A Systematic Survey of Hybrid ML Techniques for Predicting Peak Particle Velocity (PPV) in Open-Cast Mine Blasting Operations

Blasting operations in open-cast mines are crucial for mineral extraction but also come with significant environmental and structural risks. The peak particle velocity (PPV) generated during blasting is a key metric for assessing the impact of blasting vibrations on surrounding structures and the environment. Accurate PPV prediction is essential fo...

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...

Gene Selection for Single Cell RNA-seq Data via Fuzzy Rough Iterative Computation Model

Background Introduction Single-cell RNA sequencing (scRNA-seq) technology has been widely applied in biomedical research in recent years, as it can reveal the heterogeneity of gene expression at the single-cell level, providing an important tool for understanding cell types, cell states, and disease mechanisms. However, scRNA-seq data is characteri...

Scalable Multi-Modal Representation Learning Networks

Academic Background In the field of artificial intelligence, Multi-modal Representation Learning (MMRL) is a powerful paradigm aimed at mapping inputs from different modalities into a shared representation space. For example, in social networks, users often share both images and text simultaneously. Through multi-modal representation learning, mode...

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...

AI-Driven Job Scheduling in Cloud Computing: A Comprehensive Review

Academic Background With the rapid development of cloud computing technology, the demand for efficient job scheduling in dynamic and heterogeneous cloud environments has grown significantly. Traditional scheduling algorithms perform well in simple systems but are no longer sufficient for modern, complex cloud infrastructures. Issues such as resourc...