Deep Learning Framework for Mining Process Monitoring Using InSAR

Automated Detection of Self-Harm Wounds Using Deep Learning and Image Processing in Forensic Medicine

A Mohammadi, Mahdi Mehrabi, Seyed Mohammad Saadatneshan, Kamroz Amini, Mahdi Gheysari

Background and Objective: Self-harm is a psychologically damaging behavior, and its accurate differentiation from other wounds (violence, accidents, burns, diabetic ulcers) is critically important in forensic medicine. However, this differentiation often falls into a diagnostic "…Read more

Data used in "Multi-omics integration and batch correction using a modality-agnostic deep learning framework"

Jose Ignacio Alvira Larizgoitia, Gabriele Partel, Jelle Jacobs, Alejandro Sifrim

These are multimodal dataset objects and trained model parameters used in the study. The files are organized in pairs, where each multimodal dataset (.h5mu file) corresponds to a trained model parameter file (.pt) generated using the MIMA (Multimodal Integration with Modality-agn…Read more

Also available via: European Organization for Nuclear Research

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

Low Dose and High Contrast Biomedical Imaging Using SelfSupervised Deep Learning

Xiao Fan Ding, Xiaoman Duan, Ning Zhu

Self-supervised deep learning has emerged as a powerful method for image enhancement when a priori ground-truth references are not available. Stemming from Noise2Noise , it was shown that a convolutional neural network (CNN) can be trained from a noisy input and target pair of th…Read more

Also available via: European Organization for Nuclear Research

Multiscale Interpretable Deep Learning Framework for Identification and Visualization of Deformation Stages in Molecular Dynamics Trajectories

Zhengwu Long, Lingyun You

Microscopic deformation stage recognition from molecular dynamics (MD) trajectories is crucial for understanding the evolution of material damage; however, traditional empirical analysis and black-box single deep learning models lack both high-throughput spatiotemporal modeling a…Read more

# 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