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: Computer Engineering Applications in Electronic, Biomedical, and Automotive System
Автор: Вriаn D'Аndrаdе
Издательство: Nova Science Publishers
Год: 2024
Страниц: 324
Язык: английский
Формат: pdf (true)
Размер: 27.4 MB

Computer Engineering covers a broad range of applications and overlaps with other fields, including materials science, electrical/electronics, vehicles, physics, and statistics. This book provides insights into a few computer engineering applications in electronics, and in the medical and the automotive fields where advances in robotics, embedded systems, and sensors are creating a bold future. The chapters are written from the perspectives of scientists and engineering consultants who have broad experiences across multiple vendors and manufacturers of computer systems. Their unique perspectives are formed from work on consumer products including research, development, safety, legal compliance, and standards for products. Chapter 1 – In this chapter, we explore the transformative impact of miniaturization, Artificial Intelligence, and wireless technology on consumer products, which has given rise to a diverse range of wearable electronic devices. We delve into the evolution, current status, and forthcoming trends in this field, emphasizing key features, potential pitfalls, and crucial safety considerations.

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: AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment
Автор: Тоm Таulli
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true)
Размер: 10.87 MB

Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Gemini, and Claude) to code-specific systems (GitHub Copilot, Tabnine, Cursor, and Amazon CodeWhisperer). You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation. Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code. This guide also describes the best ways of using general purpose LLMs to learn a programming language, explain code, or convert code from one language to another. The Chapter 1 kicks off with a look into how generative AI is changing the game for coders. It talks about how these AI tools are helping developers think more about the big picture and less about the nitty-gritty of coding. The chapter also takes a stroll through the history of programming languages. There are also details about the advanced AI technologies like GPT-4. This book is for any developer, whether you’re just starting out or you’ve been in the game for many years.

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.

Название: Causal AI (MEAP v9)
Автор: Rоbеrt Оsаzuwа Nеss
Издательство: Manning Publications
Год: 2024
Страниц: 576
Язык: английский
Формат: epub
Размер: 39.0 MB

How do you know what might have happened, had you done things differently? Causal Machine Learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. Causal Machine Learning is a major milestone in Machine Learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars. Causal AI teaches you how to build Machine Learning and Deep Learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. For data scientists and Machine Learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python.

Название: Deep Learning Models: A Practical Approach for Hands-On Professionals
Автор: Jоnаh Gаmbа
Издательство: Springer
Год: 2024
Страниц: 211
Язык: английский
Формат: pdf (true), epub
Размер: 65.3 MB

This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With Deep Learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on Deep Learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when Deep Learning is employed. The Chapter 2 introduce some of the concepts needed to start building Deep Learning models in Python. The chapter starts with basic principles related to data manipulation and ends with explanation on how to set up the modelling environment. It is expected that the reader is familiar with some high-level programming concepts which are very easy to acquire within a short space of time. Deep Learning models mostly deal with vectors and matrices as we know them from linear algebra. This book is designed to equip professionals with the necessary skills to thrive in the active field of Deep Learning, where it has the potential to revolutionize traditional problem-solving approaches.

Название: Generative Analysis: The Power of Generative AI for Object-Oriented Software Engineering with UML (Early Release)
Автор: Jim Аrlоw, Ilа Nеustаdt
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 512
Язык: английский
Формат: epub (true)
Размер: 67.4 MB

Learn a new method of object-oriented analysis called generative analysis and keep your skill-set on pace with how generative AI is transforming the face of software engineering. Generative AI is revolutionizing many industries, including software engineering. Many aspects of manual coding are becoming automated, and the skills needed by software engineers, developers, and analysts are evolving. Anyone who writes or works with code will need to produce precise analysis artifacts to feed the AI code generation process. Enter generative analysis: a precise, structured way to for software engineers, programmers, and analysts to transition to this new, AI-enhanced, software engineering world. In Generative Analysis, experts Jim Arlow and Ila Neustadt leverage literate modeling, M++, and multivalent logic to lay out a precise and structured, step-by-step approach to object-oriented analysis that produces clear and unambiguous results suitable for further processing into code by generative AI systems such as Copilot, ChatGPT, and Gemini. Ideally, you would be a business analyst who is also a competent programmer who is comfortable building models of software systems with the Unified Modeling Language (UML). We understand that this is a very big ask! However, you don’t need to be an expert programmer, you just need to be able to read code and understand UML artefacts without necessarily understanding the fine details. For code examples, we have chosen Python because this is probably the most easily readable programming language.

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.