Название: Internet Modeling with Julia: Models, Algorithms and Programs
Автор: Ulrich Hofmann
Издательство: Springer
Год: 2024
Страниц: 164
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB

The book combines internet modeling with the new programming language Julia. It demonstrates Julia's suitability for modeling established internet models and research-oriented topics such as car-to-infrastructure communication and black-box models for load predictions with neural networks. After studying the book and gaining inspiration for further independent analyses, the reader will be able to tackle even more complex modeling tasks in research and development using Julia. In this book, the Julia programming language is used for selected topics of Internet modeling. The “common thread” is the layered architecture of the Internet: from application to technology. For network operators, beyond the technical performance parameters of transmission rate and transmission duration of IP packets, the user satisfaction values Quality of Experience (QoE) with voice, video, haptics transmission are important. With the increase in transmission performance, the old point-to-point lines are also being replaced by packet transmission in the Internet of Things (IoT), and the topic of Quality of Control (QoC) is gaining importance, currently in the area of assisted/autonomous driving. The complex processes on the internet elude exact mathematical White-Box-Model modeling, and approximate learning modeling methods are being intensively researched and integrated into the standards of network architectures. Each chapter begins with a description of the practical background. This is followed by the development of the model and the implementation with Julia, including the a-picture-is-worth-a-thousand-words presentation of results in diagrams. All Julia programs are available for download at GitHub and are labeled with the corresponding figure numbers. The most important pieces of code are explained in the book text.

Название: Machine Learning and Cryptographic Solutions for Data Protection and Network Security
Автор: J. Anitha Ruth, Vijayalakshmi G.V. Mahesh, P. Visalakshi, R. Uma, A. Meenakshi
Издательство: IGI Global
Год: 2024
Страниц: 557
Язык: английский
Формат: pdf (true), epub
Размер: 46.2 MB

In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and Machine Learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage Machine Learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and Machine Learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, Machine Learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. This book is an indispensable guide for scholars navigating the intricate domains of Elliptic Curve Cryptography, Cryptanalysis, Pairing-based Cryptography, Artificial Intelligence, Digital Signature Algorithms, and more. It not only sheds light on current challenges but also provides actionable insights and recommendations, making it an essential resource for those seeking to understand the evolving landscape of data security and actively contribute to its fortification. In a world where the stakes of cybersecurity are higher than ever, this book emerges as a beacon of knowledge, offering a proactive and informed solution to the persistent challenges faced by the research community.

Название: Applications, Challenges, and the Future of ChatGPT
Автор: Priyanka Sharma, Monika Jyotiyana, A.V. Senthil Kumar
Издательство: IGI Global
Год: 2024
Страниц: 333
Язык: английский
Формат: pdf (true), epub
Размер: 32.8 MB

The rapid progress of Artificial Intelligence (AI) technologies has resulted in a complicated landscape for researchers and practitioners. Understanding and navigating the complexities of AI applications, particularly in the context of ChatGPT and its interactions with other AI tools, can be challenging. Researchers and academics need guidance to keep up with these technologies' evolving trends and implications, which leads to gaps in knowledge and implementation strategies. Additionally, the ethical and societal impacts of integrating AI into various domains remain a significant concern, requiring a comprehensive approach to address. Applications, Challenges, and the Future of ChatGPT provide a comprehensive solution to these issues by offering a detailed analysis of the current research trends in AI, focusing on ChatGPT and its interactions with other AI tools. The book delves into how we can effectively utilize ChatGPT and other AI tools to address complex problems by exploring AI applications' collaborative potentials and emerging paradigms. By identifying research gaps and suggesting future directions, this book equips researchers and practitioners with the knowledge and tools necessary to navigate the evolving landscape of AI.

