Shilpi Rana

Diabetes mellitus remains a significant global health issue, necessitating early and accurate prediction for effective management and reduced complications. Machine learning (ML) models have been applied to predict diabetes, but achieving high accuracy is challenging due to complex and high-dimensional datasets. Effective feature selection is critical to improving both model accuracy and interpretability. This chapter explores a novel hybrid feature selection approach combining filter, wrapper, and embedded techniques to enhance diabetes prediction models. The selected method optimizes feature selection by first removing irrelevant features (filter), selecting optimal subsets (wrapper), and refining features during model training (embedded). We applied this hybrid approach to a diabetes dataset, utilizing patient records with features like age, BMI, blood pressure, and glucose levels. Models such as logistic regression, random forests, and XG Boost were evaluated using metrics like accuracy, precision, recall, F1-score, and AUC-ROC

AI-enabled cloud computing is significantly transforming the education sector by changing how educational content is delivered, accessed, and managed. This chapter examines the impact of AI-specific cloud computing technologies on education, demonstrating how they are shifting traditional educational approaches toward more interactive, personalized, and efficient systems. It explores various applications, including personalized learning experiences, predictive analytics for student performance, intelligent tutoring systems, and the automation of administrative tasks. By utilizing cloud-based AI tools, schools and universities can enhance learning, boost student success, and improve operational efficiency. The chapter also addresses the challenges of adopting AI and cloud computing in education, such as data privacy concerns, security risks, and issues related to unequal access to technology.

The ever-increasing complexity of antenna design necessitates innovative approaches to streamline the process. Artificial intelligence (AI) and machine learning (ML) offer powerful tools to address this challenge. This chapter explores integrating AI and ML techniques in antenna design and optimization. We delve into the underlying concepts of AI and ML, followed by a comprehensive review of relevant applications. The chapter discusses how AI and ML can be used for antenna parameter prediction, performance optimisation, material selection, and inverse design tasks. We explore the benefits of these techniques, including reduced design time, improved performance, and exploration of novel antenna geometries. Additionally, the chapter addresses potential challenges associated with AI and ML integration, including data quality, interpretability, and the need for human expertise. Finally, we discuss future directions and potential research areas in AI-driven antenna design.

Digital platforms have fundamentally transformed consumer behavior, reshaping traditional business models and fostering new consumption patterns. This chapter explores the evolving relationship between digital platforms and consumers, highlighting the key forces driving this change. Central to the discussion is the shift from ownership-based models to Product-as-a-Service (PaaS), where access and recurring services replace conventional one-time purchases. Digital platforms, such as Netflix, Spotify, and Airbnb, have leveraged data analytics and personalization technologies to predict and influence consumer preferences, promoting greater engagement and long-term loyalty.

This chapter takes an in-depth seem at workplace conflict in the dynamic and safety-critical landscape of the aviation industry. It focuses on the unique challenges facing aviation professionals and addresses the origins and dynamics of conflict by examining the effects of communication breakdowns, hierarchical tensions, and stress-induced scenarios. From cockpit operations to air traffic control, real-world case studies provide insight into the nuances of conflict while offering practical strategies for resolving and mitigating them. The chapter concludes with a call for proactive actions and a culture of open communication to ensure the smooth running of aviation workplaces and advance the industry's commitment to safety and efficiency. Within the pages of this chapter, the chapter embarks on a detailed exploration of the intricacies surrounding workplace conflict within the dynamic and safety-critical domain of the aviation industry.