This paper presents a machine learning-based early warning system for detecting and predicting defects in semiconductor devices. This study integrates network research models with advanced machine learning to develop a comprehensive framework for supply chain risk assessment and mitigation. The system can be integrated with multiple data streams, including real-time measurement data, performance measurement equipment, and business indicators, achieved through a combination of combined with Graph Neural Networks (GNN) and Long Short-Term Memory (LSTM) networks. The system achieved 94.3% accuracy in predicting product impact, with an average time of 15.3 days for major events. The research methodology included widespread use across 158 semiconductor manufacturers over 18 months, demonstrating a 64% reduction in impact over time and generating cost estimates of $37.2 million. The hybrid model architecture, combining GNN with LSTM networks, outperformed traditional methods with a precision rate of 0.948 and a return of 0.951. This study contributes to the understanding of supply chain vulnerabilities through the innovative use of network research and machine learning, while developing operational strategies for real-time risk assessment and reductions in semiconductor supply chains.

The rapid aging of the population has increased the demand for quality community aged care systems. This study addresses the critical need for a performance evaluation model that fits the unique challenges of delivery in Southern California. A multifaceted evaluation framework was developed, including key areas such as infrastructure, service processes, outcomes, and management. The model combines quantitative indicators with quality indicators to provide an evaluation of the quality of the service, enabling the identification of inconsistencies and areas for improvement. Application of the model across 25 service providers has shown it to be effective in capturing service changes and driving continuous improvement. The results revealed a positive relationship between better scores and improved customer outcomes, highlighting the important role of proper infrastructure, management practices well, and technology integration. In addition, this study presents detailed guidelines for implementation and policy recommendations to facilitate design, improve resource allocation, and promote collaboration among people who have a stake. These findings contribute to the creation of a sustainable, effective community-based care system capable of addressing the evolving needs of the elderly. This research not only bridges important gaps in the modeling of elderly care but also provides insight for policymakers and practitioners.

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

This paper presents a novel framework for real-time detection of anomalous trading patterns in financial markets using Generative Adversarial Networks (GANs). The proposed system integrates advanced deep learning techniques with specialised temporal attention mechanisms to identify complex market manipulation schemes while maintaining low latency requirements essential for high-frequency trading environments. The framework implements a multi-scale architecture that processes market data streams at multiple time horizons, incorporating market microstructure features and order book dynamics. Experimental evaluation on a comprehensive dataset spanning 24 months of trading data from various markets demonstrates the framework's superior performance, achieving 94.7% detection accuracy with sub-3ms latency. The system processes up to 150,000 transactions per second while maintaining stable performance across market conditions. The framework's adaptive threshold mechanism and hierarchical feature fusion approach significantly reduce false positives during periods of high market volatility. Comparative analysis shows a 15.5% improvement in detection accuracy over traditional methods. The implementation incorporates robust data preprocessing pipelines and efficient computational architectures, enabling practical deployment in production environments. The research contributes to the advancement of financial market surveillance technology by introducing innovative applications of GANs in real-time anomaly detection while addressing critical challenges in processing high-frequency trading data.

This paper proposes a dynamic reinforcement learning framework for detecting suspicious fund flows in multi-layer transaction networks. The framework integrates graph neural networks with adaptive reinforcement learning mechanisms to address the challenges of evolving money laundering patterns in financial transactions. The system architecture implements a novel multi-layer network construction approach that captures both temporal and structural characteristics of transaction patterns. A dynamic feature extraction module employs attention mechanisms and temporal convolution networks to generate comprehensive transaction representations. The reinforcement learning component utilizes a modified Deep Q-Network with prioritized experience replay to optimize detection strategies continuously. Experimental evaluation on a large-scale financial dataset comprising 10 million transactions demonstrates the framework's effectiveness. The proposed approach achieves a detection rate of 92.5% while maintaining a false positive rate below 3.68%, outperforming traditional machine learning methods and recent deep learning approaches. The framework's adaptive strategy optimization enables real-time adjustment of detection policies based on emerging patterns. Ablation studies validate the contribution of individual components, with the graph layer architecture and temporal feature extraction mechanisms showing a significant impact on system performance.

This paper presents a novel approach to improving database anomaly detection efficiency through sample difficulty estimation. Traditional anomaly detection methods often apply uniform computational resources across all data samples regardless of their complexity, resulting in inefficient resource utilization. Our framework addresses this limitation by quantifying the "difficulty" of individual database instances and strategically allocating computational resources where they provide maximum benefit. The proposed model combines isolation scores, density-based metrics, and surprise adequacy measurements to comprehensively assess sample difficulty. Based on these assessments, a difficulty-oriented priority assignment mechanism implemented through a sigmoid mapping function directs intensive computational efforts to challenging cases while processing simpler samples with lighter methods. Experimental evaluation across five diverse datasets demonstrates that our approach achieves a 52.84% reduction in average processing time compared to uniform approaches, while maintaining or improving detection accuracy. The framework achieves the highest Average Percentage of Faults Detected (APFD) score of 0.915, outperforming both traditional and deep learning-based methods. This research provides a foundation for developing intelligent, resource-aware anomaly detection systems capable of handling the increasing scale and complexity of modern database environments.