In today’s modern society, to reduce the carbon dioxide gas emission from motor vehicles and to save mother nature, electric vehicles are becoming more practical. As more people begin to see the benefits of this technology, further study on the challenges and best practices is required. Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles focuses on the integration of renewable energy sources with the existing grid, introduces a power exchange scenario in the prevailing power market, considers the use of the electric vehicle market for creating cleaner and transformative energy, and optimizes the control variables with artificial intelligence techniques. Covering key topics such as artificial intelligence, smart grids, and sustainable development, this premier reference source is ideal for government officials, industry professionals, policymakers, researchers, scholars, practitioners, academicians, instructors, and students.

In a world driven by technology and data, classical computing faces limitations in tackling complex challenges like climate modeling and financial risk assessment. These barriers impede our aspirations to revolutionize industries and solve intricate real-world problems. To bridge this gap, we must embrace quantum computing. Edited by Alex Khang PH, Principles and Applications of Quantum Computing is a transformative solution to this challenge. It delves into the interdisciplinary realms of computer science, physics, and mathematics, unveiling the incredible potential of quantum computing, which outperforms supercomputers by 158 million times. This technology, rooted in quantum mechanics, offers solutions to global problems and opens new frontiers in AI, cybersecurity, finance, drug development, and more. By engaging with this book, you become a pioneer in the quantum revolution, contributing to reshaping the limits of what's achievable in our digital age.

In the swiftly evolving realm of technology, the challenge of classical computing's constraints in handling intricate problems has become pronounced. While classical computers excel in many areas, they struggle with complex issues in cryptography, optimization, and molecular simulation. Addressing these escalating challenges requires a disruptive solution to push the boundaries of computation and innovation. Principles and Applications of Quantum Computing Using Essential Math, authored by A. Daniel, M. Arvindhan, Kiranmai Bellam, and N. Krishnaraj. This guide pioneers the transformative potential of quantum computing by seamlessly blending rigorous mathematics with quantum theory. It equips scholars, researchers, and aspiring technologists with insights to grasp and harness quantum computing's capabilities. By delving into quantum gates, algorithms, and error correction techniques, the book demystifies quantum computing, inviting exploration of quantum machine learning, cryptography, and the dynamic interplay between classical and quantum computing. As the quantum landscape expands, this book acts as a vital companion, navigating readers through the converging realms of industry, academia, and innovation. Principles and Applications of Quantum Computing Using Essential Math arrives as a timely answer to the limitations of classical computing, providing scholars with an essential roadmap to navigate the quantum technology landscape. With its clear explanations, practical applications, and forward-looking perspectives, this book serves as an indispensable tool for unraveling quantum computing's mysteries and driving innovation into uncharted domains.

The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.

With the advancement in computing technologies, the need for power is also increasing. Approximately 3% of the total power consumption is spent by data centers and computing devices. This percentage will rise when more internet of things (IoT) devices are connected to the web. The handling of this data requires immense power. Energy Systems Design for Low-Power Computing disseminates the current research and the state-of-the-art technologies, topologies, standards, and techniques for the deployment of energy intelligence in edge computing, distributed computing, and centralized computing infrastructure. Covering topics such as electronic cooling, stochastic data analysis, and energy consumption, this premier reference source is an excellent resource for data center designers, VLSI designers, network developers, students and teachers of higher education, librarians, researchers, and academicians.

Academic scholars and industry professionals alike face the formidable challenge of staying informed about emerging trends and innovations in cloud computing. The expansive realm of cloud technology has been the catalyst for several transformative changes across industries, offering unparalleled opportunities for optimization and innovation. However, even seasoned experts may find themselves daunted by the intricate web of new technologies, including green cloud computing, edge computing, cryptography in the cloud, load balancing strategies, and cloud analytics insights. Emerging Trends in Cloud Computing: Analytics, Scalability, and Service Models provides academic scholars and industry professionals with a comprehensive exploration of these critical cloud computing topics and more. This invaluable resource provides clarity and insight, serving as a guiding beacon in the ever-evolving world of cloud technology. Whether you're seeking to understand the intricacies of cloud security solutions, the nuances of scalability in cloud computing, or the various service models in the cloud, this book empowers you to navigate this dynamic field with confidence and expertise.

NeutroAlgebra and AntiAlgebra were extended to NeutroGeometry and AntiGeometry in order to catch the Non-Euclidean Geometries. In the real world, the spaces and the elements that populate them and the rules that apply to them are not perfect, uniform, homogeneous, or complete. They are fragmentary and disparate, with unclear and conflicting information, and they do not apply in the same degree to each element. Therefore, these partial, hybrid, and mixed structures are necessary. NeutroGeometry, NeutroAlgebra, and SuperHyperAlgebra in Today's World presents applications of many NeutroStructures in our real world and considers NeutroGeometry and AntiGeometry as new fields of research that resemble everyday life. Covering key topics such as hyperbolic geometry, elliptic geometry, and AntiGeometry, this reference work is ideal for mathematicians, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

The confluence of Artificial Intelligence of Things (AIoT) and Semantic Web technologies is nothing short of revolutionary. The profound impact of this synergy extends far beyond the realms of industry, research, and society; it shapes the very fabric of our future. Semantic Web Technologies and Applications in Artificial Intelligence of Things is a meticulously crafted reference that not only acknowledges this significance but also serves as a guide for those navigating the complexities of Industry 4.0 and AIoT. This curated compendium of cutting-edge technologies acts as a veritable knowledge base for future developments. As academics, scholars, and industry professionals, the ideal audience of this book, will find meticulously curated content that caters to their diverse interests and expertise, covering topics ranging from smart agriculture, manufacturing, industry, health sciences, and government. Seasoned academics, students, and visionary industry leaders, will find this book to be an indispensable guide that paves the way for innovation and progress.

Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems: Fundamentals, Technologies, and Solutions is an essential reference for energy researchers, graduate students and engineers who aim to understand the opportunities offered by artificial intelligence for the integration of electric vehicles into smart grids. This book begins by building foundational knowledge for the reader, covering the essentials of artificial intelligence and its applications for electric vehicles in a clear and holistic manner. Next, it breaks down two essential areas of application in more detail: energy management (from to energy harvesting to demand response and complex forecasting), and market strategies (including peer-to-peer, vehicle-to-vehicle, and vehicle-to-everything trading, plus the cyber-security implications). A final part provides detailed case studies and close consideration of challenges, including code and data sets for replication of techniques. Providing a clear pathway from fundamentals to practical implementation, Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems will provide multidisciplinary guidance for implementing this cutting-edge technology in the energy systems of the future. - Supports fundamental understanding of artificial intelligence and its opportunities for energy system specialists - Collects the real-world experiences of global experts - Enables practical implementation of artificial intelligence strategies that support renewable energy integration across energy systems, markets, and grids

Smart Delivery Systems: Solving Complex Vehicle Routing Problems examines both exact and approximate methods for delivering optimal solutions to rich vehicle routing problems, showing both the advantages and disadvantages of each approach. It shows how to apply machine learning and advanced data analysis techniques to improve routing systems, familiarizing readers with the concepts and technologies used in successfully implemented delivery systems. The book explains both the latest theoretical and practical advances in intelligent delivery and scheduling systems and presents practical applications for designing new algorithms for real-life scenarios.