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Elsevier

Morgan Kaufmann

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1st Edition
    • Shashi A. Chiplonkar + 1 more
    • English
    Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

  • Tcl/Tk

    A Developer's Guide
    • 4th Edition
    • Clif Flynt
    • English
    Tcl/Tk: A Developer's Guide, Fourth Edition is an essential resource for computer professionals, from systems administrators to programmers. It covers new Tcl features, expanded Tcl-OO coverage, web technology using Rivet and SQLite, and AI integration with AWS. The book also delves into Tcl's standard tools, multi-faceted nature, and extensibility, making it ideal for developing GUIs, client/server middleware, and web applications. Readers will quickly learn to code in Tcl and extend its capabilities with the inclusion of numerous code examples and case studies.The updated edition includes over 150 pages on the latest Tcl extensions, proven techniques, and tools for effective programming. Extensive code snippets and online tutorials enhance understanding, while case studies provide practical insights. The book also discusses Tcl's role as the hidden "secret sauce" in commercial applications, highlighting its graphics and control infrastructure. With a vibrant user community and evolving API, Tcl/Tk remains a powerful and versatile programming platform for both beginners and experienced programmers.

  • Challenges and Applications of Generative Large Language Models

    • 1st Edition
    • Anitha S. Pillai + 2 more
    • English
    Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1st Edition
    • Pallavi Chavan + 2 more
    • English
    Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

  • Learning-Driven Game Theory for AI

    Concepts, Models, and Applications
    • 1st Edition
    • Mehdi Salimi + 1 more
    • English
    Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.

  • Engineering Generative AI-Based Software

    • 1st Edition
    • Miroslaw Staroń
    • English
    Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.

  • Multimodal Learning Using Heterogeneous Data

    • 1st Edition
    • Saeid Eslamian + 3 more
    • English
    Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

  • Computer Animation

    Algorithms and Techniques
    • 4th Edition
    • Andrew Hogue + 1 more
    • English
    Computer Animation: Algorithms and Techniques, Fourth Edition surveys computer algorithms and programming techniques for specifying and generating motion for graphical objects, that is, computer animation. It is primarily concerned with three-dimensional (3D) computer animation. In this edition, the most current techniques are covered along with the theory and high-level computation that have earned the book a reputation as the best technically oriented animation resource. As in previous editions, the book addresses practical issues, provides accessible techniques, and offers straightforward implementations.

  • Consensus

    Fueling Blockchain Innovation and DApp Expansion
    • 1st Edition
    • Ali Ahmadian + 3 more
    • English
    Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.

  • Mathematical Modeling for Big Data Analytics

    • 1st Edition
    • Passent El-Kafrawy + 1 more
    • English
    Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

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