Metabolic engineering and deep learning-driven protein engineering for N-Acetylneuraminic acid biosynthesis in Escherichia coli

# 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

Machine learning-driven reconstruction of the climatological geomagnetic diurnal variations at middle to low latitudes

Xuegang Liu

This repository is used to support the research article "Machine learning-driven reconstruction of the climatological geomagnetic diurnal variations at middle to low latitudes: Leveraging Swarm-to-ground observations mapping to bridge unmonitored regions". It contains the code an…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

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

Multitask Bayesian Neural Networks for Multiparameter Protein Engineering

Fabio Herrera

Zenodo Dataset Description This repository contains a curated benchmark collection of 27 multiparameter protein mutational datasets for evaluating machine learning models in protein engineering, with a particular focus on multi-task learning. The benchmark includes both single-po…Read more

Client Recognition Using Text-Dependent Voice Biometrics: A Machine Learning and Deep Learning Approach

Annual Methodological Archive Research Review