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Overview: 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 (AI). 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 (NLP) 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. Machine Learning (ML): Machine learning, a core subset of AI, involves algorithms that enable systems to learn from and interpret data without explicit programming. In WHS, ML can be utilized to analyze vast amounts of incident data, workplace audits, and risk assessments. With the recognizing patterns and predicting potential hazards, ML aids in proactive safety management, reducing the likelihood of accidents before they occur.
Genre: Non-Fiction > Tech & Devices
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