DeePath: Deep learning for pathogenicity prediction from next generation sequencing

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…Read more

A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction

Sunanda Budihal, Sheetalrani Kawale, Abhishek Angadi

The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…Read more

Also available via: European Organization for Nuclear Research

ML Precipitation Prediction: Hybrid Deep Learning for Spatiotemporal Forecasting in Mountainous Areas

Manuel Ricardo Perez Reyes

A hybrid deep learning framework for monthly precipitation prediction in mountainous areas of Boyacá, Colombia. Combines Graph Neural Networks (GNN) with temporal attention mechanisms and ConvLSTM architectures for accurate spatiotemporal forecasting. This implementation includes…Read more

Evaluation of the Implementation of the Deep Learning Approach in Learning in the Subject of PJOK in Public Junior High Schools in Godean District

Andi Raafa Firmansyach, Ngatman

This study aims to evaluate the implementation of the deep learning approach in Physical Education, Sports, and Health (PJOK) learning in public junior high schools in Godean District, based on the Countenance Stake Evaluation Model, which includes antecedents, transactions, and…Read more

Evaluating Deep Learning Models for Streamflow Prediction Under RCP 4.5 and RCP 8.5 Climate Scenarios

Jamshid Piri, Hatice Citakoglu

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…Read more