Author

R. S. Kumar

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Open access Oct 2026

An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India

Groundwater resources in Chennai, Tamil Nadu, India are under increased stress due to rapid urbanization, erratic monsoon patterns and largely unregulated groundwater extraction. Addressing this challenge requires not only continuous monitoring but also accurate forecasting of groundwater fluctuations. This...

S. Boomika, R. Sitharthan, R. Tanukasree et al. · Frontiers in Artificial Inte... · 0 citations

Open access Aug 2026

Hybrid fuzzy clustering and temporal deep learning framework for multi-parameter forecasting in industrial thermal processes

Blast furnace (BF) operation involves strongly coupled thermal and chemical processes governed by nonlinear heat transfer, gas–solid reactions, and dynamic operational control. Accurate real-time prediction of key thermal and gas parameters is essential for maintaining furnace stability, improving energy efficiency, an...

V. Vignesh, G. V. Narendran, R. S. Kumar et al. · Frontiers in Artificial Inte... · 0 citations

Open access Jul 2026

Hybrid fuzzy C-means and deep learning framework for intelligent fault classification in solar PV systems

A novel hybrid intelligent classification system for PV fault detection is proposed by integrating Fuzzy C-Means (FCM) clustering and Deep Learning (DL) techniques such as Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU).

V. Vignesh, R. S. Kumar, G. Suganeshwari · Frontiers in Artificial Inte... · 0 citations