Essential Principles of Hardware Implementation in Artificial Intelligence
This book addresses and illustrates the effectiveness of the essential principles of a hardware implementation of an artificial intelligence real-time system for making changes in the production of large-scale products available through multi-input and multi-output platforms. The objective is not only to know about the predictable product but also to provide the right solutions to the expert experience industry. These solutions are supported by advanced technology applications with high ratings, instilling confidence in their quality and effectiveness. On the other hand, artificial intelligence techniques have calculated the overall process and predicted higher approximation rates before production.
Machine Translation and Translation Theory
Pervasive and ubiquitous, machine translation systems have been transforming communication and understanding across languages and cultures on a historical scale. Focused on both Neural Machine Translation tools, such as Google Translate, and generative AI tools, such as ChatGPT, Omri Asscher pursues the juncture between machine translation and the diverse, often competing, frameworks of human translation theory. He shines a light on the subtleties of the intersection between the two: the places where machine translation corresponds well with the ideas that have been developed on human translation throughout the years, and the places where machine translation seems to challenge translation theory, and perhaps even require that we rethink some of its basic assumptions.Machine Translation and Translation Theory reflects the need for an accessible, panoramic view on the subject. It offers a detailed discussion of various points of theoretical interest: definitions of translation; equivalence in translation; aesthetics of translation; translation ethics; translation as cross-cultural communication; and translation's historical agency.This is key reading for researchers and students in translation studies, as well as scholars of AI-mediated communication, and computer scientists interested in how machine translation architectures correspond with the understanding of translation in the humanities.
Machine Translation and Translation Theory
Pervasive and ubiquitous, machine translation systems have been transforming communication and understanding across languages and cultures on a historical scale. Focused on both Neural Machine Translation tools, such as Google Translate, and generative AI tools, such as ChatGPT, Omri Asscher pursues the juncture between machine translation and the diverse, often competing, frameworks of human translation theory. He shines a light on the subtleties of the intersection between the two: the places where machine translation corresponds well with the ideas that have been developed on human translation throughout the years, and the places where machine translation seems to challenge translation theory, and perhaps even require that we rethink some of its basic assumptions.Machine Translation and Translation Theory reflects the need for an accessible, panoramic view on the subject. It offers a detailed discussion of various points of theoretical interest: definitions of translation; equivalence in translation; aesthetics of translation; translation ethics; translation as cross-cultural communication; and translation's historical agency.This is key reading for researchers and students in translation studies, as well as scholars of AI-mediated communication, and computer scientists interested in how machine translation architectures correspond with the understanding of translation in the humanities.
Fuzzy Decision Support Systems for Efficient Energy Management
This reprint highlights innovative research on fuzzy decision support systems (FDSSs) applied to efficient energy management. As global energy demands grow and fossil fuel resources vanish, decision-makers face increasing complexity in balancing sustainability, cost, and performance. The contributions in this collection explore how FDSSs offer powerful tools for navigating uncertainty and supporting complex, multi-criteria decisions across various energy-related domains. Topics include FDSS applications in construction, mobility, industry, environmental management (air, water, soil), and the broader energy lifecycle from exploration to supply. By integrating socio-economic and ecological considerations, this reprint provides a valuable resource for researchers and practitioners developing intelligent and adaptable solutions for energy efficiency.
Transforming Healthcare
This comprehensive volume explores cutting-edge technologies, groundbreaking methodologies, and insightful ethical considerations. It brings together leading minds and research across diverse domains, offering readers a glimpse into the future of healthcare. From leveraging AI in diagnostics and personalized treatments to advancing telemedicine and remote patient monitoring, the text addresses real-world challenges and opportunities in the rapidly evolving healthcare industry.Key features: Innovative approaches to early disease detection, including Alzheimer's and cancer, using machine learning and deep learning techniques. State-of-the-art applications of design thinking, meta-heuristics, and federated learning in healthcare. Ethical perspectives on the use of AI technologies and their impact on businesses and patient care. A deep dive into the role of IoT, NLP, and AI-driven image processing in revolutionizing healthcare delivery. Generalized models analyzing trade credits and their implications for healthcare supply chains. Whether you're a researcher, healthcare professional, or technology enthusiast, this book serves as an invaluable resource for understanding and shaping the future of health technologies. Packed with diverse perspectives and actionable insights, it's a must-read for anyone passionate about making a difference in healthcare.