Название: Geospatial Application Development Using Python Programming
Автор: Mohammad Gouse Galety, Arul Kumar Natarajan, Tesfaye Fufa Gedefa, Tsegaye Demsis Lemma
Издательство: IGI Global
Год: 2024
Страниц: 364
Язык: английский
Формат: pdf (true), epub
Размер: 51.7 MB

Academics and researchers currently grapple with a pressing issue; the demand for precise and insightful geographical information has surged across various fields, encompassing urban planning, environmental monitoring, agriculture, and disaster management. This surge has revealed a substantial knowledge gap, underscoring the need for effective applications that can bridge the gap between cutting-edge technologies and practical usage. Geospatial Application Development Using Python Programming emerges as the definitive solution to this challenge. This comprehensive book equips academics, researchers, and professionals with the essential tools and insights required to leverage the capabilities of Python programming in the realm of spatial analysis. It goes beyond merely connecting these two realms; it actively fosters their collaboration. By advancing knowledge in spatial sciences and highlighting Python's pivotal role in data analysis and application development, this book plays a crucial part in addressing the challenge of effectively harnessing geographical data. Tailored to meet the discerning needs of scholars, researchers, and professionals, this book represents an invaluable resource. It offers a comprehensive reference for those aiming to enhance their proficiency in spatial analysis and computer programming, thus encouraging innovation and progress in this dynamic field. For educators and students aspiring to excel in the use of spatial technology and Python programming, this book serves as a catalyst for advancement, contributing to the solution of effectively meeting the increasing demand for precise geographical data and its diverse applications. Geospatial Application Development Using Python Programming is the needed transformative response to a critical academic challenge.

Название: Python: A Practical Learning Approach
Автор: T.S. Murugesh, Shriram K. Vasudevan, Sini Raj Pulari
Издательство: CRC Press
Год: 2025
Страниц: 166
Язык: английский
Формат: pdf (true)
Размер: 31.2 MB

Python’s simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python’s versatility is evident in its applications, spanning web development, data analysis, Machine Learning and automation. The language’s interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python’s wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. Python: A Practical Learning Approach exemplifies Python’s simplicity and versatility with numerous examples, ensuring a seamless learning journey. Beyond theory, the language’s practicality allows learners to actively apply their knowledge in real-world scenarios, establishing Python as an asset in education. Python can be used for building a variety of applications catering to different domains/sectors. One can build Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Data Analysis, Big Data, Automation, Embedded Applications and many other applications with Python. Therefore, it is an ideal choice for anyone to learn.

Название: The Lean-Agile Dilemma: Product Management Inside a Chunky Corporate
Автор: Katie Tamblin
Издательство: Apress
Год: 2024
Страниц: 259
Язык: английский
Формат: pdf (true), epub (true), mobi
Размер: 10.1 MB

Over the last decade, Lean-Agile principles have become the standard methodology by which product and technology teams operate. However, Lean-Agile was written for small companies seeking new markets to buy their innovative software products, not for an average corporation with a catalogue of existing software products that must be maintained and rebuilt. The typical tech-enabled business isn't a lean startup; it's a chunky corporate. This book offers an alternative software development methodology perfectly suited for chunky corporate product management. You’ll quickly find out how individual players in the corporate organization impact business performance. You’ll follow the story of Blake, a well-meaning but ultimately naive, CEO of a fictional company called Acme Tech. As he and Acme struggle to maintain business performance, it becomes apparent how embedded and complex his challenges are. Lean-Agile principles are regularly misapplied in the corporate environment, causing tension, missed deadlines, and inefficient working practices. You’ll see that managing software transformation at a large, mature business requires a new set of tools. Popular development methodologies are not well-suited for the replatforming exercises that chunky corporates inevitably face. This book helps product and technology leaders navigate software development projects at organizations grappling with the constraints of investor ownership.

