الصفحة 1
الصفحة 1
Object detection with deep learning models : Principles and applications
Discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval / A structured overview of deep learning in object detection / A diversified collection of applications of object detection using deep neural networks / Emphasize agriculture and remote sensing domains / Exclusive discussion on moving object detection
Multimedia computing systems and virtual reality
Presents recent advancements in multimedia systems as they relate to various application areas such as healthcare services and agriculture-related industries. The authors also discuss human-machine interface design, graphics modelling, rendering/animation, image/graphics techniques/systems and visualization. They then go on to explore multimedia content adaptation for interoperable delivery. Finally, the book covers cultural heritage, philosophical/ethical/societal/international issues, standards-related virtual technology and multimedia uses. This book is intended for computer engineers and computer scientists developing applications for multimedia and virtual reality and professionals working in object design and visualization, transformation, modelling and animation of the real world.
Mobile and Wireless Communications with Practical Use-Case Scenarios
While wireless technologies had a spectacular evolution over the past years, the present trend is to adopt a global heterogeneous network of shared standards that enables the provisioning of Quality of Service and Quality of Experience to the end-user. To this end, enabling technologies like Machine Learning, Internet of Things, Digital Twins, are seen as promising solutions for next generation networks that will enable an intelligent adaptive interconnected environment with support for prediction and decision making so that the heterogeneous applications and users requirements can be highly satisfied. The aim of this textbook is to provide the readers with a comprehensive technical foundation of the mobile communication systems and wireless network design, operations and applications of various radio access technologies. Additionally, it also introduces the reader to the latest advancements in technologies in terms of Internet of Things ecosystem, Machine Learning and Digital Twins for IoT-enabled intelligent environments. Furthermore, this textbook also includes practical use-case scenarios using Altair WinProp Software as well as Phyton, TensorFlow and Jupiter as support for practice-based laboratory sessions"
Microwave Engineering : Concepts and Fundamentals
Covers everything from wave propagation to reflection and refraction, guided waves, and transmission lines, providing a comprehensive understanding of the underlying principles at the core of microwave engineering. This encyclopedic text not only encompasses nearly all facets of microwave engineering, but also gives all topics—including microwave generation, measurement, and processing—equal emphasis. Packed with illustrations to aid in comprehension. Describes the mathematical theory of waveguides and ferrite devices, devoting an entire chapter to the Smith chart and its applications Discusses different types of microwave components, antennas, tubes, transistors, diodes, and parametric devices Examines various attributes of cavity resonators, semiconductor and RF/microwave devices, and microwave integrated circuits
IoT and AI Technologies for Sustainable Living : A Practical Handbook
Brings together all the latest methodologies, tools and techniques related to the Internet of Things and Artificial Intelligence in a single volume to build insight into their use in sustainable living. The areas of application include agriculture, smart farming, healthcare, bioinformatics, self-diagnosis systems, body sensor networks, multimedia mining, and multimedia in forensics and security. Provides a comprehensive discussion of modeling and implementation in water resource optimization, recognizing pest patterns, traffic scheduling, web mining, cyber security and cyber forensics. It will help develop an understanding of the need for AI and IoT to have a sustainable era of human living. The tools covered include genetic algorithms, cloud computing, water resource management, web mining, machine learning, block chaining, learning algorithms, sentimental analysis and Natural Language Processing (NLP).
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Introduction to C++ : 500+ Difficulty-Scaled Solved Programming Exercises
Includes more than 500 exercises and examples of progressive difficulty to aid the reader in understanding the C++ principles and to see how concepts can materialize in code. The examples are designed to be short, concrete, and substantial, quickly giving the reader the ability to understand how to apply correctly and efficiently the features of the C++ language and to get a solid programming know-how. Rest assured that if you are able to understand this book's examples and solve the exercises, you can safely go on to edit larger programs, you will be able to develop your own applications, and you will have certainly established a solid fundamental conceptual and practical background to expand your knowledge and skills
Intelligent Interactive multimedia systems for e-healthcare applications
It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more. It covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for health monitoring and management systems using machine learning techniques, and for fusion applications in medical image processing. The book goes into detail on the various methods and techniques for supporting multimedia systems for e-healthcare.
Information Security Handbook
Provides a comprehensive collection of knowledge for emerging multidisciplinary research areas such as cybersecurity, IoT, Blockchain, Machine Learning, Data Science, and AI. This book brings together, in one resource, information security across multiple domains. It explores basic and high-level concepts and serves as a manual for industry while also helping beginners to understand both basic and advanced aspects in security-related issues. The handbook explores security and privacy issues through the IoT ecosystem and implications to the real world and, at the same time, explains the concepts of IoT-related technologies, trends, and future directions.
Human-Computer Interaction and Cybersecurity Handbook - Human Factors and Ergonomics
Covers all areas of human–computer interaction and human factors in cybersecurity Includes information for IT specialists, who often desire more knowledge about the human side of cybersecurity Provides a reference for other disciplines such as psychology, behavioral science, software engineering, and security management Offers a source of information for cybersecurity practitioners in government agencies and private enterprises Presents new areas such as smart networking and devices
Handbook of Fractional Calculus for Engineering and Science
Provides reliable methods for solving fractional-order models in science and engineering. Contains efficient numerical methods and algorithms for engineering-related equations. Contains comparison of various methods for accuracy and validity. Demonstrates the applicability of fractional calculus in science and engineering. Examines qualitative as well as quantitative properties of solutions of various types of science- and engineering-related equations.
Foundation of semantic web technologies
Concentrates on Semantic Web technologies standardized by the World Wide Web Consortium: RDF and SPARQL enable data exchange and querying, RDFS and OWL provide expressive ontology modeling, and RIF supports rule-based modeling. The text also describes methods for specifying, querying, and reasoning with ontological information. In addition, it explores topics that are clearly beyond foundations, such as tools, applications, and engineering aspects.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
Data science, AI, and machine learning in drug development
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations
Data science and data analytics : Opportunities and challenges
Gives the concept of data science, tools, and algorithms that exist for many useful applications / Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems / Identifies many areas and uses of data science in the smart era / Applies data science to agriculture, healthcare, graph mining, education, security, etc.
Data analytics, computational statistics, and operations research for engineers : Methodologies and applications
Presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information.
Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks
Discusses intelligent computing through the Internet of Things (IoT) and Big-Data in vehicular environments in a single volume. It covers important topics, such as topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular ad-hoc networks, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