Robotics
This textbook introduces robotic theories and technologies, bridging the gap between classic theories and real-world applications of robotic manipulators. A valuable resource to broad readers, including those who have a limited background in general engineering and wish to explore non-conventional applications of robotic manipulators.
Robotics
Robotics: From Theory to Practice introduces robotic theories and technologies to audiences, including university students, professionals with engineering backgrounds, and even high-school students interested in building their own robots. We aim to bridge the gap between classic theories and real-world applications of robotic manipulators, which, to date, have far exceeded the domain of conventional industry.The contents are divided into three parts. The first two cover classic theories of robotics, including kinematics, dynamics, path planning, control, and programming. Specifically, Part I is an introduction targeting junior students, featuring more simplistic topics and examples. Part II provides the senior students and professionals with more in-depth discussions on critical topics and more comprehensive examples. In Part III, we demonstrate how classic robotics theory can be extended to more advanced theoretical frameworks and adopted in real-world applications beyond conventional industries.This textbook is valuable to broad readers, including those who have limited background in general engineering and wish to explore non-conventional applications of robotic manipulators. The scaffolded contents from Part I to Part III are created to lower the prerequisites and smooth the learning curve.
Big Data Computing
This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.Features: Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies. Explains computing models using real-world examples and dataset-based experiments. Includes case studies, quality diagrams, and demonstrations in each chapter. Describes modifications and optimization of existing technologies along with the novel big data computing models. Explores references to machine learning, deep learning, and graph processing. This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.
Machine Learning for Neuroscience
This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.
Data Science for Civil Engineering
This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.
Route Optimization and Congestion Control in Wireless Network
The network is made up of two types of components: nodes and communication lines. The nodes typically handle the network protocols and provide switching capabilities. A node is usually itself a computer (general or special) which runs specific network software. The communication lines may take many different shapes and forms, even in the same network. Examples include: copper wire cables, optical fiber, radio channels, and telephone lines. By applying mathematics to a problem of the real world mostly means, at first, modeling the problem mathematically, may be with hard restrictions, idealizations, or simplifications, then solving the mathematical problem, and finally drawing conclusions about the real problem based on the solutions of the mathematical problem. Since about 60 years, a shift of paradigms has taken place in some sense; the opposite way has come into fashion. The point is that the world has done well even in times when nothing about mathematical modeling was known. The one of the alternate ways is evolutionary computation, which encompasses three main components- Evolution strategies, Genetic Algorithms and Evolution programs. Genetic Algorithms encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to several structures so as to preserve critical information. Shortest path routing algorithms are well established problem and addressed by many researchers in different ways. One such alternative is to use a GA-based routing algorithm. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. We propose to use this huge stochastic optimization tool for Optimum Path Routing Problem. GA may be used for optimization of searching process for optimum path routing in a network for optimization of both the distance and the congestion problem in a network. The proposed book "Congestion control in Wireless Networks" has been implemented in MATLAB 7.0.
ConVision- A Context-based Deep Learning Algorithm for Efficient Lane Detection
Dive into the cutting-edge world of artificial intelligence with ""ConVision- A Context-based Deep Learning Algorithm for Efficient Lane Detection."" This comprehensive volume brings together six groundbreaking research studies, each meticulously designed and implemented with IEEE-level formulas, simulations, and equations. Discover how theoretical insights are seamlessly transformed into practical applications, enhancing various AI domains. From sophisticated simulations to robust implementations, this book showcases innovative approaches that push the boundaries of AI technology. Whether you're a researcher, practitioner, or enthusiast, this book offers a deep understanding of AI's core principles and real-world impact. Authored by experts in the field, this volume is a must-read for anyone seeking to stay at the forefront of AI research and application. Get ready to explore the future of artificial intelligence and its transformative potential. Praise for Advances in Artificial Intelligence: ""A remarkable collection of innovative AI research that bridges the gap between theory and practice."" - Dr. Jane Smith, AI Researcher ""Essential reading for anyone involved in AI development and application."" - TechReview Magazine Target Audience: Researchers, Practitioners, AI Enthusiasts, and Academics.
