Author

Vinod Kumar Yarlanki

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Conference Aug 2026

A Hybrid Autoencoder with Temporal Fusion Transformer Model for Accurate Dust Prediction in Mining Environments

This study presents a systematic framework for data analysis and dust prediction using Machine Learning (ML) techniques. The Air Experimental Dataset is used to collect input data, which is then preprocessed using advanced missing value handling and data cleaning to increase the quality and dependability of the data. M...

Dasari Appaji, Vinod Kumar Yarlanki, J. M. Durga et al. · International Conference on... · 0 citations

Conference Aug 2026

Multisensory Data-Driven Fault Prediction Model using Conformer-Attention Model

For large-scale industrial machine the advancement of Artificial Intelligence (AI) and sensor technology have transformed predictive maintenance approaches. To overcome these problems, this paper offers an AI-Driven Predictive Maintenance Framework that uses multisensory operational data for early defect detection and...

Vinod Kumar Yarlanki, Vamshi krishna Kona, Manikanta Matam et al. · International Conference on... · 0 citations

Conference Aug 2026

UCS Prediction of Carbonate Rocks using Deep Feedforward Neural Networks with Aquila Algorithm Model

For safe and cost effective design of geotechnical and rock engineering schemes, accurate Uniaxial Compressive Strength (UCS) prediction is crucial, especially in carbonate rock formations with considerable heterogeneity. For enhancing the precision and resilience of UCS prediction, this study proposes a hybrid intelli...

J. M. Durga, Vinod Kumar Yarlanki, Dasari Appaji et al. · International Conference on... · 0 citations