Mohammad Yazdi

Gebundenes Buch

Artificial Intelligence in Workplace Health and Safety

Data-Driven Technologies, Tools and Techniques

Beschreibung

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.

Autor

Mohammad Yazdi's illustrious academic and professional journey began with his BSc in Safety and Technical Protection Engineering from the Petroleum University of Technology in Abadan, Iran, conferred in 2012. He further elevated his educational prowess by obtaining an MSc degree in Industrial Engineering from the Eastern Mediterranean University in Famagusta, Cyprus, in 2017. Immediately after, Mohammad integrated his expertise into academia by affiliating with the Centre for Marine Technology and Ocean Engineering (CENTEC) at the University of Lisbon in Portugal. He made significant contributions both in- person during 2017 and 2018 and remotely in 2019. His relentless pursuit of knowledge led him to undertake a dual PhD program in 2022, partnering with the Centre for Risk, Integrity, and Safety Engineering (C- RISE) at the Memorial University of Newfoundland, Canada, and Macquarie University in Australia. Before venturing into these academic achievements, Mohammad dedicated himself to practical applications in the industrial world from 2012 to 2016. He took on multifaceted roles such as a firefighter, safety officer, WHS/ OHS advisor, and auditor, serving vital sectors including power plants, and the oil and gas industry. His research interests and professional background converge at the nexus of system safety, risk assessment, resilience, process integrity, and asset management, especially concerning renewable and non- renewable energy infrastructure. With an impressive track record of leading large- scale energy projects and technology- rich initiatives, Mohammad offers invaluable support to asset operators, developers, and maintainers. Passionately, he endeavors to bridge the gap between industry and academia, crafting innovative solutions that foster advanced decision- making, systems thinking, and creativity. Mohammad champions an asset lifecycle approach from capital investment planning, spanning through operation, maintenance, and culminating in disposal. This holistic perspective allows him to integrate pragmatic thinking, balancing superior business outcomes with optimum functionality and reliability.

Produktdetails

• Intelligent Data-Driven Systems and Artificial Intelligence
• Verlag: CRC Press / Taylor & Francis
• Seitenzahl: 114
• Erscheinungstermin: 30. Oktober 2024
• Englisch
• Abmessung: 234mm x 156mm x 10mm
• Gewicht: 390g
• ISBN-13: 9781032845395
• ISBN-10: 1032845392
• Artikelnr.: 71264903