High Security Network for IOT using Edge Computing
Edge computing and the internet of things (IOT) have revolutionised the manner in which data is handled and analysed, making it possible to cut down on latency while simultaneously increasing energy efficiency. Yet, assuring the integrity of these systems and the security of their data continues to be a difficulty. This research proposes a unique system that addresses these difficulties in edge computing and IOT contexts. It mixes Base64 encoding, trustworthy algorithms, and virtual force mapping by trust value. This architecture not only boosts security and dependability by prioritising trustworthy components and preserving data while it is being sent, but it also adds significantly to reductions in energy usage, which is a huge win for everyone involved. The technique that has been provided provides a strong solution for safe, efficient, and reliable data processing in edge computing and IOT systems. As a result, it paves the way for future improvements in these sectors.
Development of Efficient Bio-Inspired Computing Algorithm for Image Classification
Agriculture is a cornerstone of India's economy, deeply intertwined with the nation's food security and the livelihoods of millions. Accounting for approximately 15% of India's GDP, the agricultural sector supports a vast majority of the rural population through employment and income generation. India's diverse crop production, ranging from rice and wheat to sugarcane and cotton, not only drives economic growth but also ensures food stability and self-sufficiency, mitigating the need for imports and protecting against global food crises. Beyond its economic contributions, agriculture fosters rural development by enhancing income levels and promoting rural entrepreneurship, thus lifting communities out of poverty and supporting inclusive growth. The classification of plants within this sector is vital, providing a systematic approach to understanding the vast array of plant species in India. This knowledge aids farmers and agricultural experts in making informed decisions on cultivation, pest management, and resource allocation, while also conserving native species and preserving traditional farming practices. This research delves into the intersection of agriculture and advanced computing techniques, with a focus on the following objectives: 1. Analyzing and evaluating various bio-inspired computing algorithms for plant image segmentation and classification. 2. Proposing a novel bio-inspired optimization algorithm and testing its efficacy on standard benchmarks. 3. Developing a new image classification method based on existing or new bio-inspired algorithms. 4. Comparing the performance of the proposed classification approach against current methods using a plant image dataset. This work aims to enhance agricultural efficiency and sustainability through innovative computational techniques, ultimately benefiting India's vast agricultural landscape.
Artificial Mind
Beyond AI--Artificial Mind with True Intelligence. How that could emerge.Billionaire computer scientist David Sterling was no stranger to controversy but his latest criticisms of AI at its very technological foundation struck a nerve at a time when AI was taking the world by storm-hinting at a new generation of AI that could upend everything. He went so far as to claim AI had absolutely zero intelligence of its own, labeling it as a knowledge system.Before he could share his groundbreaking discovery, Sterling was found-by none other than his old friend, MIT professor William Foster-murdered.Now, haunted by the past and driven by sense of duty, Foster partners with Anastasia Ivanov, a gifted computer scientist, to uncover Sterling's lost research. But powerful forces are closing in-ruthless players determined to claim the discovery for themselves, no matter the cost.The truth could change the world.The price could be their lives.Can Foster and Anastasia survive long enough to unlock the future?Author BioThe author is a retired Canadian computer scientist and engineer. He received his Ph.D. from McGill University, Montreal, Canada. He held various architecture leadership positions at two of the world's largest telecommunication equipment makers, some startup companies, and a mid-sized international corporation of thousands of engineers as its Chief Architect. He played a pivotal role in the design and development of numerous products in supercomputing, server virtualization, and telecommunication. His extensive work in telecommunication spanned the areas of electrical and optical, as well as wireless and wireline.The author has traveled frequently around the globe, partly for pleasure but mostly for providing technical guidance to product development at his companies' R&D sites in North America, Europe, and Asia.
