Applied Machine Learning for Iot and Data Analytics (13 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware - Artificial intelligence and augmented reality are transforming precision surgery by improving visualization, surgical planning, and intraoperative decision-making. Together, these technologies enable more accurate, personalized, and data-driven procedures while supporting improved patient outcomes and greater efficiency across a range of surgical specialties. AI-Enhanced Augmented Reality in Precision Surgery provides a comprehensive overview of the integration of AI and AR in contemporary surgical practice. The book examines AI-powered medical imaging, surgical planning, real-time augmented reality guidance, robotics-assisted interventions, and adaptive AI-AR systems for personalized care. It also explores ethical considerations, cybersecurity, regulatory frameworks, and emerging trends, supported by practical case studies that demonstrate clinical applications and future directions in precision surgery.>Key Features: -Comprehensive Technical Insights: Detailed breakdowns of deep learning architectures (e.g., U-Net, CNNs) and AR hardware (e.g., Microsoft HoloLens) used in clinical settings.-Practical Application: Extensive real-world case studies across medical specialties, including neurosurgery, orthopaedics, and thoracic surgery.-Holistic Approach: In-depth discussions on critical non-technical barriers, such as data privacy, algorithmic bias, and global regulatory compliance.…

Condition: New.

Paperback. Condition: new. Paperback. Applied Machine Learning for IoT and Data Analytics (Volume 3) offers a comprehensive exploration of how machine learning transforms behavioural data into actionable intelligence. In an era where data-driven strategies shape competitive advantage, this volume examines how organisations can harness predictive analytics to understand patterns, anticipate risks, and unlock hidden opportunities.The book introduces the foundational principles of behavioural analytics and advances into practical machine learning applications across diverse domains. It addresses critical integration challenges such as data quality, model reliability, privacy protection, and ethical considerations-highlighting transparency and responsible data governance as essential pillars of modern analytics frameworks. Through empirical research and real-world case studies, the volume demonstrates how predictive insights can enhance employee engagement, improve customer experiences, optimise marketing performance, and support public safety initiatives. Bridging theory with applied implementation, the book equips readers with both conceptual clarity and practical strategies for deploying machine learning-driven behavioural intelligence in dynamic organisational environments. Key Features: -Comprehensive overview of behavioural analytics and predictive modelling foundations.-Application of machine learning techniques with real-world perspectives on implementation and management.-Insights into improving employee retention, customer engagement, and operational efficiency.-Discussion of integration challenges, including data quality and governance frameworks, with a focus on ethical AI, transparency, data privacy, and responsible analytics practices This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Applied Machine Learning for IoT and Data Analytics (Volume 1) is an integrated exploration of nature-inspired optimisation techniques within the emerging Industry 5.0 paradigm- Positioned at the intersection of artificial intelligence, computational intelligence, industrial engineering, and cyber-physical systems, this volume centres on human-centricity, sustainability, resilience, and intelligent automation. The book comprehensively reviews evolutionary computation, swarm intelligence, neural computation, and hybrid metaheuristics, explaining how these methods can be systematically designed, statistically validated, and benchmarked for real-world deployment. Foundational chapters address Explainable AI (XAI), statistical experimental design, ANOVA-based modelling, parameter tuning strategies, and performance evaluation frameworks. Through fifteen carefully curated chapters, the book presents practical case studies in wireless sensor networks, smart manufacturing, micro-machining, welding optimisation, renewable energy systems, motor control, wireless communications, banking automation, and advanced antenna design. Emphasis is placed on experimental rigour, benchmarking, and reproducibility-bridging the gap between theoretical advancements and industrial implementation. Key Features: -Comprehensive review of classical and hybrid bio-inspired algorithms.-Integration of optimisation techniques within the Industry 5.0 framework.-Covers Explainable AI for transparent optimisation systems with a strong focus on experimental design, ANOVA modelling, and statistical validation.-Practical case studies across manufacturing, energy, communications, and automation.-Emphasis on reproducibility and methodological rigour with forward-looking insights into AI-enhanced and explainable optimisation trends. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Applied Machine Learning for IoT and Data Analytics (Volume 3) offers a comprehensive exploration of how machine learning transforms behavioural data into actionable intelligence. In an era where data-driven strategies shape competitive advantage, this volume examines how organisations can harness predictive analytics to understand patterns, anticipate risks, and unlock hidden opportunities.The book introduces the foundational principles of behavioural analytics and advances into practical machine learning applications across diverse domains. It addresses critical integration challenges such as data quality, model reliability, privacy protection, and ethical considerations-highlighting transparency and responsible data governance as essential pillars of modern analytics frameworks. Through empirical research and real-world case studies, the volume demonstrates how predictive insights can enhance employee engagement, improve customer experiences, optimise marketing performance, and support public safety initiatives. Bridging theory with applied implementation, the book equips readers with both conceptual clarity and practical strategies for deploying machine learning-driven behavioural intelligence in dynamic organisational environments. Key Features: -Comprehensive overview of behavioural analytics and predictive modelling foundations.-Application of machine learning techniques with real-world perspectives on implementation and management.-Insights into improving employee retention, customer engagement, and operational efficiency.-Discussion of integration challenges, including data quality and governance frameworks, with a focus on ethical AI, transparency, data privacy, and responsible analytics practices This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Applied Machine Learning for IoT and Data Analytics (Volume 1) is an integrated exploration of nature-inspired optimisation techniques within the emerging Industry 5.0 paradigm- Positioned at the intersection of artificial intelligence, computational intelligence, industrial engineering, and cyber-physical systems, this volume centres on human-centricity, sustainability, resilience, and intelligent automation. The book comprehensively reviews evolutionary computation, swarm intelligence, neural computation, and hybrid metaheuristics, explaining how these methods can be systematically designed, statistically validated, and benchmarked for real-world deployment. Foundational chapters address Explainable AI (XAI), statistical experimental design, ANOVA-based modelling, parameter tuning strategies, and performance evaluation frameworks. Through fifteen carefully curated chapters, the book presents practical case studies in wireless sensor networks, smart manufacturing, micro-machining, welding optimisation, renewable energy systems, motor control, wireless communications, banking automation, and advanced antenna design. Emphasis is placed on experimental rigour, benchmarking, and reproducibility-bridging the gap between theoretical advancements and industrial implementation. Key Features: -Comprehensive review of classical and hybrid bio-inspired algorithms.-Integration of optimisation techniques within the Industry 5.0 framework.-Covers Explainable AI for transparent optimisation systems with a strong focus on experimental design, ANOVA modelling, and statistical validation.-Practical case studies across manufacturing, energy, communications, and automation.-Emphasis on reproducibility and methodological rigour with forward-looking insights into AI-enhanced and explainable optimisation trends. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…