Название: The Decision Maker's Handbook to Data Science: AI and Data Science for Non-Technical Executives, Managers, and Founders, 3rd Edition
Автор: Stylianos Kampakis
Издательство: Apress
Год: 2024
Страниц: 189
Язык: английский
Формат: pdf (true), epub (true), mobi
Размер: 10.1 MB

Data Science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Artificial Intelligence (AI) is an umbrella term that captures an extraordinary field of computer science dedicated to creating systems capable of tasks that, until recently, required human intelligence. Today, AI stands at the forefront of innovation. The development of advanced algorithms and the exponential increase in computational power have catapulted AI from academic papers to real-world applications. Machine Learning (ML), a cornerstone of contemporary AI, is predicated on the principle that systems can learn from data, identify patterns, and make decisions with minimal human intervention. This paradigm shift from explicit programming to data-driven learning has been pivotal to AI’s success. By using algorithms that iteratively learn from data, ML has enabled AI to develop predictive models that are integral to a myriad of applications, from email filtering to sophisticated diagnostics in healthcare. Deep Learning, a subset of ML, has taken inspiration from the information processing patterns found in the human brain. It utilizes algorithms called neural networks which consist of layered structures resembling neurons. These networks can learn and make intelligent decisions on their own. Deep Learning has been fundamental in advancing AI, enabling machines to recognize images, decipher spoken words, and even generate human-like text.

Название: Coding Games in Scratch
Автор: Jon Woodcock
Издательство: DK
Год: 2024
Страниц: 226
Язык: английский
Формат: epub (true)
Размер: 21.1 MB

If you like playing computer games, why not make your own? This book has all you need to build amazing games, including thrilling racing challenges, zany platform games, and fiendish puzzles. Follow the simple steps to become an expert coder, using the popular programming language Scratch™ 3.0. This updated edition is the first to be tablet-computer friendly, making it even easier to get coding. Improve your coding skills and create your own games, before remixing and customizing them. Jumpy Monkey will show you how to simulate gravity in your games, or give Dog's Dinner a try to learn about collision detection. Pick up the fundamentals of computer programming in steps that make even the most difficult coding concepts fun and easy to understand. Don't just learn how computer code works—understand why it's done that way. Then share your games online and challenge friends and family to beat each other's scores. Once you've whizzed through the book, the possibilities are endless!

Название: Explainable, Interpretable, and Transparent AI Systems
Автор: B.K. Tripathy, Hari Seetha
Издательство: CRC Press
Год: 2025
Страниц: 355
Язык: английский
Формат: pdf (true)
Размер: 29.6 MB

Transparent Artificial Intelligence Systems facilitate understanding the decision-making process and provide opportunities in various aspects of providing explainability of AI models. This book provides up-to-date information on latest advancements in the field of Explainable AI, which is the critical requirement of AI/ML/DL models. It provides examples, case studies, latest techniques, and applications from the domains of health care, finance, network security etc. It also covers open-source interpretable tool kits such that practitioners can use them in their domains. There is an imminent need for explainable AI (XAI) techniques that are accountable, fair, transparent, and trustworthy without compromising the performance of the models, which would lead to better interaction with machines by users. It becomes essential to explain why and how decisions are made by machines. Machine Learning models must have the ability to provide reasoning, describe their merits and demerits, and convey an understanding of how they will behave. To achieve this, new or modified machine‑learning techniques that provide the required explanations must be developed. These models, in combination with human‑computer interface techniques, must be able to provide users with a greater understanding of why, how, and when a machine model or a deep learning model will perform well. It is important for any business to understand the process used to make decisions by AI models. Transparent AI systems facilitate understanding of the decision‑making process and also provide opportunities in various aspects of explaining AI models. Under the above background, this book aims to provide readers with up‑to‑date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), or Deep Learning (DL) models.

Название: Практикум по программированию. Обработка числовых данных
Автор: Алексеев Ю.А., Ваулин А.С., Куров А.В.
Издательство: МГТУ
Год: 2008
Cтраниц: 288
Формат: pdf (ocr)
Размер: 31 мб
Язык: русский

Приведены обработка числовых типов данных, краткие теоретические сведения, примеры программ реализации изучаемых алгоритмов, а также задания для выполнения лабораторных работ по каждой из рассматриваемых в курсе тем. Пособие ориентировано на среду программирования Бе1рЫ и содержит большое количество важных алгоритмов решения инженерных задач.