Recent advances in information and communication technologies have enhanced the standards of metropolitan planning and development. With the increase in mobile communication, this will help to deliver innovative new services and apps in the field of urban e-planning. New Approaches, Methods, and Tools in Urban E-Planning is a key resource for the latest academic research on recent innovations in urban e-planning, citizen e-participation, the use of social media, and new forms of data collection and idea generation for urban planning. Presenting broad coverage among a variety of pertinent views and themes such as ethnography, e-consultation, and civic engagement, this book is ideally designed for planners, policymakers, researchers, and graduate students interested in how recent technological advancements are enhancing the traditional practices in e-planning.

The internet of things (IoT) has had a major impact on academic and industrial fields. Applying these technologies to healthcare systems reduces medical costs while enriching the patient-centric approach to medicine, allowing for better overall healthcare proficiency. However, usage of IoT in healthcare is still suffering from significant challenges with respect to the cost and accuracy of medical sensors, non-standard IoT system architectures, assorted wearable devices, the huge volume of generated data, and interoperability issues. Incorporating the Internet of Things in Healthcare Applications and Wearable Devices is an essential publication that examines existing challenges and provides solutions for building smart healthcare systems with the latest IoT-enabled technology and addresses how IoT improves the proficiency of healthcare with respect to wireless sensor networks. While highlighting topics including mobility management, sensor integration, and data analytics, this book is ideally designed for computer scientists, bioinformatics analysts, doctors, nurses, hospital executives, medical students, IT specialists, software developers, computer engineers, industry professionals, academicians, researchers, and students seeking current research on how these emerging wireless technologies improve efficiency within the healthcare domain.

Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. Applying Integration Techniques and Methods in Distributed Systems is a critical scholarly publication that defines the current state of distributed systems, determines further goals, and presents architectures and service frameworks to achieve highly integrated distributed systems and presents solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting topics such as multimedia, programming languages, and smart environments, this book is ideal for system administrators, integrators, designers, developers, researchers, and academicians.

Throughout the world, artificial intelligence is reshaping businesses, trade interfaces, economic activities, and society as a whole. In recent years, scholarly research on artificial intelligence has emerged from a variety of empirical and applied domains of knowledge. Computer scientists have developed advanced deep learning algorithms to leverage its utility in a variety of fields such as medicine, energy, travel, education, banking, and business management. Although a growing body of literature is shedding light on artificial intelligence-enabled difficulties, there is still much to be gained by applying fresh theory-driven techniques to this vital topic. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments provides a comprehensive understanding of the business systems, platforms, procedures, and mechanisms that underpin different stakeholders' experiences with reality-enhancing technologies and their transformative application in management. The book also identifies areas in various business processes where artificial intelligence intervention would not only transform the business but would also make the business more sustainable. Covering key topics such as blockchain, business automation, and manufacturing, this reference work is ideal for computer scientists, business owners, managers, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory--optimal control and dynamic programming - or on algorithms--most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework--from design to application--of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

The concept of quantum computing is based on two fundamental principles of quantum mechanics: superposition and entanglement. Instead of using bits, qubits are used in quantum computing, which is a key indicator in the high level of safety and security this type of cryptography ensures. If interfered with or eavesdropped in, qubits will delete or refuse to send, which keeps the information safe. This is vital in the current era where sensitive and important personal information can be digitally shared online. In computer networks, a large amount of data is transferred worldwide daily, including anything from military plans to a country's sensitive information, and data breaches can be disastrous. This is where quantum cryptography comes into play. By not being dependent on computational power, it can easily replace classical cryptography. Limitations and Future Applications of Quantum Cryptography is a critical reference that provides knowledge on the basics of IoT infrastructure using quantum cryptography, the differences between classical and quantum cryptography, and the future aspects and developments in this field. The chapters cover themes that span from the usage of quantum cryptography in healthcare, to forensics, and more. While highlighting topics such as 5G networks, image processing, algorithms, and quantum machine learning, this book is ideally intended for security professionals, IoT developers, computer scientists, practitioners, researchers, academicians, and students interested in the most recent research on quantum computing.

Advances in Computers, Volume 114, the latest volume in this innovative series published since 1960, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters in this updated release include A Comprehensive Survey of Issues in Solid State Drives, Revisiting VM performance and optimization challenges for big data, Towards Realizing Self-Protecting Healthcare Information Systems: Design and Security Challenges, and SSIM and ML based QoE enhancement approach in SDN context.

Social media has emerged as a powerful tool that reaches a wide audience with minimum time and effort. It has a diverse role in society and human life and can boost the visibility of information that allows citizens the ability to play a vital role in creating and fostering social change. This practice can have both positive and negative consequences on society. Examining the Roles of IT and Social Media in Democratic Development and Social Change is a collection of innovative research on the methods and applications of social media within community development and democracy. While highlighting topics including information capitalism, ethical issues, and e-governance, this book is ideally designed for social workers, politicians, public administrators, sociologists, journalists, policymakers, government administrators, academicians, researchers, and students seeking current research on social advancement and change through social media and technology.

FOCAPD-19/Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, July 14 - 18, 2019, compiles the presentations given at the Ninth International Conference on Foundations of Computer-Aided Process Design, FOCAPD-2019. It highlights the meetings held at this event that brings together researchers, educators and practitioners to identify new challenges and opportunities for process and product design.

BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.

Multifunctional Nanocomposites for Targeted Drug Delivery in Cancer Therapy explores the design, synthesis, and application of different multifunctional nanocomposites drug delivery system for cancer treatment. It encompasses initial chapters discussing introductory information about cancer, followed by chapters focusing on the detailed information about various novel drug delivery systems for treatment of several organ site cancers such as prostate, skin, breast, lung, liver, pancreas, stomach, colon, blood, mouth and throat. It is a valuable resource for cancer researchers, oncologists, graduate students, and members of biomedical research who need to understand more about novel nanotechnologies applied to cancer treatment.

Quality assurance is an essential aspect for ensuring the success of corporations worldwide. Consistent quality requirements across organizations of similar types ensure that these requirements can be accurately and easily evaluated. Shaping the Future Through Standardization is an essential scholarly book that examines quality and standardization within diverse organizations globally with a special focus on future perspectives, including how standards and standardization may shape the future. Featuring a wide range of topics such as economics, pedagogy, and management, this book is ideal for academicians, researchers, decision makers, policymakers, managers, corporate professionals, and students.

Medical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals' success rates and treatment options become limited and fall to disastrous levels. Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care.

"Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines" is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems.

The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.
Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brainOffers a new neuromorphic foundation for machine learning based upon the reduced error logistic regression (RELR) method and provides simple examples of RELR computations in toy problems that can be accessed in spreadsheet workbooks through a companion website"