Fadi Al Machot – författare
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3 produkter
3 produkter
Recent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support
Inbunden, Engelska, 2024
2 192 kr
Skickas inom 10-15 vardagar
This book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems.
Recent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support
Häftad, Engelska, 2025
2 192 kr
Skickas inom 10-15 vardagar
This book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. It covers state-of-the-art machine learning techniques and large language models using multimodal sensors. Enhancing the quality of life for individuals with special needs, particularly the elderly, is a key focus in Active and Assisted Living (AAL) research. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems. This book offers insights into technologies transforming AAL for researchers, students, and practitioners. It is a valuable resource for developing responsive and personalized support systems that enhance life quality in smart environments. It is also essential for advancing the understanding of machine learning and sensor technologies in AAL and emotion recognition.
Del 1375 - Lecture Notes in Networks and Systems
Designing the Conceptual Landscape for a XAIR Validation Infrastructure
Proceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany
Häftad, Engelska, 2025
1 755 kr
Skickas inom 10-15 vardagar
This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.