Browsing by Author "Vengatesh T"

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    Explainable Ai for Environmental Decision Support: Interpreting Deep Learning Models In Climate Science
    (2025-12-30) Vengatesh T; Kishor Barasu Bhangale; Ronicca M.
    Deep learning (DL) models have demonstrated high accuracy in climate science applications but suffer from "blackbox" opacity, hindering their adoption in environmental decision-making. This research bridges this gap by integrating Explainable AI (XAI) techniques with DL models to enhance transparency in climate predictions. Using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, we forecast regional temperature anomalies and interpret outputs via SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model- agnostic Explanations). Our methodology is validated on ERA5 reanalysis data (1980–2025), achieving a prediction RMSE of 0.86°C. XAI analysis reveals that oceanic heat fluxes and atmospheric pressure patterns are critical drivers of anomalies. The framework empowers policymakers with actionable insights, ensuring DL models are both accurate and trustworthy for climate action.
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    Interpretable Recurrent Neural Networks for Modeling Temporal Dynamics of Acquired Drug Resistance in Targeted Therapy
    (2026-05-26) Pushpalatha V.; Thanvi Kuttaiah; Kamaleshwar T.; Anat Jaslin Jini A.; Jakkapu Nagalakshmidevi; Susmitha V.; Sathyaraju S.; Jayasudha R.; Deepa A.R; Vengatesh T
    Acquired drug resistance remains a fundamental obstacle in targeted cancer therapy, often arising from complex temporal evolutionary dynamics within tumor cell populations. Traditional pharmacokinetic-pharmacodynamic (PKPD) models struggle to capture nonlinear, long-term sequential dependencies, while standard recurrent neural networks (RNNs) suffer from black-box opacity. In this paper, we propose IRNN-DR (Interpretable RNN for Drug Resistance), a novel architecture combining Long Short-Term Memory (LSTM) units with an attention-based temporal relevance mask and a learnable dynamical system backbone. Using longitudinal single- cell resistance data from EGFR-mutant non-small cell lung cancer (NSCLC) cell lines treated with osimertinib, we demonstrate that IRNN-DR achieves high predictive accuracy (AUC-ROC = 0.94) while providing explicit interpretations of resistance-driving time points and genetic pathways. Our contribution includes a temporal Shapley value module and a resistance trajectory clustering method. Results indicate that IRNN-DR can identify early "critical windows" of resistance emergence, with potential clinical utility for adaptive therapy scheduling