Integration of Cloud Computing with Emerging Technologies
This book gives a complete overview of cloud computing, its importance, its trends, innovations, and its amalgamation with other technologies. It provides content for reference.
Artificial Intelligence in Telemedicine
This book explores the role of artificial Intelligence in Telemedicine. It explains the concepts through the detailed study and processing of biosignals, physiological parameters, and medical images. The book focuses on computational algorithms in telemedicine for the processing of biosignals, physiological parameters, and medical Images. The book is presented in two section. The first section presents the role of computational algorithms in the processing of biosignal and medical images for disease diagnosis and treatment planning. Noise removal in ECG signal using an improved adaptive learning approach, classification of ECG signals using CNN for cardiac arrhythmia detection, EEG signal analysis for stroke detection, and EMG signal analysis for gesture classification were discussed in this section. Application of CNN in pertussis Diagnosis by temperature monitoring, physician handwriting recognition using deep learning model, melanoma detection using ABCD parameters, and transfer learning enabled heuristic approach for pneumonia detection was also discussed in this section The second section focus on the role of IoT and artificial intelligence in the healthcare sector. IoT in smart health care and applications of artificial intelligence in disease diagnosis and prediction was discussed in this section. The importance of 5G/6G in the pandemic scenario for telemedicine applications, wireless capsule endoscopy image compression, leukemia detection from the microscopic cell images, and genomic signal processing using numerical mapping techniques was also discussed in this section. This book can be used by a wide range of users including students, research scholars, faculty, and practitioners in the field of engineering for applications in biomedical signal, image analysis, and diagnosis.
Smart Embedded Systems
"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future.Salient Features: The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologies This book is an essential read for anyone interested in the field of embedded systems. Whether you're a student looking to broaden your knowledge base, researchers looking in-depth insights, or professionals planning to use this cutting-edge technology in real-world applications, this book offers a thorough grounding in the subject.
Smart Cities
This book discusses the basic principles of sustainable development in a smart city ecosystem to better serve the life of citizens. It examines smart city systems driven by emerging IoT-powered technologies and the other dependent platforms.Smart Cities: AI, IoT Technologies, Big Data Solutions, Cloud Platforms, and Cybersecurity Techniques discusses the design and implementation of the core components of the smart city ecosystem. The editors discuss the effective management and development of smart city infrastructures, starting with planning and integrating complex models and diverse frameworks into an ecosystem. Specifically the chapters examine the core infrastructure elements, including activities of the public and private services as well as innovative ICT solutions, computer vision, IoT technologies, data tools, cloud services, AR/VR technologies, cybersecurity techniques, treatment solution of the environmental water pollution, and other intelligent devices for supporting sustainable living in the smart environment.The chapters also discuss machine vision models and implementation as well as real-time robotic applications. Upon reading the book, users will be able to handle the challenges and improvements of security for smart systems, and will have the know-how to analyze and visualize data using big data tools and visualization applications. The book will provide the technologies, solutions as well as designs of smart cities with advanced tools and techniques for students, researchers, engineers, and academics.
Computer Organization, Design, and Architecture
This unique and classroom-proven text provides a hands-on introduction to the design of computer systems. It depicts, step by step, the design and programming of a simple but complete hypothetical computer, followed by detailed architectural features of existing computer systems as enhancements to the structure of the simple computer.
Handbook on Federated Learning
Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.
Computer Organization, Design, and Architecture
This unique and classroom-proven text provides a hands-on introduction to the design of computer systems. It depicts, step by step, the design and programming of a simple but complete hypothetical computer, followed by detailed architectural features of existing computer systems as enhancements to the structure of the simple computer.
Integrating LoRa and Direction APIs for High-Performance Machine Vision in Smart Cars
Advancing Medical Assistance - Hungarian-Language Medical Chatbot with Artificial Intelligence
Edge-AI in Healthcare
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.
