Название: Spatiotemporal Data Analytics and Modeling: Techniques and Applications
Автор: Jоhn А, Sаthееsh Аbimаnnаn, Еl-Sауеd М. Еl-Аlfу
Издательство: Springer
Серия: Big Data Management
Год: 2024
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 18.9 MB

With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with Artificial Intelligence and Machine Learning and Internet of Things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A “spatial data management system” is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with Artificial Intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of Artificial Intelligence and Machine Learning for spatiotemporal data analysis and modeling. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as Data Science and engineering, Big Data analytics and mining, Artificial Intelligence, Machine Learning and Deep Learning, cloud computing, and Internet of Things.

Название: Deep Learning in Internet of Things for Next Generation Healthcare
Автор: Lаvаnуа Shаrmа, Рrаdеер Кumаr Gаrg
Издательство: CRC Press
Год: 2024
Страниц: 311
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book presents the latest developments in Deep Learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with Deep Learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of Deep Learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with Deep Learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, and genomes. Convolutional neural networks (CNNs), in particular, are excellent at extracting hierarchical characteristics from images using Deep Learning models. These models learn to recognize edges, textures, forms, and even intricate patterns inside images in the context of human vision. CNNs may automatically identify pertinent features for IoT applications from unprocessed visual data without the need for explicit feature engineering. The classifcation and recognition of objects is one of the most important uses of Deep Learning and human vision in IoT. Deep Learning models can be taught to identify and categorize objects in frames of pictures or videos. Security (intruder detection), retail (product identifcation), healthcare (medical image analysis), and other felds all make use of these capabilities. Postgraduate students and researchers in the departments of Computer Science, working in the areas of the IoT, Deep Learning, Machine Learning, image processing, Big Data, cloud computing, and remote sensing will find this book useful.

Название: Practical Web Accessibility: A Comprehensive Guide to Digital Inclusion, 2nd Edition
Автор: Аshlеу Firth
Издательство: Apress
Год: 2024
Страниц: 560
Язык: английский
Формат: pdf (true), epub
Размер: 59.5 MB

Everyone deserves to use the Internet. An estimated 1.3 billion people experience significant disability. That’s 16percent of the world’s population, or one in six of us. At the same time, over 96 percent of the one million most popular websites have an accessibility issue. Add to this the massive rise in legal cases around sites not being accessible, including Beyoncé, Disney, and Netflix, and you have an important topic that more and more people are starting to engage with. In this updated and revamped second edition of the Amazon technology chart-topping Practical Web Accessibility, you’ll be guided through a broad range of disabilities and access needs. You’ll understand the ways these users typically engage with the web, the barriers they often face, and practical advice on how your websites and content can be compliant, but more than that, inclusive and enjoyable to use. There’s also a new chapter on “Outsourcing Accessibility,” exploring third party “bolt-on” tools, “build your own website” platforms like Wix, and popular design systems. You‘ll explore whether they’re helpful or detrimental in the fight to make the web more accessible. For anyone, regardless of what they do, who wants to learn how to make websites and their content more accessible for those with disabilities. In the world of web, the book has been used by front and backend developers, designers, product and project managers, team and business leaders.

Название: Text and Math Into LaTeX, 6th Edition
Автор: Gеоrgе Grаtzеr
Издательство: Springer
Год: 2024
Страниц: 628
Язык: английский
Формат: pdf (true)
Размер: 33.0 MB

For more than 30 years,this comprehensive manual has been the standard introduction and complete reference for writing articles and books containing mathematical formulas. This sixth edition uses a slightly changed title, Text and Math into LaTeX, to emphasize the importance of text in mathematical/scientific composition. Sections that contained commands no longer much needed (such as includeonly) and the introductory sections to PDF (now ubiquitous) have been omitted. Many sections are now enhanced with discussion of new and useful packages. An occasional encouragement for the reader to consult ChatGPT for confirmation on various points illustrates the positive relationship between ChatGPT and LaTeX. LaTeX is widely used in many fields, including mathematics, Computer Science, engineering, physics, chemistry, economics, and linguistics. (ChatGPT lists 45 fields). Part I provides a quick introduction to LATEX, from typing examples of text and math to typing your first article such as on that shown on page 4 and similarly: creating your first presentation, such as the sample presentation, in a very short time. The rest of the book provides a detailed exposition of LATEX. LATEX has a huge collection of rules and commands. While the basics in Part I should serve you well in all your writing, most articles and presentations will also require to look up some special topics.

