With ongoing advancements in natural language processing (NLP) and deep learning methods, the demand for computational and memory resources has considerably increased, which signifies the determination of efficient and compact models in resource-constrained environments. A comprehensive overview of the most recent advancements in pruning and quantization methods for deep neural networks is provided in this paper. Numerous cutting-edge techniques that harness the complementary advantages of pruning and quantization have been analyzed, highlighting their effectiveness in reducing model size, enhancing computational efficiency, and minimizing memory usage. These techniques include Quantization and Sparsity Aware Fine Tuning, Compression Learning by In-Parallel Pruning-Quantization, GroupReduce, Quantization-Pruned Attention, Structured Pruning, Normalized Linear Quantization (Prune and NLQ), Quantization and Pruning for Sentiment Analysis, an Automatic mixed-precision Quantization approach for BERT compression (AQ-BERT), Mixed Precision Quantization, Unstructured Pruning, and Quantization, and Magnitude Pruning. The datasets utilized, models employed, and outcomes achieved are taken into account within this research. The utilization of pruning and quantization techniques across diverse deep-learning tasks, NLP, and sentiment analysis are also discussed. Moreover, issues such as compatibility with hardware configurations, optimization complexities, accuracy degradation, and other constraints have been analysed. Several challenges and limitations of weight or unit pruning that are utilized for optimizing memory and quantization techniques to enhance precision are explored. The in-depth analysis of these state-of-the-art techniques and experiments provides a broad understanding. Furthermore, strategies to effectively reduce the computational and memory demands of neural networks without compromising their performance are also analysed.

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain.

Protecting AI in web applications is necessary. This domain is a composite of technology and huge scope with good prospects and immense difficulties. This chapter covers the landscape of security issues with advancing generative AI techniques for integration into web development frameworks. The initial section is on security in web development—a conversation on the subtleties of generative AI-based methods. In a literal stance, the chapter offers 13 ways to approach it. Among the threats are those that introduce security issues related to generative AI deployments, which illustrate why it is vital for defenders and infrastructure owners to implement mitigation measures proactively. This chapter pertains to the security and privacy of data and lessons for securing and preventing vulnerability. The chapter explores attacks, model poisoning, bias issues, defence mechanisms, and long-term mitigation strategies. Additionally, Service A promotes transparency, explainability, and compliance with applicable laws while structuring a development methodology and deployment methods/operation. The text outlines how to respond and recover from incidents as it provides response frameworks for everyone involved in managing security breaches. Finally, it addresses trends, possible threats, and lessons learned from real-world case studies. In order to contribute to addressing these research needs, this chapter sheds light on the security considerations associated with AI for web development and suggests recommendations that can help researchers, practitioners, and policymakers enhance the security posture of popular generative AI advancements used in generating web applications.

Student dropout remains a critical challenge for higher education institutions, with significant implications for resource allocation, academic planning, and institutional sustainability. This study applies machine learning techniques to predict student non-continuation and attrition to support data-driven retention strategies in higher education. By framing the problem as a multi-class classification task (Dropout, Enrolled, Graduate), the proposed framework enables early and differentiated intervention planning. Using a publicly available higher education student dataset (4424 records, 34 features, multi-class outcome), a structured analytical pipeline was implemented, incorporating Winsorisation for outlier mitigation, SMOTE for class imbalance handling, and targeted feature engineering. Model performance was assessed using a 5-fold nested cross-validation framework. Four classifiers, Extra Trees, Random Forest, Gradient Boosting, and Logistic Regression, were trained on an optimised subset of 28 features. Among these, the Extra Trees model achieved the strongest performance, attaining a mean AUC of 0.96 (±0.0053) and an accuracy of 87.4% (±0.012). Model interpretability was enhanced through SHAP analysis, which identified cumulative approved academic units and tuition fee payment status as the most influential predictors of student outcomes. The findings underscore the value of early predictive analytics for informing proactive institutional interventions, particularly in academic monitoring and financial support to strengthen student retention frameworks.