Hong Lin - Böcker

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing

There is a growing need for insights and practical experiences in the evolving field of cyber security for business analytics a need addressed by Cyber Security in Business Analytics. Divided into sections covering cyber security basics, artificial intelligence (AI) methods for threat detection, and practical applications in e-commerce and e-banking, the book's team of experts provides valuable insights into securing business data and improving decision-making processes. It covers topics such as data privacy, threat detection, risk assessment, and ethical considerations, catering to both technical and managerial audiences.• Presents real-case scenarios for enhancing understanding of how cyber security principles are applied in diverse organizational settings• Offers advanced technologies such as artificial intelligence methods for cyber threat detection, offering readers• Provides a detailed exploration of howAI can make cybersecurity better by helping detect threats, unusual activities, and predict potential risks• Focuses on the convergence of cyber security and data-driven decision-making and explores how businesses can leverage analytics while safeguarding sensitive information• Includes insights into cutting-edge techniques in the field, such as detailed explorations of various cyber security tools within the context of business analyticsCyber Security in Business Analytics will be useful for scholars, researchers and professionals of computer science and analytics.

Smart grids with distributed clean energy generation, storage and prosumers are the future of energy systems. They need two-way digital communication between multiple customers and suppliers of energy, to produce, buy and sell electricity to the grid at small scales. These arrangements need a system that maintains, checks, and registers information about transactions.Blockchain technology is able to handle these requirements with smart contracts, peer-to-peer energy trading and immutable transactions. A blockchain is a digital ledger of transactions that can be accessed across the entire network of computer systems. Combined with sensors to track energy flows, blockchain can enable the smart grid.After an introduction to blockchain, chapters cover integration with IoT, IoT- and blockchain-enabled smart grids, use of sensors, security and privacy, use of machine and deep learning, energy storage and transaction, as well as use of cryptocurrencies for transactions.Written by an international team with the necessary multi-disciplinarity, this work for researchers in power systems as well as economists with related interests covers the use of blockchain technology for smart grids.

New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.

This book discusses recent advances in the estimation and control of networked systems with unacknowledged packet losses: systems usually known as user-datagram-protocol-like. It presents both the optimal and sub-optimal solutions in the form of algorithms, which are designed to be implemented easily by computer routines. It also provides MATLAB® routines for the key algorithms. It shows how these methods and algorithms can solve estimation and control problems effectively, and identifies potential research directions and ideas to help readers grasp the field more easily.The novel auxiliary estimator method, which is able to deal with estimators that consist of exponentially increasing terms, is developed to analyze the stability and convergence of the optimal estimator. The book also explores the structure and solvability of the optimal control, i.e. linear quadratic Gaussian control. It develops various sub-optimal but efficient solutions for estimation and control for industrial and practical applications, and analyzes their stability and performance.This is a valuable resource for researchers studying networked control systems, especially those related to non-TCP-like networks. The practicality of the ideas included makes it useful for engineers working with networked control.

This book discusses recent advances in the estimation and control of networked systems with unacknowledged packet losses: systems usually known as user-datagram-protocol-like.

It covers a wide range of topics in computer science and information technology, including data mining and data warehousing, high-performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing, cognitive computing, and information security.