Artificial Intelligence on Dark Matter and Dark Energy
As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold.In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-?-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and - to the best of our knowledge - in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet.This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
Blockchain Technology for IoE
This book explores opportunities and challenges in the field of Internet of Everything (IoE) security and privacy under the umbrella of distributed ledger technologies and blockchain technology including distributed consensus mechanisms, crypto-sensors, encryption algorithms, and fault tolerance mechanisms for devices and systems. It focusses on the applicability of blockchain technology, including architectures and platforms for blockchain and IoE, authentication and encryption algorithms for IoE, malicious transactions detection, blockchain for forensics, and so forth. Outlines the major benefits as well as challenges associated with integration of blockchain with IoE; Describes detailed framework to provide security in IoE using blockchain technology; Reviews various issues while using distributed ledger technologies for IoE; Provides comprehensive coverage of blockchain for IoE in securing information including encryption schemes, authentication, security issues, and challenges; Includes case studies in realistic situations like healthcare informatics, smart industry, and smart transportation. This book is aimed at researchers and graduate students in computing, cryptography, IoT, computer engineering, and networks.
Machine Learning Algorithms and Applications in Engineering
Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
Continuous-Time Signals and Systems
Drawing on author's 30+ years of teaching experience, "Continuous-Time Signals and Systems: A MATLAB Integrated Approach" represents a novel and comprehensive approach to understanding signals and systems theory. Many textbooks use MATLAB as a computational tool, but Alkin's text employs MATLAB both computationally and pedagogically to provide interactive, visual reinforcement of fundamental concepts important in the study of continuous- time signals and systems.In addition to 210 traditional end-of-chapter problems and 168 solved examples, the book includes hands-on MATLAB modules consisting of: 77 MATLAB-based homework problems and projects (coordinated with the traditional end-of-chapter problems) 106 live scripts and GUI-based interactive apps that animate key figures and bring core concepts to life Downloadable MATLAB code for most of the solved examples 64 fully detailed MATLAB exercises that involve step by step development of code to simulate the relevant signal and/or system being discussed, including some case studies on topics such as synthesizers, simulating instrument sounds, pulse-width modulation, etc. The ebook+ version includes clickable links that allow running MATLAB code associated with solved examples and exercises in a browser, using the online version of MATLAB. It also includes audio files for some of the examples. Each module or application is linked to a specific segment of the text to ensure seamless integration between learning and doing. The aim is to not simply give the student just another toolbox of MATLAB functions, but to use the development of MATLAB code as part of the learning process, or as a litmus test of students' understanding of the key concepts. All relevant MATLAB code is freely available from the publisher. In addition, a solutions manual, figures, presentation slides and other ancillary materials are available for instructors with qualifying course adoption.
Digital Signal Processing
Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) while also providing a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers. This book goes beyond DSP theory, showing the implementation of algorithms in hardware and software. Additional topics covered include DSP for artificial intelligence, adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as pulse-code modulation, 繕-law, adaptive differential pulse-code modulation, multi-rate DSP, oversampling analog-to-digital conversion, sub-band coding, wavelet transform, and neural networks.
Artificial Intelligence in Radiation Oncology and Biomedical Physics
This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics.AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.
