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: Data Storytelling with Generative AI: using Python and Altair (MEAP v5)
Автор: Аngеliса Lо Duса
Издательство: Manning Publications
Год: 2024
Страниц: 425
Язык: английский
Формат: pdf, epub
Размер: 46.5 MB

Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data analysis toolkit. Data Storytelling with Python Altair and Generative AI teaches you how to turn raw data into effective, insightful data stories. You’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you’ll love how quick Generative AI makes the whole process! Python is the second ingredient of this book. As a data journalism professor, I have experimented with many Python libraries for data visualization, such as Matplotlib, Plotly, and Seaborn. However, at the end of my experiments, I realized that the simplest library for data visualization is Altair. Unlike other libraries, Altair is declarative, thus enabling you only to focus on the output of your data visualization. Thus, this book will focus on Altair to build data visualization. Although you can find many materials on the web about Altair, they focus only on how to build raw charts. In this book, you’ll learn how to build data stories with Altair and not simply raw charts. Last but not least, you’ll learn how to use Generative AI tools for data storytelling. This book won’t focus on Generative AI concepts and theory. Instead, you will learn how to apply Generative AI tools, including GitHub Copilot, ChatGPT, and DALL-E, to data storytelling.

Название: Data-Centric Artificial Intelligence for Multidisciplinary Applications
Автор: Раrikshit N. Маhаllе, Nаmrаtа N. Wаsаtkаr, Gitаnjаli R. Shindе
Издательство: CRC Press
Год: 2024
Страниц: 309
Язык: английский
Формат: pdf (true)
Размер: 19.4 MB

This book explores the need for a Data-Centric Artificial Intelligence (AI) approach and its application in the multidisciplinary domain, compared to a model‑centric approach. It examines the methodologies for data‑centric approaches, the use of data‑centric approaches in different domains, the need for edge AI and how it differs from cloud‑based AI. It discusses the new category of AI technology, "data‑centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding Machine Learning and Big Data analytics tools, data‑centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. Data-Centric Artificial Intelligence (AI) denotes an approach within AI and Machine Learning (ML) that places significant emphasis on the pivotal role of meticulously curated, high‑quality data in the development and implementation of AI models and systems. Under this paradigm, data assumes the bedrock upon which AI algorithms are constructed and honed, and its effective handling, preprocessing, and analysis stand as pivotal factors for achieving precise and dependable AI outcomes.

Название: AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications
Автор: Sitа Rаni, Раnkаj Вhаmbri, Sасhin Кumаr
Издательство: CRC Press
Год: 2024
Страниц: 339
Язык: английский
Формат: pdf (true)
Размер: 22.0 MB

AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. This literary work delves into the revolutionary convergence of Artificial Intelligence (AI), Digital Twins, and Industry 4.0, providing a detailed examination of the theoretical underpinnings and practical implementations within this swiftly advancing domain. Artificial Intelligence (AI), Machine Learning, Deep Learning, Internet of Things (IoT), Cloud Computing, Digital Twin, and the most recent connectivity services are the most prominent of the era’s cutting-edge technologies that are assisting users intelligently and predicting their behavior, as well as providing the connectivity and convenience of data to satisfy the majority of their needs. The target audience of this book encompasses a broad spectrum, ranging from scholars and learners to practitioners and experts in the fields of research, education, engineering, and industry. The book aims to provide comprehensive insights into the interplay between AI, digital twins, and the Fourth Industrial Revolution, also known as Industry 4.0.

Название: Math and Architectures of Deep Learning (Final Release)
Автор: Кrishnеndu Сhаudhurу
Издательство: Manning Publications
Год: 2024
Страниц: 553
Язык: английский
Формат: pdf (true)
Размер: 75.9 MB

Shine a spotlight into the Deep Learning "black box". This comprehensive and detailed guide reveals the mathematical and architectural concepts behind Deep Learning models, so you can customize, maintain, and explain them more effectively. Deep Learning can’t be learned by going through a small fragmented set of simplified recipes from which all math has been scrubbed out. This is a mathematical topic and mastery requires understanding the math along with the programming. What is needed is a resource which presents this topic with the requisite amount of math—no more and no less—with the connection between the Deep Learning and math explicitly spelled out. This is exactly what this book strives to provide with its dual presentation of the math and corresponding PyTorch code snippets. Readers need to know Python and the basics of algebra and calculus.

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: Intelligent Technologies for Automated Electronic Systems
Автор: S. Каnnаdhаsаn, R. Nаgаrаjаn, N. Shаnmugаsundаrаm, Jуоtir Моу Сhаttеrjее
Издательство: Bentham Books
Год: 2024
Страниц: 234
Язык: английский
Формат: pdf (true)
Размер: 48.4 MB

This volume explores a diverse range of applications for automated Machine Learning and predictive analytics. The content provides use cases for Machine Learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. The major objective of Smart Electronic Systems is to provide a platform where researchers from the fields of hardware and software may work together under one roof to speed up the development of smart electronic systems. Effective and secure data sensing, storage, and processing are essential in today's information age. Modern smart electronic systems meet the criteria of effective sensing, storage, and processing. Effective techniques and software that allow for a quicker analysis and retrieval of necessary data are simultaneously becoming more important. The internet world now includes big data, which comprises large, complex data collections. It is becoming harder to store and analyse the vast amount of structured and unstructured data that has to be collected. With concurrent hardware and software development, the Internet of Things (IoT) and cyber-physical systems (CPS) have been growing to include everyday consumer electronics. The effectiveness and performance of current and next generations of computing and information processing systems depend on advancements in both hardware and software. Some of the focused areas in this field include memristor and memristive systems, advanced 3D IC technologies, design methodologies, and 3D pacing. Others include molecular electronics, biosensors, bio-molecular and biologically inspired computing, nanoelectronics for energy harvesting, spintronics, domain-wall and phase-change memories, and nanoelectronics for energy harvesting.

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: Generative AI in Action (MEAP v5)
Автор: Аmit Ваhrее
Издательство: Manning Publications
Год: 2024
Страниц: 266
Язык: английский
Формат: epub
Размер: 39.7 MB

Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI has created new opportunities for organizations of all sizes. You can easily use tools like ChatGPT, Bard, and Stable Diffusion to generate text and images for product catalogs, marketing campaigns, technical reporting, and other common tasks. Coding assistants like Copilot are accelerating productivity in software teams. In this insightful book, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Generative AI in Action shows you exactly how to add generative AI tools for text, images, and code, and more into your organization’s strategies and projects. The book begins with the fundamentals of generative AI models and architectures, and introduces practical use-cases to create efficient processes for marketing, software development, business report generation and other practical tasks. You’ll quickly master best practices for prompt engineering, model fine tuning and evaluation, and explore the emerging architecture patterns that support generative AI in your enterprise workflow. Along the way, you’ll explore important facts about AI safety and ethics, and look ahead to new trends such as explainable AI, transfer learning, and reinforcement learning. With a frank discussion of risks like hallucinations and jailbreaks, Generative AI in Action gives you the insight you need to incorporate these powerful technologies with confidence. For enterprise architects and senior developers interested in upgrading their architectures with generative AI.