An Enhanced Framework for Real-Time Dense Crowd Abnormal Behavior Detection Using YOLOv8

Academic Background With the increasing demand for public safety, especially during large-scale religious events such as the Hajj pilgrimage, abnormal behavior detection in dense crowds has become a critical issue. Existing detection methods often perform poorly under complex conditions such as occlusion, illumination variations, and uniform attire...

A Comprehensive Survey of Loss Functions and Metrics in Deep Learning

Deep Learning, as a crucial branch of artificial intelligence, has achieved significant progress in recent years across various fields such as computer vision and natural language processing. However, the success of deep learning largely depends on the choice of loss functions and performance metrics. Loss functions are used to measure the differen...

Challenges in Detecting Security Threats in WoT: A Systematic Literature Review

With the rapid development of the Internet of Things (IoT) and Web of Things (WoT), security issues have become increasingly prominent. In particular, the frequent occurrence of Denial of Service (DoS) attacks has made the security of WoT systems an urgent problem to be addressed. WoT achieves seamless connectivity between IoT devices and the inter...

Deep Learning-Based Multi-Modal Data Integration Enhancing Breast Cancer Disease-Free Survival Prediction

Breast cancer is one of the most common malignancies among women worldwide. Although early intervention and appropriate treatment have significantly improved patient survival rates, approximately 30% of cases still experience recurrence and distant metastasis, resulting in a 5-year survival rate of less than 23%. Traditional clinical prediction met...

Multi-Modal Interpretable Representation for Non-Coding RNA Classification and Class Annotation

Non-coding RNAs (ncRNAs) play critical roles in cellular processes and disease development. Although genome sequencing projects have revealed a vast number of non-coding genes, the functional classification of ncRNAs remains a complex and challenging issue. The diversity, complexity, and functionality of ncRNAs make them important subjects in biome...

DeepES: Deep Learning-Based Enzyme Screening for Identifying Orphan Enzyme Genes

Academic Background With the rapid advancement of sequencing technology, scientists have been able to obtain a vast amount of protein sequence data, including many enzyme sequences. However, despite the establishment of large enzyme databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BRENDA, sequence information for many enzyme...

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers

Academic Background Cryogenic Electron Microscopy (Cryo-EM) is a crucial experimental technique for determining the structures of macromolecules such as proteins. However, the effectiveness of Cryo-EM is often hindered by noise and missing density values caused by experimental conditions such as low contrast and conformational heterogeneity. Althou...

Sul-BERTGRU: An Ensemble Deep Learning Method Integrating Information Entropy-Enhanced BERT and Directional Multi-GRU for S-Sulfhydration Sites Prediction

Background Introduction Post-Translational Modifications (PTMs) are crucial mechanisms for regulating cellular activities, including gene transcription, DNA repair, and protein interactions. Among these, cysteine, a rare amino acid, participates in various PTMs through its thiol group, playing a significant role in redox balance and signal transduc...

APNet: An Explainable Sparse Deep Learning Model to Discover Differentially Active Drivers of Severe COVID-19

Academic Background The COVID-19 pandemic has had a significant impact on global public health systems. Although the pandemic has somewhat subsided, its complex immunopathological mechanisms, long-term sequelae (such as “long COVID”), and the potential for similar threats in the future continue to drive in-depth research. Severe COVID-19 cases are ...

Deep Learning to Quantify the Pace of Brain Aging in Relation to Neurocognitive Changes

As the global aging problem intensifies, the incidence of neurodegenerative diseases (such as Alzheimer’s Disease, AD) is increasing year by year. Brain aging (Brain Aging, BA) is one of the significant risk factors for neurodegenerative diseases, but it does not completely align with chronological age (Chronological Age, CA). Traditional methods f...