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S. Gomathi

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Conference Aug 2026

YieldSense-X: Temporal Deep Learning Framework for Crop Yield Prediction Using Climate and Soil Dynamics

Precise yield prediction of crops plays a vital role in food security, proper management of resources and sustainable agriculture. This paper proposes the YieldSense-X, a time-dependent deep learning model that optimally estimates crop yield based on the dynamics of climate and soil. The suggested model combines Long S...

Santhiya S, K. Malarkodi, S. Gomathi · 0 citations
Conference Aug 2026

Hybrid Adaptive Control of Bidirectional Wireless EV Charging Using Deep Learning and Model Predictive Control

The wireless electric vehicle (EV) charging system needs the control in order to ensure the efficiency of power transfer, the bidirectional process, and the nonlinear characteristics of the system behavior under the dynamic conditions. The paper is a proposal of a Hybrid Adaptive Control Framework with some potential t...

S. Anupriya, K. Malarkodi, S. Gomathi · 0 citations
Conference Aug 2026

ChargeRL: Deep Reinforcement Learning Framework for Bidirectional Wireless EV Energy Flow Control

The high pace of electric vehicles (EVs) adoption requires effective and smart energy management mechanisms, especially in bidirectional wireless power transfer (WPT). Conventional rule-based or optimization-based techniques cannot adjust successfully to the changing and stochastic circumstances and result in a subopti...

S. Anupriya, K. Malarkodi, S. Gomathi · 0 citations
Conference Aug 2026

A Comprehensive Review of Electric Vehicle Charging Technologies, Architectures, Power Flow Mechanisms, and Standardization Frameworks

The fast pace of electric vehicle (EV) adoption has increased pressure on effective, consistent, and scalable charging infrastructure. In this paper, I provide a detailed overview of the EV charging system, including charging technologies, station designs, power flow designs, standards, and optimization methods. The mo...

S. Gomathi · 0 citations
Conference Aug 2026

A Novel Two-Stage Residual-Corrected Stacking Framework for Photovoltaic Power Output Prediction

Proper prediction of photovoltaic (PV) power output is essential in ensuring the successful incorporation of solar energy in the smart grid systems and energy management systems. Current single-algorithm models are often ineffective to represent the compound non-linear interactions between meteorological variables, tim...

S. Gomathi · 0 citations

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