Abstract With the rapid development of the internet and digital music, competition among music platforms has become intense. This study focuses on NetEase Cloud Music and proposes a development strategy based on the decision tree algorithm. By collecting user behavior data and constructing decision tree models, the study predicts user preferences and needs, providing personalized recommendations and services on the music platform. Experimental results of this strategy show that... With the rapid development of the internet and digital music, competition among music platforms has become intense. This study focuses on NetEase Cloud Music and proposes a development strategy based on the decision tree algorithm. By collecting user behavior data and constructing decision tree models, the study predicts user preferences and needs, providing personalized recommendations and services on the music platform. Experimental results of this strategy show that it effectively improves user satisfaction and platform profitability. Compared to traditional recommendation algorithms, the decision tree algorithm exhibits better interpretability and accuracy, enabling a deeper understanding of user preferences and behavior patterns, thus providing more precise content recommendations. This innovative development strategy for music platforms helps better meet user demands, achieve sustained growth, and gain competitive advantages. Read More Read Less

Abstract With the growth and progress of the country's economic strength, people's travel levels are continuously improving. Traditional fuel-powered vehicles tend to produce more volatile pollutants during driving, negatively impacting the ecological environment and resource utilization. This paper uses adaptive genetic algorithms to analyze the scale of the new energy vehicle's intelligent charging process. It explores the optimal design approach for... With the growth and progress of the country's economic strength, people's travel levels are continuously improving. Traditional fuel-powered vehicles tend to produce more volatile pollutants during driving, negatively impacting the ecological environment and resource utilization. This paper uses adaptive genetic algorithms to analyze the scale of the new energy vehicle's intelligent charging process. It explores the optimal design approach for intelligent charging stations using this algorithm. Firstly, the computation and modelling process of the adaptive genetic algorithm is analysed to address the issues of difficulty and slowness in charging. In-depth discussions are conducted on power transmission, power management, charging paths, and other aspects using monitoring mathematical models. A filtering algorithm is used to optimize the adaptive genetic algorithm and construct a solution model for intelligent charging control strategies. Finally, the optimal design strategy of car charging stations under intelligent induction is studied. The results show that the adaptive genetic algorithm has good effects in optimizing the intelligent charging strategy of new energy vehicles and the intelligent control design of charging stations. Read More Read Less

Abstract This paper focuses on applying a deep learning- based intelligent video surveillance system, particularly emphasising using the YOLOv7 model for object detection. By reviewing the development of intelligent video surveillance technology, we recognize the importance of deep learning in computer vision. The structure and characteristics of the YOLOv7 model are detailed, including the input layer, backbone network layer, feature fusion layer, and output layer. To validate the model's... This paper focuses on applying a deep learning- based intelligent video surveillance system, particularly emphasising using the YOLOv7 model for object detection. By reviewing the development of intelligent video surveillance technology, we recognize the importance of deep learning in computer vision. The structure and characteristics of the YOLOv7 model are detailed, including the input layer, backbone network layer, feature fusion layer, and output layer. To validate the model's performance, we conducted experiments on the VOC dataset, and the results show that the YOLOv7 model achieved an average detection accuracy of 0.89 on this dataset. The experimental results demonstrate the efficiency, accuracy, and robustness of the YOLOv7 model in intelligent video surveillance, providing essential references for optimizing and applying intelligent video surveillance systems. Read More Read Less

Abstract In this study, we employed data mining to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer. This technique is beneficial not only for our decision-making but also for the long-term operation of our systems. Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts. These findings contribute to a deeper understanding of common cause failures... In this study, we employed data mining to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer. This technique is beneficial not only for our decision-making but also for the long-term operation of our systems. Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts. These findings contribute to a deeper understanding of common cause failures in secure computer systems and prepare for enhancing their security. Establishing a robust information system is essential to meet the increasing demands, especially in the complex internet environment where secure digital computer systems face ever-growing challenges. Read More Read Less