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About

Durga Praveen Deevi is an emerging researcher working at the intersection of artificial intelligence, medical informatics, and intelligent optimization systems. His work primarily focuses on developing advanced computational frameworks for healthcare automation, with a particular emphasis on chronic kidney disease (CKD) detection and prediction using cutting-edge AI architectures. Deevi has made notable contributions by integrating probabilistic neuro-fuzzy systems, deep learning models, and Internet of Medical Things (IoMT) technologies to address longstanding challenges in medical diagnostics, including data uncertainty and temporal dependency modeling. His 2024 paper introducing the AI-Integrated Probabilistic Neuro-Fuzzy TemporalFusionNet garnered 3 citations shortly after publication, while his follow-up work employing hybrid GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic, and CNN-TCN architectures for IoMT-based CKD detection has already attracted early scholarly attention. Beyond medical applications, Deevi has also contributed to evolutionary computation, publishing research on enhancing Particle Swarm Optimization algorithms to overcome premature convergence in complex, nonlinear problem spaces. Though early in his career, his interdisciplinary approach bridging robotic automation, fuzzy logic, and clinical diagnostics positions him as a promising voice in AI-driven healthcare research.