Radu Prodan – författare

Security and Privacy Issues in Internet of Medical Things addresses the security challenges faced by healthcare providers and patients. As IoMT devices are vulnerable to cyberattacks, and a security breach through IoMT devices may act as a pathway for hackers to enter hospital networks, the book covers a very timely topic. The incorporation of blockchain in the healthcare environment has given birth to the Internet of Medical Things (IoMT), which consists of a collection of healthcare systems that espouse groundbreaking technologies. Systems consist of inter-linked sensors, wearable technology devices and clinical frameworks that perform explicit, secure machine-to-machine and cloud platform communications.

The significance of IoMT in the field of healthcare is undoubtedly a win-win situation for patients through technology enhancements and a collection of analytics that helps in better diagnosis and treatment. Due to higher accuracy levels, IoMT devices are more reliable in reporting and data tracking and help avoid human errors and incorrect reporting.

Provides methods for constructing novel IoMT architectures and middleware services for healthcare applications to protect and secure patient data and privacy Presents readers with information security and privacy models for IoMT, including Artificial Intelligence and Deep Learning, Data Storage security, Cloud, Fog and Edge computing security, and Wireless sensor device security Provides readers with case studies for real-world applications of IoMT security, including risk assessment for IoMT, Ethical issues in IoMT, Security assessment frameworks, and Threat-based security analysis for IoMT

Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagnosis and treatment of neurological disorders. Each chapter provides in-depth exploration of specific areas where AI can improve existing methodologies, offering practical guidance, case studies, and research findings that can be directly applied in the field. It explains the application of AI in diagnosing and treating major neurological illnesses and showcases the potential of AI in predicting diseases such as epilepsy and neurodegenerative disorders.As such, this book offers a detailed overview of AI and machine learning techniques relevant to neurological research.Offers a comprehensive exploration of cutting-edge AI, big data analytics, and machine learning methodologies specifically applied in the field of neurologyPresents various machine learning techniques such as image segmentation, classification, neural networks, and image processingShowcases techniques in diagnosing early neurological disease identification and deep learning applications using advanced brain imaging technologies like EEG, MEG, fMRI, fNIRS, and PETProvides practical insights and case studies

Grid computing has become a topic of significant interest in the scientific community as a means of enabling application developers to aggregate resources scattered around the globe for solving large-scale scientific problems. This monograph addresses four critical software development aspects for the engineering and execution of applications on parallel and Grid architectures.A new directive-based language called ZEN is proposed for compact specification of wide value ranges of interest for arbitrary application parameters, including problem or machine sizes, array or loop distributions, software libraries, interconnection networks, or target execution machines. Based on the ZEN language, a novel experiment management tool called ZENTURIO is developed for automatic experiment management of large-scale performance and parameter studies on parallel and Grid architectures. This tool has been validated with respect to functionality and usefulness on several real-world parallel applications from various domains, including theoretical chemistry, photonics, finances, and numerical mathematics.Depending on the ZENTURIO experiment management architecture a generic optimization framework is built up that integrates general-purpose meta-heuristics for solving NP-complete performance and parameter optimization problems in an exponential search space specified using the ZEN experiment specification language. Finally a timely approach is proposed for modeling and executing scientific workflows in dynamic and heterogeneous Grid environments, introducing an abstract formal model for hierarchical representation of complex directed graph-based workflows.Thus this monograph contributes to various research areas related to integrated tool development for efficient engineering and high performance execution of scientific applications in Grid environments.