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About

Sharadha Kodadi is an emerging researcher at the intersection of artificial intelligence, medical informatics, and computational optimization. Her work focuses primarily on developing sophisticated machine learning frameworks for healthcare applications, with a particular emphasis on chronic kidney disease (CKD) detection and monitoring through Internet of Medical Things (IoMT) systems. Kodadi's most recognized contribution introduces a probabilistic neuro-fuzzy temporal fusion architecture that integrates AI with robotic automation to address the inherent uncertainty and complexity of clinical medical data, garnering early citation attention in the field. Building on this foundation, her subsequent work advances a dynamic hybrid framework combining GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic, and CNN-TCN architectures, demonstrating her commitment to pushing diagnostic accuracy through multi-modal deep learning approaches. Beyond healthcare AI, Kodadi has also contributed to metaheuristic optimization, specifically refining particle swarm optimization algorithms to overcome persistent challenges like premature convergence in nonlinear, discrete problem spaces. Though early in her research career with a growing citation profile across 2024–2025 publications, her interdisciplinary approach — bridging fuzzy logic, deep learning, and clinical automation — positions her as a promising voice in intelligent medical systems research.