Exploring Psychology Social Innovation And Advanced Applications Of Machine Learning
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Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning
Machine learning (ML algorithms can be used to better understand human behavior in its various developmental stages and to assist in addressing psychological issues. Additionally, in the realm of mental health and well-being, algorithms can assist with early detection of disorders and customization of treatments as well as personalize recommendations and suggestions based on user behavior. By focusing on user experience and usability, ML may be used to address challenges faced by private enterprises and social issues. Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning explores the relationships between human psychology and machine learning technology, enabling researchers to delve into areas such as user interface design, ethics in artificial intelligence, and the social impact of algorithms. Furthermore, it promotes interdisciplinary collaboration by bringing together perspectives from different fields, which could stimulate new research and innovative approaches in the field of machine learning. Covering topics such as industrial processes, digital therapy, and machine vision, this book is an excellent resource for psychologists, computer scientists, engineers, healthcare practitioners, educators, business leaders, policymakers, professionals, researchers, scholars, academicians, and more.
Artificial Intelligence and Machine Learning for Enhancing Resilience: Concepts, Applications, and Future Directions
As contemporary societies face unprecedented challenges such as mounting mental health issues, environmental crises, and socioeconomic insecurity, the urgency of developing objective, scalable, and dynamic methodologies to study resilience has never been greater. This book arises at the intersection of cutting-edge technology and human insight. It focuses on the possibility for AI and ML to transform resilience assessment, prediction, and interventions across the individual, organizational, and ecological levels. The chapters included in this book represent an organized synthesis of cutting-edge science, pragmatic applications, and prospective potential. With machine learning algorithms to estimate psychological resilience and AI-based models for climate change adaptation and ecosystem management, this book demonstrates the rich innovations that are emerging at the cross-sector of technology and resilience science. Perhaps most importantly, this book does not gloss over the urgent ethical, technical, and regulatory issues that arise when AI is introduced to sensitive topics such as mental health and environmental management. Questions about data privacy, algorithmic bias, model interpretability, and equitable technology deployment are thoroughly investigated, providing lessons learned and suggestions for moving ahead. A significant strength of this work is its global focus. Showcasing work from contributors of various methodologies and regions provides the latest views on new methodologies, strategies for practical implementation, and on what still needs to be invented. This guarantees that the publication engages with the messy socio-cultural and environmental contexts in which these interventions work and that it doesn’t just mirror technological possibilities. For academicians, practitioners, technologists, and policymakers, this book is both a fundamental reference and an outlook resource. It provides: Holistic examination of AI and ML in the context of psychological, organizational, and ecological resilience. In-depth reviews on methodological innovations, such as deep learning, natural language processing, and sensor-based assessments. Unprecedented appraisals of barriers to implementation, with ethical and regulatory considerations. We trust that this book will inspire conversation, fuel innovation, and support a future in which technology supplements, rather than replaces, human ability to adapt, recover, and flourish. We encourage readers to critique the content, to reflect on how AI, ML, and resilience intersect in their particular contexts, and to join us in shaping a future where technological and human resilience evolve together.
Transformative Lean Six Sigma Techniques for the Quality 5.0 Paradigm
Author: de Sá, José Carlos Vieira
language: en
Publisher: IGI Global
Release Date: 2025-07-30
In the shift from Industry 4.0 to Industry 5.0, where human-machine collaboration, personalization, and sustainability are prioritized, Lean Six Sigma (LSS) plays a critical role in shaping efficient and adaptive production systems. By integrating emerging technologies like blockchain, internet of things (IoT), artificial intelligence (AI), and autonomous robots, LSS evolves to meet the complex demands of modern industries. This transformation not only enhances operational excellence but also supports smarter decision-making, greater transparency, and reduced waste. As a result, sectors are seeking improvements in customization, worker empowerment, and overall system resilience. The impact is a more sustainable and human-centric industrial future. Transformative Lean Six Sigma Techniques for the Quality 5.0 Paradigm explores the integration of LSS with the emerging framework of Industry 5.0. It additionally emphasizes the importance of adopting sustainable and human-centric approaches within the LSS framework. Covering topics such as autonomous robots, ethical leadership, and sustainability, this book is an excellent resource for industry professionals, process improvement specialists, quality managers, operations leaders, researchers, academicians, and more.