Название: Handbook of Digital Twins
Автор: Zhihаn Lуu
Издательство: CRC Press
Год: 2024
Страниц: 923
Язык: английский
Формат: pdf (true)
Размер: 13.5 MB

Over the last two decades, Digital Twins (DTs) have become the intelligent representation of future development in industrial production and daily life. Consisting of over 50 chapters by more than 100 contributors, this comprehensive handbook explains the concept, architecture, design specification and application scenarios of DTs. As a virtual model of a process, product or service to pair the virtual and physical worlds, DTs allow data analysis and system monitoring by using simulations. The fast-growing technology has been widely studied and developed in recent years. Featured with centralization, integrity and dynamics, it is cost-effective to drive innovation and performance. Many fields saw the adaptation and implementation across industrial production, healthcare, smart city, transportation and logistics. World-famous enterprises such as Siemens, Tesla, ANSYS and General Electric have built smart factories and pioneered digital production, heading towards Industry 4.0. The Cognitive Digital Twins (CDT) for manufacturing systems was proposed based on advances in cognitive science, Artificial Intelligence (AI) technologies, and Machine Learning techniques. According to the fundamental aspects of cognition, they define CDT as a DT with additional cognitive capabilities, including perception, attention, memory, reasoning, problem‑solving and learning. Recall that a Digital Twin (DT) is a virtual copy of a physical asset that promptly reproduces and alters the state and behavior of the asset. DT intensely employs Industry 4.0 data‑driven Artificial Intelligence technologies and simulations for adaptive monitoring, forecasting, optimization, and controlling asset performance.

Название: AI in Material Science: Revolutionizing Construction in the Age of Industry 4.0
Автор: Sуеd Sааd, Sуеd Аmmаd, Кumееl Rаshееd
Издательство: CRC Press
Год: 2025
Страниц: 289
Язык: английский
Формат: pdf (true)
Размер: 11.7 MB

This book explores the transformative impact of Artificial Intelligence (AI) on material science and construction practices in the Industry 4.0 landscape. It enquires into AI history and applications, examining material optimization, smart materials, and AI in construction. Covering automation, robotics, and AI-assisted design, the book provides insights into ethical considerations and future trends. A modern reference for scholars and professionals, it bridges academia and practical applications in the dynamic intersection of AI and materials science. As an academic exploration, this book navigates the ever-evolving landscape of Artificial Intelligence’s (AI) influence on modern materials within the Industry 4.0 era. The comprehensive nature of this work is rooted in the foundational principles and realistic applications of AI, focusing on its integration into material science and construction practices. Our collective goal in crafting this collection stems from a recognition of the widening breach between academic literature and industry-centric publications within the AI and materials science domain. AI has become an industry-wide revolutionary force in the context of Industry 4.0. The book presents a solid case for continuing the investigation of AI in the field of material science through this academic and professional introduction, opening the door for readers to learn about the revolutionary possibilities that lie at the intersection of AI and the construction industry. Learning about Artificial Intelligence, the fundamental concepts and tenets of AI are covered in this section. It examines the idea of AI, the distinctions between specific and general AI, and the many methods and algorithms used in systems that use AI. Readers can comprehend AI’s possible uses and consequences in the setting of material science by developing a solid comprehension of the technology. AI represents one of the most transformative and interesting fields in ultramodern technology and computer wisdom.

Название: Software Project Management: Methods and Techniques
Автор: Lаwrеnсе J. Реtеrs
Издательство: CRC Press
Год: 2024
Страниц: 245
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

The management of a software project has been shown to be the number one factor in determining a software development project’s success. It has been found that most software projects fail because of poor management. Not surprisingly, most software development managers have not been trained in project management. Software Project Management: Methods and Techniques aims to remedy this situation in two ways: familiarizing software developers with the elements of the project management discipline and providing fact-based resources on practicing software project management. Much like the checklist pilots go through prior to a flight, this book provides a pre-project checklist which enables the software engineering team to review and evaluate an extensive set of technical and sociopolitical risks which will help the software project manager and the team determine the project team’s chances of success. This same list and the individual question responses can be used later as part of the project’s closeout process helping team members to improve their individual and collective abilities to assess risk. Intended for both students and software project managers, the book is organized along the lines of the five major functions of a software project manager: planning; scheduling and costing; controlling; staffing; and motivating. The basics of each of these functions are presented in a single chapter.

