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Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis. The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today. Readership Scholars and professionals interested in machine learning trends and research.

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Internet of things. --- Machine learning. --- Data privacy. --- Information privacy --- Information resources management --- Privacy --- Learning, Machine --- Artificial intelligence --- Machine theory --- IoT (Computer networks) --- Things, Internet of --- Computer networks --- Embedded Internet devices --- Machine-to-machine communications

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This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users' privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. Shows how federated learning utilizes data generated by consumer devices without intruding on privacy, allowing machine learning models to deliver personalized services; Analyzes how federated learning provides a privacy-preserving mechanism to effectively leverage decentralized resources inside end-devices to train machine learning models; Presents case studies that provide a tried and tested approaches to resolution of typical problems in federated learning.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- fuzzy logic --- cybernetica --- datamining --- algoritmen --- KI (kunstmatige intelligentie) --- data acquisition --- AI (artificiële intelligentie)