critical aerospace functionalities (Paper ID: CRP18-1007). 3rd International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT-2018), Department of Electronics and Communication Engineering, SVCE, Bangalore.

Nanda, M., & Rao, P. R. (2018). An approach for generating self-checking test bench. International Journal for Research in Applied Science and Engineering Technology, 6(6). (Paper ID: IJRASET17914).

Nanda, M., & Rao, P. R. (2018). Aerospace data bus safety criteria as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). A procedure to verify and validate an FPGA level testing as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). Verification cases and procedure for IP-core development. International Journal of Engineering Research and Advanced Technology. (ISSN 2454-6135).