Название: Stochastic Methods in Scientific Computing: From Foundations to Advanced Techniques
Автор: Маssimо D’Еliа, Кurt Lаngfеld, Вiаgiо Luсini
Издательство: CRC Press
Год: 2024
Страниц: 401
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

Stochastic Methods in Scientific Computing: From Foundations to Advanced Techniques introduces the reader to advanced concepts in stochastic modelling, rooted in an intuitive yet rigorous presentation of the underlying mathematical concepts. A particular emphasis is placed on illuminating the underpinning Mathematics, and yet have the practical applications in mind. The reader will find valuable insights into topics ranging from Social Sciences and Particle Physics to modern-day Computer Science with Machine Learning and AI in focus. The book also covers recent specialised techniques for notorious issues in the field of stochastic simulations, providing a valuable reference for advanced readers with an active interest in the field. Alongside the advances in computer hardware and the steady increase of available computer resources, computer simulations for advanced scientific computing have enjoyed an upsurge in interest. Practitioners and scientists realised that many applications have stochastic elements for a number of reasons: the lack of data is compensated by a stochastic model replacing those data; a deterministic description of a system is neither possible nor desirable. The Data Science revolution over the last decade has delivered many practical solutions using algoritms called Machine Learning (ML) or Artificial Intelligence (AI). It has triggered a proliferation of tools for everyday life with tremendous impacts, such as speech recognition, voice, image and video generation and medical and other expert systems to name just a few.

Название: Frame Theory in Data Science
Автор: Zhihuа Zhаng, Раllе Е.Т. Jоrgеnsеn
Издательство: Springer
Год: 2024
Страниц: 262
Язык: английский
Формат: pdf (true), epub
Размер: 39.3 MB

This book establishes brand-new frame theory and technical implementation in Data Science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis. Given that Data Science is unanimously recognized as a core driver for achieving Sustainable Development Goals of the United Nations, these frame techniques bring fundamental changes to multi-channel data mining systems and support the development of digital Earth platforms. This book integrates the authors' frame research in the past twenty years and provides cutting-edge techniques and depth for scientists, professionals, and graduate students in Data Science, applied mathematics, environmental science, and geoscience. In this book, we have established the theory of Dirac frames, polynomial frames, quasiorthogonal frames, periodic frames, and frame trees, and characterized adaptive segmentation of data spectral domain by quasi-orthogonal and pseudo-project frame operators. As an emerging branch of statistical and Deep Learning, frame networks can automatically acquire novel knowledge from observation data through a statistical learning process and then makes reliable predictions and downscaling. A frame network consists of three layers: the input layer, the hidden layer, and the output layer, where various frames are embedded into each node of the hidden layer and frame coefficients are used as the weight of the directed edges from one node to another node.

Название: Innovation in the University 4.0 System based on Smart Technologies
Автор: Shаshi Каnt Guрtа, Jоаnnа Rоsаk-Szуrосkа
Издательство: CRC Press
Год: 2024
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 36.2 MB

This text presents a comprehensive analysis of mathematical formulations for proving the effectiveness of Artificial Intelligence in education and investigates the possibilities for integrating advanced Artificial Intelligence algorithms. Artificial Intelligence (AI) in education signifies a paradigm shift in teaching and learning. A major driving force behind AI is the concept of neural networks and Deep Learning. These powerful tools enable AI applications to deliver personalized, dynamic, and engaging learning experiences. To understand the role of these technologies in education, we must first comprehend the basics. Neural networks are AI systems modeled after the human brain, consisting of interconnected layers of nodes, or “neurons,” that process information. These layers constitute an input layer, one or more hidden layers, and an output layer. Each node processes the input it receives and passes on the result, simulating the process of human brain cells transmitting signals. Deep Learning, a subset of Machine Learning, involves using neural networks with multiple hidden layers. The text is primarily written for graduate students, postgraduate students, and academic researchers working in the fields of Computer Science and engineering, information technology and Machine Learning.