Mohammad Yazdi – författare

In today''s dynamic workplace environment, ensuring the safety and well-being of employees has never been more critical. This book explores cutting-edge technologies intersecting with workplace safety to deliver effective and practical results.

Artificial Intelligence in Workplace Health and Safety: Data-Driven Technologies, Tools and Techniques offers a comprehensive roadmap for professionals, researchers, and practitioners in work health and safety (WHS), revolutionizing traditional approaches through the integration of data-driven methodologies and artificial intelligence. Covering the foundations and practical applications of data-driven WHS and historical perspectives to current regulatory frameworks, it investigates the key concepts of data collection, management, and integration. Through real-world case studies and examples, readers can discover how AI technologies such as machine learning, computer vision, and natural language processing are reshaping WHS practices, mitigating risks, and optimizing safety measures. The reader will learn applications of AI and data-driven methodologies in their workplace settings to improve safety. With its practical insights, real-world examples, and progressive approach, this title ensures that readers are not just prepared for the future of WHS but empowered to shape it for better.

This text is written for professionals and practitioners seeking to enhance workplace safety through innovative technologies. This extends to safety professionals, HR personnel and engineers across different sectors.

In today''s dynamic workplace environment, ensuring the safety and well-being of employees has never been more critical. This book explores cutting-edge technologies intersecting with workplace safety to deliver effective and practical results.

Artificial Intelligence in Workplace Health and Safety: Data-Driven Technologies, Tools and Techniques offers a comprehensive roadmap for professionals, researchers, and practitioners in work health and safety (WHS), revolutionizing traditional approaches through the integration of data-driven methodologies and artificial intelligence. Covering the foundations and practical applications of data-driven WHS and historical perspectives to current regulatory frameworks, it investigates the key concepts of data collection, management, and integration. Through real-world case studies and examples, readers can discover how AI technologies such as machine learning, computer vision, and natural language processing are reshaping WHS practices, mitigating risks, and optimizing safety measures. The reader will learn applications of AI and data-driven methodologies in their workplace settings to improve safety. With its practical insights, real-world examples, and progressive approach, this title ensures that readers are not just prepared for the future of WHS but empowered to shape it for better.

This text is written for professionals and practitioners seeking to enhance workplace safety through innovative technologies. This extends to safety professionals, HR personnel and engineers across different sectors.

Resilience is a journey that shapes how we navigate challenges and grow through adversity. The “Resilient Mindset: Engineering Strength Through Systems Thinking” draws from the author’s experiences in academic research, industry practice, and personal growth to explore resilience as a practical and transformative human process. By connecting the science of resilience with real-world challenges, this book offers a perspective on how individuals and systems alike can thrive under pressure and uncertainty.

This book delivers tools and insights designed to help individuals thrive in an ever-changing world. Readers will discover academic insights, practical tools, and lived experiences that illuminate resilience as both a mindset and a skill. Each chapter provides actionable strategies to enhance mental clarity, emotional adaptability, and long-term growth, supported by real-world examples and reflective exercises. It features examples of recovering from setbacks, adapting to changes, and sustaining progress in the face of uncertainty, through which it offers a clear and empowering path to building confidence and strength. Readers will learn how to navigate changes to the system and how they can stay resolute as processes and personnel change.

This book is for anyone seeking to grow through life’s challenges, including human factors, professionals, health and safety personnel, human resource employees, and business and management practitioners.

Resilience is a journey that shapes how we navigate challenges and grow through adversity. The “Resilient Mindset: Engineering Strength Through Systems Thinking” draws from the author’s experiences in academic research, industry practice, and personal growth to explore resilience as a practical and transformative human process. By connecting the science of resilience with real-world challenges, this book offers a perspective on how individuals and systems alike can thrive under pressure and uncertainty.

This book delivers tools and insights designed to help individuals thrive in an ever-changing world. Readers will discover academic insights, practical tools, and lived experiences that illuminate resilience as both a mindset and a skill. Each chapter provides actionable strategies to enhance mental clarity, emotional adaptability, and long-term growth, supported by real-world examples and reflective exercises. It features examples of recovering from setbacks, adapting to changes, and sustaining progress in the face of uncertainty, through which it offers a clear and empowering path to building confidence and strength. Readers will learn how to navigate changes to the system and how they can stay resolute as processes and personnel change.

This book is for anyone seeking to grow through life’s challenges, including human factors, professionals, health and safety personnel, human resource employees, and business and management practitioners.

This book reviews and presents a number of approaches to Fuzzy-based system safety and reliability assessment. For each proposed approach, it provides case studies demonstrating their applicability, which will enable readers to implement them into their own risk analysis process.

The book begins by giving a review of using linguistic terms in system safety and reliability analysis methods and their extension by fuzzy sets. It then progresses in a logical fashion, dedicating a chapter to each approach, including the 2-tuple fuzzy-based linguistic term set approach, fuzzy bow-tie analysis, optimizing the allocation of risk control measures using fuzzy MCDM approach, fuzzy sets theory and human reliability, and emergency decision making fuzzy-expert aided disaster management system.

This book will be of interest to professionals and researchers working in the field of system safety and reliability, as well as postgraduate and undergraduate students studying applications offuzzy systems.

This book reviews and presents several approaches to advanced decision-making models for safety and risk assessment. Each introduced model provides case studies indicating a high level of efficiency, robustness, and applicability, which allow readers to utilize them in their understudy risk-based assessment applications.

The book begins by introducing a novel dynamic DEMATEL for improving safety management systems. It then progresses logically, dedicating a chapter to each approach, including advanced FMEA with probabilistic linguistic preference relations, Bayesian Network approach and interval type-2 fuzzy set, advanced TOPSIS with spherical fuzzy set, and advanced BWM with neutrosophic fuzzy set and evidence theory.

This book reviews and presents several advanced approaches to energy infrastructure assets'' intelligent reliability and maintainability. Each introduced model provides case studies indicating high efficiency, robustness, and applicability, allowing readers to utilize them in their understudy intelligent reliability and maintainability of energy infrastructure assets domains.

The book begins by reviewing the state-of-the-art research on the reliability and maintainability of energy infrastructure assets and emphasizes the intelligent tools and methods proposed from a bibliometric and literature review point of view. It then progresses logically, dedicating a chapter to each approach, dynamic Bayesian modeling network, convolutional neural network model, global average pooling-based convolutional Siamese network, an integrated probabilistic model for the failure consequence assessment, and more.

This book interests professionals and researchers working in reliability and maintainability and postgraduate and undergraduate students studying intelligent reliability applications and energy infrastructure assets'' maintainability.

This book is a comprehensive exploration of computational mathematics and its impact on enhancing the reliability and maintainability of industrial systems. With its careful blend of theoretical foundations, practical applications, and future perspectives, this book is a vital reference for researchers, engineers, and professionals seeking to optimize industrial systems'' performance, efficiency, and resilience.