In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning’s contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments. Demystifying Federated Learning for Blockchain and Industrial Internet of Things rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. It provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication. Covering topics such as smart agriculture, object identification, and educational big data, this premier reference source is an essential resource for computer scientists, programmers, government officials, business leaders and managers, students and faculty of higher education, researchers, and academicians.

FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources. Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service. Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage. This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered. Audience The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.

The Internet of Things (IoT) and blockchain are two new technologies that combine elements in many ways. A system where the virtual and physical worlds interact is created by integrating pervasive computing, ubiquitous computing, communication technologies, sensing technologies, Internet Protocol, and embedded devices. A massive number of linked devices and vast amounts of data present new prospects for developing services that can directly benefit the economy, environment, society, and individual residents. Due to the size of IoT and insufficient data security, security breaches may have a huge impact and negative effects. IoT not only connects gadgets but also people and other entities, leaving every IoT component open to a wide variety of assaults. The implementation and application of IoT and blockchain technology in actual scientific, biomedical, and data applications are covered in this book. The book highlights important advancements in health science research and development by applying the distinctive capabilities inherent to distributed ledger systems. Each chapter describes the current uses of blockchain in real-world data collection, medicine development, device tracking, and more meaningful patient interaction. All of these are used to create opportunities for expanding health science research. This paradigm change is studied from the perspectives of pharmaceutical executives, biotechnology entrepreneurs, regulatory bodies, ethical review boards, and blockchain developers. Key Features: Provides a foundation for the implementation process of blockchain and IoT devices based on healthcare-related technology Image processing and IoT device researchers can correlate their work with other requirements of advanced technology in the healthcare domain Conveys the latest technology, including artificial intelligence and machine learning, in healthcare-related technology Useful for the researcher to explore new things like security, cryptography, and privacy in healthcare related technology Tailored for people who want to start in healthcare-related technology with blockchain and IoT This book is primarily for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, and biomedical engineering.

DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.

Cybersecurity has emerged to address the need for connectivity and seamless integration with other devices and vulnerability assessment to find loopholes. However, there are potential challenges ahead in meeting the growing need for cybersecurity. This includes design and implementation challenges, application connectivity, data gathering, cyber-attacks, and cyberspace analysis. Perspectives on Ethical Hacking and Penetration Testing familiarizes readers with in-depth and professional hacking and vulnerability scanning subjects. The book discusses each of the processes and tools systematically and logically so that the reader can see how the data from each tool may be fully exploited in the penetration test’s succeeding stages. This procedure enables readers to observe how the research instruments and phases interact. This book provides a high level of understanding of the emerging technologies in penetration testing, cyber-attacks, and ethical hacking and offers the potential of acquiring and processing a tremendous amount of data from the physical world. Covering topics such as cybercrimes, digital forensics, and wireless hacking, this premier reference source is an excellent resource for cybersecurity professionals, IT managers, students and educators of higher education, librarians, researchers, and academicians.

Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue. Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.

As social robots and the artificial intelligence (AI) that powers them become more advanced, they will likely take on more social and work roles. There is a variety of ways social robots can be engaged in human life, and they can leave an impact in terms of ease of use, productivity, and human support. The interactivity and receptivity of social robots can encourage humans to form social relationships with them. But now robots are intended to perform socially intelligent and interactive services like reception, guidance, emotional companionship, and more, which makes social human-robot interaction essential to help improve aspects of quality of life as well as to improve the efficiency of human care services. AI-Enabled Social Robotics in Human Care Services addresses recent advances in the latest technologies, new research results, and developments in the area of social robotics and AI and the latest developments in the field and future directions that can be beneficial to human society and human care services. Covering topics such as agriculture waste management systems, elder care, and facial emotion recognition, this premier reference source is an essential resource for AI professionals, computer scientists, robotics engineers, human care professionals, students and educators of higher education, librarians, researchers, and academicians.

The book provides comprehensive research ideas about Edge-AI technology that can assist doctors in making better data-driven decisions. It provides insights for improving the healthcare industry by examining future trends, simplifying decision making and investigating structured and unstructured data. Edge-AI in Healthcare: Trends and Future Perspective is more than a comprehensive introduction to Artificial Intelligence as a tool in healthcare data. The book is split into five chapters covering the entire healthcare ecosystem. First section is introduction to Edge-AI in healthcare. It discusses data usage, modelling and simulation techniques as well as machine and deep learning approaches. The second section discusses the implementation of edge AI for smart healthcare. The topics discussed in this section include, AR/VR and cloud computing, big data management, algorithms, optimization, and IoMT techniques and methods. Third section covers role of Edge-AI in healthcare and the challenges and opportunities of the technologies. This section also provides case studies and discusses sustainability, security, privacy, and trust related to Edge-AI in healthcare. This book is intended to benefit researchers, academics, industry professionals, R & D organizations and students working in the field of healthcare, healthcare informatics and their applications.

COMPUTATIONAL INTELLIGENCE IN BIOPRINTING The book provides a comprehensive exploration of the evolving field of bioprinting in regenerative medicine and is an essential guide for professionals seeking a thorough understanding of the field. Computational Intelligence in Bioprinting provides a comprehensive overview of the evolving field of bioprinting in reformative medicine, defining the process of printing structures using viable cells, biomaterials, and living molecules. The primary goal is to provide substitutes for tissue implants, which might lead to eliminating the requirement for organ donors, as well as to transform animal testing for the learning and analysis of disease and the growth of treatments. The book offers a comprehensive overview of bioprinting technologies and their applications, emphasizing the integration of computation intelligence, artificial intelligence, and other computer science advancements in the field. By harnessing the power of computational intelligence techniques such as AI, machine learning, optimization algorithms, and data analytics, existing hurdles can be overcome and the full potential of bioprinting can be unlocked. The book covers an extensive range of topics, including bio-ink formulation and characterization, bioprinter hardware and software design, tissue and organ modeling, image analysis, process optimization, and quality control. Audience The book is aimed at professionals, practitioners and researchers in the fields of bioprinting, tissue engineering, and computational intelligence in medicine.