Computer Science, Technology and Applications – serie

The Internet of Medical Things (IoMT) is emerging from today’s world to futuristic vision in reshaping healthcare. From wearable devices to AI enabled implantable devices, the IoMT connects patients, providers, and policymakers promising unprecedented accuracy, efficiency, and personalization. Though this also brings critical challenges like cybersecurity threats, legal uncertainties, ethical dilemmas, and the urgent demand for transparency and trust. This book brings together leading voices from computer science, law, and ethics to present a comprehensive guide to smart IoMT decision systems where you will find: knowledge-based frameworks that blend AI, semantics, and predictive analytics; legal and regulatory analyses from India, the EU, and the United States; discussions on accountability, transparency, and ethical responsibility; practical insights into risks, threats, and proactive security architectures and a research roadmap for next-generation IoMT intelligence, including digital twins, federated learning, and hybrid AI models. Whether you are a healthcare professional, AI researcher, policymaker, or student, this book offers critical understanding and forward-looking vision needed to navigate the evolving world of IoMT with confidence and care.

This book presents cutting-edge, effective methods of artificial intelligence and machine learning for data modeling, with applications to the important and challenging area of air pollution. Both classical stochastic and tree-based ensemble learning approaches and their hybrid combinations are considered, including: ARIMA, Temporal Causal Modeling, wavelet transforms, Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forests, and Adaptively Resampling and Combining (Arcing). The selected methods require minimal computer resources (execution time and memory) and are oriented for inclusion in distributed environments and mobile devices. In addition, the book emphasizes the statistically correct construction and investigation of the problems under consideration and detailed analysis of model errors, rather than presenting statistical theory or ready-made codes from software packages. The indicated approaches have been demonstrated to forecast time series of air pollutants such as particulate matter, sulfur dioxide, nitrogen dioxide, and others, depending on a small number of rapidly changing meteorological and atmospheric factors. The methods and frameworks are applied to empirical data from several cities in Bulgaria. The results of the individual applications are presented in five chapters as case studies. These studies demonstrate in detail the steps of the developed approaches for modeling and forecasting of real measured data related to urban air pollution. The book has the potential to serve not only as a systematic introduction to the selected ensemble learning methods for time series, but also as a tool and guide for building adequate and statistically valid forecasting models.