Resources for: Rapid customization of base editors via machine learning-powered combinatorial mutagenesis (MLDE/ProteinMPNN workflow on TadA)

# 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

Intelligent Classification of Respiratory Diseases Using Machine Learning-Based Lung Sound Analysis

Tara V K, Varsha S

Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, bronchiectasis, bronchiolitis and upper respiratory tract infection (URTI) remain among the leading causes of illness and death worldwide. Conventional diagnosis relies heavily on auscul…Read more

PhishNet: A Cost-Sensitive Stacked-Ensemble Approach to Machine Learning-Based Phishing Website Detection

Jahnavi Somaraju, Dhanalakshmi G, Rakshitha V, Kavitha G, Rajani A

Real-world phishing traffic is heavily imbalanced — legitimate URLs vastly outnumber phishing ones in any live traffic stream — and the two error types carry different costs: a missed phishing site (false negative) can lead directly to credential theft, while a legitimate site wr…Read more

Dataset for User Acceptance Evaluation of Machine Learning-Based Investment Analysis Website Using UTAUT

I Gusti Arya Dwipa Aryawangsa, Ni Luh Putu Ayu Diantini, Yuniarty

RAQA-AutoML: An Integrated Solution for Automated Machine Learning

Abdullah Kaviani Rad

Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…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