Abstract This paper explores the application of data mining techniques to enhance the management and evaluation of distance education. It begins by contextualizing China's educational reforms, emphasizing decentralization and institutional autonomy, which have increased the need for data... This paper explores the application of data mining techniques to enhance the management and evaluation of distance education. It begins by contextualizing China's educational reforms, emphasizing decentralization and institutional autonomy, which have increased the need for data driven decision making. The study highlights the challenges of online learning remarkably low supervision, poor instructional quality, and high dropout rates and proposes Educational Data Mining (EDM) as a solution. EDM leverages algorithms like decision trees (especially C4.5), K-means, Apriori, SVM, KNN, and Naive Bayes to analyze student behavior, predict performance, and support timely interventions. Among these, the decision tree algorithm is selected for its interpretability, accuracy, and efficiency in handling diverse data types. The paper details the algorithm's computational framework, including information entropy and gain calculations, and presents an intelligent model for distance education management. Experimental results demonstrate that the optimized C4.5-based model improves both accuracy and processing speed compared to traditional methods. Association rule mining reveals significant behavioral patterns linked to student success, such as homework scores and forum participation. The study concludes that integrating data mining into distance education enables proactive, personalized support and more effective administrative oversight. While traditional assessment persists, algorithmic approaches offer a scalable, equitable pathway to enhance teaching, learning, and institutional management in the era of big data. Further refinement of these models is recommended for broader and more robust application. Read More Read Less

Abstract The rapid expansion of mobile communication services and the intensification of market competition have made customer retention a critical determinant of profitability and sustainability for telecommunications operators. With the proliferation of operational support systems and customer interaction... The rapid expansion of mobile communication services and the intensification of market competition have made customer retention a critical determinant of profitability and sustainability for telecommunications operators. With the proliferation of operational support systems and customer interaction platforms, mobile operators generate massive volumes of heterogeneous data that, if properly analyzed, can yield actionable insights for strategic decision making. This paper presents a comprehensive, systematically revised study of the application of data mining techniques to customer value analysis, segmentation, and retention in the mobile communications sector. Building on customer lifetime value (CLV/LTV) theory and hybrid data mining algorithms, a communication data mining model is developed to support customer relationship management (CRM) and decision support systems. An expectation maximization (EM)-based hybrid algorithm is employed to handle incomplete and mixed type data, enabling robust customer classification and churn related analysis. Experimental results using SAS Enterprise Miner demonstrate satisfactory predictive accuracy and practical relevance. The findings confirm strong associations between customer satisfaction, loyalty, and churn risk, and highlight the importance of identifying high value yet churn prone customer segments. This study contributes to the literature by integrating value theory, probabilistic modeling, and data mining within the specific operational context of mobile communication enterprises, particularly in emerging markets. Read More Read Less

Abstract The document "Music, Artificial Intelligence, and Deep Learning: A Review of Concepts and Genre Classification Approaches" provides a comprehensive overview of AI's transformative role in music information retrieval (MIR), with a focus on music genre classification (MGC).... The document "Music, Artificial Intelligence, and Deep Learning: A Review of Concepts and Genre Classification Approaches" provides a comprehensive overview of AI's transformative role in music information retrieval (MIR), with a focus on music genre classification (MGC). It outlines key AI applications in music including genre classification, recommendation, melody extraction, therapy, and generation and traces the evolution from traditional machine learning (e.g., kNN, SVM) to deep learning models (e.g., CNNs, LSTMs). The review emphasizes that while traditional methods rely on handcrafted audio features like MFCCs, chroma, and rhythm histograms, deep learning enables automatic feature learning from raw or time- frequency representations, yielding significantly higher accuracy (up to 97.8% in recent studies). Tables compare model performance, feature extraction techniques, and trade offs between interpretability and scalability. The paper acknowledges persistent challenges, such as capturing long term harmonic structure and the data hungry nature of deep models. It highlights innovations like Shuai Yu's NHAN-GAF for melody extraction and hybrid architectures (e.g., CNN-LSTM) that integrate temporal and spectral modeling. The proposed AI-based music processing pipeline spanning audio acquisition, preprocessing, feature extraction, and deep modeling illustrates an end to end workflow for modern MIR systems. The review concludes that AI, intense learning, enhances rather than replaces human creativity, and calls for future work on musicaware networks, attention mechanisms, and larger, diverse datasets to improve robustness and interpretability in real world applications. Read More Read Less