Competing in the Age of AI
The 21st century has ushered in an era of unprecedented technological transformation, and at the heart of this revolution lies Artificial Intelligence (AI). No longer confined to the realms of science fiction, AI has become a driving force reshaping industries, redefining business models, and altering the very fabric of competition. In the Age of AI, organizations are no longer competing solely on traditional factors like scale, efficiency, or product differentiation. Instead, they are racing to harness the power of AI to unlock new opportunities, innovate at scale, and deliver unparalleled value to customers.AI is not just a tool; it is a paradigm shift in how businesses operate. From automating routine tasks to enabling data-driven decision-making, AI is transforming every aspect of the value chain. Companies that embrace AI are reimagining their strategies, reinventing their operations, and redefining their relationships with customers. Those that fail to adapt risk being left behind in an increasingly digital and intelligent world.In this new era, the rules of competition are being rewritten. Traditional barriers to entry are crumbling as AI-powered startups challenge established giants. Data has become the new currency, and algorithms the new differentiators. The ability to collect, analyze, and act on data in real-time is now a critical competitive advantage. Moreover, AI is enabling hyper-personalization, allowing businesses to anticipate customer needs and deliver tailored experiences at scale.The rise of AI technology for business is reshaping industries, redefining competition, and revolutionizing leadership. In the age of AI, enterprises must adapt or risk obsolescence. Whether you're an AI entrepreneur, a corporate leader, or a creative professional, understanding AI tools and AI models is no longer optional-it's essential for survival.From AI for business development to AI business strategy, organizations must leverage AI offerings to stay ahead. Books like Competing in the Age of AI highlight how AI in business is rewriting the rules of the game. Companies that master AI strategy for leaders and implement AI leadership frameworks will dominate their markets, while those that hesitate will fall behind.The business AI revolution extends beyond automation-it's about AI-driven guidance, smarter decision-making, and unlocking AI business data for competitive advantage. Whether deploying AI tools for business, refining business algorithms, or navigating management in the age of AI, leaders must embrace technology leadership to thrive.For AI for entrepreneurs and established firms alike, the future belongs to those who harness AI for corporate growth, AI-driven wellness, and strategy in the digital age. This is more than just AI and business-it's about competing for the future in artificial worlds where intelligence is the ultimate currency.The question is no longer if AI will transform your industry, but how-and whether you're prepared to lead in a world run by AI. Will your AI business plan set you apart, or will you be left behind in the AI age? The time to act is now.
Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare
Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare, 1st Edition, is an excellent compilation of current and advanced Multi-Criteria Decision Making (MCDM) techniques and their applications to multiple recent and innovative healthcare analytics problems. The healthcare business has expanded rapidly in recent years, and one of the top priorities in the sector is now the efficacy and efficiency of the various healthcare delivery systems. The entire performance of hospitals must be improved if the healthcare business wants to see an improvement in both the satisfaction and safety of their patients. Finding the best medical facility among many of its competitors may be difficult since there are so many, and they are so highly diverse in terms of features and performance trade-offs. This book has brought together the introductory discussions, fundamental concepts, challenges, and insights of multiple advanced healthcare management problems along with the application of MCDM to obtain the best option among multiple alternatives. A few important takeaways from this book are: Developing an efficient model for supplier performance evaluation and selection in healthcare industries with incomplete information A computational reliance approach for assessing healthcare service quality aspects and their measurement in an uncertain environment An efficient and provable approach for recommending suitable mobile healthcare products under uncertain environments Establishing a decision-making strategy to select healthcare waste treatment methods Assessing the usability of mHealth applications in practice related to type 2 diabetes The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable MCDM technique when making decisions to prioritize the selection criteria of any healthcare-related problems to ensure a sustainable practice. .
Information Society and Media Development in Modern Mongolia
This book provides an account of Mongolian information society from the perspective of critical media studies. The converged media sphere in modern Mongolia mirrors and shapes political communication, economic outlook, institutional norms, and Mongolian identity. When placing Mongolia on the global information society map, the arguments in the book juxtapose information society tenets and structural constraints like the small market, communist past, and mining-dependent economy. Today, people in Mongolia take advantage of the mobility, speed, and spatiality of the internet, as the Mongolians of old once saddled their horses and galloped across the grassy steps of Eurasia.
