Comparative Analysis of Methodologies and Approaches in Recommender Systems Utilizing Large Language Models

Academic Background With the explosive growth of internet information, recommender systems (RSs) have become indispensable in modern digital life. Whether it’s movie recommendations on Netflix or personalized news feeds on social media, recommender systems are reshaping users’ online experiences. However, traditional recommender systems face numero...

Enhancing Decentralized Energy Storage Investments with Artificial Intelligence-Driven Decision Models

Academic Background As the global energy structure transitions towards renewable energy, the importance of decentralized energy storage is becoming increasingly prominent. Unlike traditional centralized energy storage systems, decentralized energy storage localizes the energy production and storage processes, reducing the risk of large-scale system...

Power Aggregation Operators Based on Aczel-Alsina T-Norm and T-Conorm for Intuitionistic Hesitant Fuzzy Information and Their Application to Logistics Service Provider Selection

Academic Background In modern supply chain management, the selection of logistics service providers is a complex and critical issue. Enterprises need to evaluate and choose third-party organizations capable of efficiently managing and executing logistics tasks. However, the decision-making process in reality often involves significant uncertainty a...

Dombi Weighted Geometric Aggregation Operators on the Class of Trapezoidal-Valued Intuitionistic Fuzzy Numbers and Their Applications to Multi-Attribute Group Decision-Making

Academic Background In modern engineering and management fields, decision-making problems are often accompanied by uncertainty and ambiguity. Traditional fuzzy set theory has certain limitations when dealing with these issues, especially in complex Multi-Attribute Group Decision-Making (MAGDM) problems. Intuitionistic Fuzzy Set (IFS), as an extende...

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...

Dual Representation Learning for One-Step Clustering of Multi-View Data

In real-world applications, multi-view data is widely available. Multi-view data refers to data collected from multiple sources or through multiple representations, such as different language versions of the same news story or disease data obtained through different medical tests. Multi-view learning is an effective method for mining multi-view dat...

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...