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About

Naga Sushma Allur is an emerging researcher working at the intersection of artificial intelligence, medical diagnostics, and intelligent optimization systems. Her work focuses primarily on applying advanced machine learning and neuro-fuzzy frameworks to critical healthcare challenges, with a particular emphasis on Chronic Kidney Disease (CKD) detection and monitoring through Internet of Medical Things (IoMT) platforms. Allur's most notable contributions include the development of a Probabilistic Neuro-Fuzzy TemporalFusionNet architecture that integrates AI with robotic automation for CKD diagnostics, addressing longstanding limitations in handling uncertain and incomplete medical data. Building on this foundation, she further advanced the field with a sophisticated hybrid framework combining GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic, and CNN-TCN architectures — enabling more accurate temporal pattern recognition in continuously streamed patient data. Her research has garnered early citation traction, reflecting growing interest in her methodologies within the biomedical AI community. Beyond healthcare, Allur has also contributed to optimization theory, proposing enhancements to Particle Swarm Optimization algorithms to overcome premature convergence in nonlinear, discrete problem spaces. Though early in her career, her interdisciplinary approach bridging intelligent systems, clinical applications, and computational optimization positions her as a promising voice in next-generation AI-driven healthcare research.