Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

This book consists of a collection of the high-quality research articles in the field of computer vision and robotics which are presented in the International Conference on Computer Vision and Robotics (CVR 2023), organized by BBD University Lucknow, India, during 24–25 February 2023. The book discusses applications of computer vision and robotics in the fields like medical science, defence, and smart city planning. The book presents recent works from researchers, academicians, industry, and policy makers.

The 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022. The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking.

This two-volume set, LNCS 13810 and 13811, constitutes the refereed proceedings of the 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, together with the papers of the Second Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022. The total of 84 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 226 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Researching and Analysing Business: Research Methods in Practice provides an accessible and practical guide to various data collection and data analysis techniques within management, from both quantitative and qualitative perspectives. This key resource functions as a comprehensive reference tool – covering a broad variety of methodologies – examining both the theory behind them and their application in practice. These include systematic literature review through bibliometric and meta-analysis, secondary vs primary sources, qualitative research vs quantitative research, combining qualitative and quantitative methods, qualitative and quantitative research method approaches, fsQCA, data mining, and sentiment analysis. Chapters are rich in examples, data sets, practical exercises, easy-to-follow slides, and a glossary, which help readers to understand and apply research approaches as well as to assess the strengths and weaknesses of each method. Unique in its practical approach and with insights from active researchers, this book is required and recommended reading for advanced undergraduate and postgraduate students studying research methods as a core module within business and management courses. It is also a useful tool for PhD students and academics within the discipline. Online support materials include PowerPoint slides.

The five-volume set CCIS 1832-1836 contains the extended abstracts of the posters presented during the 25th International Conference on Human-Computer Interaction, HCII 2023, which was held as a hybrid event in Copenhagen, Denmark, in July 2023. The total of 1578 papers and 396 posters included in the 47 HCII 2023 proceedings volumes were carefully reviewed and selected from the 7472 contributions.The posters presented in these five volumes are organized in topical sections as follows: Part I: ​HCI Design: Theoretical Approaches, Methods and Case Studies; Multimodality and Novel Interaction Techniques and Devices; Perception and Cognition in Interaction; Ethics, Transparency and Trust in HCI; User Experience and Technology Acceptance Studies.Part II: Supporting Health, Psychological Wellbeing, and Fitness; Design for All, Accessibility and Rehabilitation Technologies; Interactive Technologies for the Aging Population.Part III: ​Interacting with Data, Information and Knowledge; Learning and Training Technologies; Interacting with Cultural Heritage and Art.Part IV: ​Social Media: Design, User Experiences and Content Analysis; Advances in eGovernment Services; eCommerce, Mobile Commerce and Digital Marketing: Design and Customer Behavior; Designing and Developing Intelligent Green Environments; (Smart) Product Design.Part V: ​Driving Support and Experiences in Automated Vehicles; eXtended Reality: Design, Interaction Techniques, User Experience and Novel Applications; Applications of AI Technologies in HCI.

In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction. By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.