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Dr. Shahanaz Ayub is a Professor in the Department of Electronics and Communication Engineering at Bundelkhand Institute of Engineering and Technology, Jhansi, Uttar Pradesh, India. Her research focuses on AI-based behavioral analytics, wearable technologies, machine learning models, and intelligent prediction systems.
The chapter delineates a comprehensive system architecture that integrates wearable devices, edge computing, cloud platforms, and AI analytics for the detection and prediction of stress. Machine learning and deep learning methodologies, encompassing classification models, time-series analysis, and neural networks, are utilised to discern stress patterns and predict prospective burnout dangers. Applications including tailored wellness advice, proactive intervention systems, and organisational decision support are examined. Data privacy challenges, ethical considerations, sensor precision, and user approval are rigorously analysed. Emerging themes include multimodal sensing, explainable AI, and the integration of digital health are also examined. By utilising AI and wearable IoT technology, institutions may actively promote faculty well-being, improve productivity, and cultivate healthier academic environments.
The chapter examines essential methodologies such as classification, clustering, ensemble learning, and deep learning for modelling entrepreneurial behaviour. It also investigates the function of behavioural analytics in identifying psychological traits such as risk propensity, creativity, resilience, and decision-making approaches. A comprehensive framework that integrates data collection, preprocessing, feature engineering, model training, and evaluation is proposed. Applications in education, entrepreneurial ecosystems, and workforce development are emphasised. Moreover, difficulties including data privacy, bias, interpretability, and ethical implications are rigorously examined. Emerging trends, such explainable AI, real-time behavioural analytics, and AI-driven entrepreneurship support systems, are also examined. This chapter establishes a thorough basis for comprehending how machine learning and behavioural data might improve entrepreneurship prediction and development.