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    • Essentials of Big Data Analytics

      • 1st Edition
      • February 1, 2026
      • Pallavi Chavan + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 0 6 2
      • eBook
        9 7 8 0 4 4 3 4 5 2 0 7 9
      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.

    • Quantum Theory, Decision Making and Social Dynamics

      • 1st Edition
      • February 1, 2026
      • Tofigh Allahviranloo + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 9 0 7
      • eBook
        9 7 8 0 4 4 3 3 6 4 9 1 4
      Quantum Theory, Decision Making and Social Dynamics is an innovative exploration of the intersection of quantum theory, decision-making, and social networks. As quantum theory continues to permeate various fields, there is a growing need for accessible resources that elucidate its principles and applications. This book aims to fill that gap by illuminating the complex interplay between quantum theory and social dynamics, particularly in the context of decision-making. It addresses the challenges of understanding and applying quantum theory in social dynamics and equips readers with the knowledge to harness its potential in decision-making processes. The book is organized into nine chapters, each delving into a specific aspect of quantum theory and its applications. Chapter 1 provides an overview of quantum theory, tracing its historical development and key principles. Chapter 2 explores the integration of quantum theory with fuzzy systems, highlighting real-world applications and emerging trends. In Chapter 3, the focus shifts to the application of quantum theory in social networks, including quantum social network models and case studies. Chapter 4 examines decision-making processes within social networks, while Chapter 5 introduces and analyzes quantum decision-making models. Chapter 6 delves into quantum graph theory, discussing quantum graphs, walks, and algorithms. The subsequent chapters address the challenges and limitations of quantum decision-making (Chapter 7), future directions for the field (Chapter 8), and conclude with a summary of key points and final thoughts (Chapter 9). Quantum Theory, Decision Making and Social Dynamics is an invaluable resource for graduate students, researchers, and professionals in the fields of applied mathematics, computer science, physics, and social sciences interested in applying quantum approaches to the study of decision-making. These readers often face challenges in understanding complex quantum concepts, applying quantum theory to social networks, and utilizing quantum decision-making models. This book provides a solid foundation in quantum theory, introduces its applications in diverse areas, and prepares readers to navigate the quantum age with confidence. By offering a comprehensive overview and practical insights, this book empowers its audience to appreciate the profound impact of quantum theory on social network dynamics and decision-making.

    • Data Science for Teams

      • 1st Edition
      • July 30, 2025
      • Harris V. Georgiou
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 0 6 8
      • eBook
        9 7 8 0 4 4 3 3 6 4 0 7 5
      Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

    • Advanced Topics in Inverse Data Envelopment Analysis

      • 1st Edition
      • July 10, 2025
      • Mehdi Soltanifar + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 8 8 4
      • eBook
        9 7 8 0 4 4 3 3 6 4 8 9 1
      Advanced Topics in Inverse Data Envelopment Analysis: Approaches for Handling Ratio Data explores and tackles the most significant challenges encountered by researchers and practitioners in decision analysis and performance evaluation. This book delves into the sophisticated realm of Ratio Data Envelopment Analysis (DEA-R), offering a thorough examination of advanced methodologies, practical examples, and insights into managing complex problems involving both non-negative and negative data. Filling crucial gaps in existing literature, this comprehensive resource focuses on the emerging field of Inverse DEA-R, equipping readers with the necessary tools and knowledge to address a wide range of challenging data types. This book serves as an essential guide for making informed and efficient decisions, guiding researchers and graduate students in computer science, applied mathematics, industrial engineering, and finance, navigating the complexities of decision analysis in today's data-driven world.

    • Computational Intelligence for Genomics Data

      • 1st Edition
      • January 21, 2025
      • Babita Pandey + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 0 8 0 6
      • eBook
        9 7 8 0 4 4 3 3 0 0 8 1 3
      Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

    • Applied Graph Data Science

      • 1st Edition
      • January 27, 2025
      • Pethuru Raj + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 6 5 4 3
      • eBook
        9 7 8 0 4 4 3 2 9 6 5 5 0
      Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

    • Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

      • 1st Edition
      • July 24, 2025
      • Tilottama Goswami + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 0 4 4 5
      • eBook
        9 7 8 0 4 4 3 3 3 0 4 5 2
      Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.

    • Foundations of Cloud Computing

      • 1st Edition
      • November 1, 2025
      • Robert Shimonski
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 4 7 9 0
      • eBook
        9 7 8 0 4 4 3 2 1 4 8 1 3
      Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

    • Data Science in the Medical Field

      • 1st Edition
      • September 25, 2024
      • Seifedine Kadry + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 4 0 2 8 7
      • eBook
        9 7 8 0 4 4 3 2 4 0 2 9 4
      Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

    • Synthetic Data and Generative AI

      • 1st Edition
      • January 9, 2024
      • Vincent Granville
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 8 5 7 6
      • eBook
        9 7 8 0 4 4 3 2 1 8 5 6 9
      Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.