Multi-Cloud Administration Guide
As businesses increasingly adopt cloud-first strategies, managing workloads across multiple cloud platforms becomes a critical challenge. This comprehensive book provides practical solutions and in-depth knowledge to efficiently operate in a multi-cloud world. Learn to leverage frameworks from AWS, Azure, GCP, and Alibaba Cloud to maximize the benefits of multi-cloud environments. Understand cloud networking, software-defined networking, and microservices to optimize cloud connectivity. Develop a robust data strategy to ensure data quality, security, and integrity across multiple cloud platforms. Discover how automation and AI can help maintain compliance with governmental and industry regulations in the cloud. Designed for cloud architects, IT administrators, and technical managers, this book is also valuable for anyone looking to deepen their understanding of cloud technologies and multi-cloud strategies. FEATURES- Uses frameworks from AWS, Azure, GCP, and Alibaba Cloud to maximize the benefits of multi-cloud environments- Provides practical instructions and real-world examples for managing multi-cloud environments - Features insights into cloud-native technologies, serverless functions, and container orchestration with Kubernetes- Explores the details of multi-cloud connectivity, storage, compute, data management, security, and compliance- Includes companion files with code samples and color figures available for downloading
Emerging Trends in Computer Science and Its Application
The conference brought together a diverse group of scholars, researchers, and industry professionals to engage in meaningful discussions and share insights on cutting-edge trends in artificial intelligence, machine learning, data science, and their multifaceted applications. This collaboration and knowledge exchange fostered an environment of innovation, making the conference a successful and impactful event for all participants. It aimed to highlight these significant advancements and serve as a valuable resource for researchers, academicians, and practitioners who wish to stay informed about the recent innovations and methodologies shaping the landscape of computational intelligence. By showcasing a wide range of research topics and practical implementations, it not only addressed the current challenges but also inspired new ideas and approaches for future research.
Convergence of Deep Learning and Artificial Intelligence in Internet of Things
This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.- Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things.- Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe.- Explores the importance of blockchain technology in the Internet of Things security issues.- Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy.- Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications.This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It furtherdiscusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.
Machine Learning for Healthcare
Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.
Computer Organisation and Architecture
Computer organization and architecture is becoming an increasingly important core subject in the areas of computer science and its applications, and information technology constantly steers the relentless revolution going on in this discipline. This textbook demystifies the state of the art using a simple and step-by-step development from traditional fundamentals to the most advanced concepts entwined with this subject, maintaining a reasonable balance among various theoretical principles, numerous design approaches, and their actual practical implementations. Being driven by the diversified knowledge gained directly from working in the constantly changing environment of the information technology (IT) industry, the author sets the stage by describing the modern issues in different areas of this subject. He then continues to effectively provide a comprehensive source of material with exciting new developments using a wealth of concrete examples related to recent regulatory changes in the modern design and architecture of different categories of computer systems associated with real-life instances as case studies, ranging from micro to mini, supermini, mainframes, cluster architectures, massively parallel processing (MPP) systems, and even supercomputers with commodity processors. Many of the topics that are briefly discussed in this book to conserve space for new materials are elaborately described from the design perspective to their ultimate practical implementations with representative schematic diagrams available on the book's website.Key Features Microprocessor evolutions and their chronological improvements with illustrations taken from Intel, Motorola, and other leading families Multicore concept and subsequent multicore processors, a new standard in processor design Cluster architecture, a vibrant organizational and architectural development in building up massively distributed/parallel systems InfiniBand, a high-speed link for use in cluster system architecture providing a single-system image FireWire, a high-speed serial bus used for both isochronous real-time data transfer and asynchronous applications, especially needed in multimedia and mobile phones Evolution of embedded systems and their specific characteristics Real-time systems and their major design issues in brief Improved main memory technologies with their recent releases of DDR2, DDR3, Rambus DRAM, and Cache DRAM, widely used in all types of modern systems, including large clusters and high-end servers DVD optical disks and flash drives (pen drives) RAID, a common approach to configuring multiple-disk arrangements used in large server-based systems A good number of problems along with their solutions on different topics after their delivery Exhaustive material with respective figures related to the entire text to illustrate many of the computer design, organization, and architecture issues with examples are available online at http: //crcpress.com/9780367255732 This book serves as a textbook for graduate-level courses for computer science engineering, information technology, electrical engineering, electronics engineering, computer science, BCA, MCA, and other similar courses.
Handbook of Moth-Flame Optimization Algorithm
Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters.Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides an in-depth analysis of this algorithm and the existing methods in the literature to cope with such challenges.Key Features: Reviews the literature of the Moth-Flame Optimization algorithm Provides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithm Proposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems Demonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithm Introduces several applications areas of the Moth-Flame Optimization algorithm This handbook will interest researchers in evolutionary computation and meta-heuristics and those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas.
