PREFACE
The current digital transformation of modern societies is being changed by artificial intelligence (AI). Once considered a futuristic concept, AI is now present in numerous aspects of everyone’s daily lives, from recommendation systems and healthcare analytics to industrial automation and business decision-making. AI technologies are growing in sophistication and accessibility, which expands their potential for innovation and societal impact. This book, entitled Applied Artificial Intelligence: Insights, Innovations, and Sectoral Challenges, brings together recent and innovative research on the practical applications of AI across diverse domains. It reflects the convergence of theoretical developments and real-world implementations in the industry, showing how AI is no longer confined to fundamental research, but is now actively improving industries and redefining user experiences. Chapter 1 begins with a foundational overview of contrastive learning techniques in healthcare. With the increasing availability of unlabeled medical data, contrastive learning provides a powerful method to train robust models using limited annotations, offering promising paths forward in diagnostics, genomics, and personalized health monitoring. This opening chapter synthesizes recent progress and discusses future ethical and technical challenges, including explainability and scalability. Chapter 2 introduces a cascade hybrid approach that combines collaborative filtering and text embedding techniques to solve the “cold start” problem in job recommendation platforms. This chapter demonstrates how combining classical and modern AI methods can lead to substantial improvements in matching accuracy, thus highlighting the value of AI in addressing real-world data limitations. Chapter 3 builds upon the subject of user-centered AI by examining how data mining techniques can extract meaningful suggestions from large volumes of user reviews. Focusing on the gaming industry, the authors illustrate how small and medium-sized development teams can apply AI to identify and act upon the most valuable user feedback. This work highlights the potential of AI to empower innovation even in resource-constrained environments. Broadening the scope, Chapter 4 explores the macro-level effects of AI adoption across sectors. The authors analyze how AI contributes to business model transformation, labor market shifts, and organizational restructuring.
While offering efficiency and personalization, AI-driven processes also raise questions about skill displacement and socio-economic transition. This chapter provides a balanced perspective on the benefits and limitations of AI integration. The final chapter diverges from conventional AI applications, presenting a systematic Quality Function Deployment (QFD)-based
methodology for the selection of grippers in industrial robotics. While not explicitly AI-driven, the approach exemplifies how intelligent decision support frameworks can optimize industrial processes. Its inclusion in this volume provides the transition between AI and broader automation systems, enriching the interdisciplinary value of the book. This collection reflects the collaborative efforts of researchers who are advancing the knowledge on applied AI. It is intended for an audience of academics, engineers, data scientists, and decision-makers willing to
understand how AI methodologies translate into tangible innovations. Whether the reader is interested in algorithmic foundations, domain-specific applications, or the societal implications of AI, this book offers valuable guidelines. The editor hope that this volume contributes meaningfully to the ongoing dialogue around AI deployment and serves as a means for further research and responsible innovation.
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