Data print books and ebooks | Elsevier

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.

Quantum Cryptography and Annealing for Securing Industrial IoT

1st Edition
March 1, 2026
Seifedine Kadry + 5 more
English
Paperback
9 7 8 0 4 4 3 3 8 3 4 9 6
eBook
9 7 8 0 4 4 3 3 8 3 5 0 2

The Industrial Internet of Things (IIoT) revolutionizes industry, but with its interconnectedness comes vulnerability. Traditional cybersecurity crumbles before the looming threat of quantum computers. Quantum Cryptography and Annealing for Securing Industrial IoT focuses on the rapidly evolving field of quantum security solutions for Industrial Internet of Things (IIoT) platforms, emphasizing the critical intersection of quantum cryptography, post-quantum cryptography, and their practical applications in IIoT. The book’s primary objective is to drive advancements that significantly intersect quantum cryptography in securing IIoT devices, elevate secure IIoT infrastructures, and optimize the overall delivery. Distinguishing itself by prioritizing practical applications, this book offers a nuanced perspective on how technological integrations in quantum cryptosystems are actively employed in real-world scenarios. The authors meticulously examine the role of quantum cryptosystems in the design, analysis, and optimization of IIoT-specific hardware, examining their resilience to physical and side-channel attacks and evaluating performance. This book strikes a balance between theoretical concepts and practical applications, providing insights into the challenges and solutions encountered in applying quantum cryptographical principles to IIoT engineering problems. It highlights the interdisciplinary collaboration required in the field, recognizing the collaborative nature of quantum cryptography engineering that involves professionals from diverse fields. The book empowers readers with a comprehensive understanding of the pivotal role played by applied quantum cryptography in shaping the future of industrial engineering.

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.

Fundamentals of Statistics for Researchers and Data Analysts

1st Edition
January 1, 2026
Shashi Chiplonkar + 1 more
English
Paperback
9 7 8 0 4 4 3 4 4 7 5 1 8
eBook
9 7 8 0 4 4 3 4 4 7 5 2 5

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.

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.

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

1st Edition
May 12, 2025
Dinh Thai Hoang + 3 more
English
Paperback
9 7 8 0 4 4 3 2 9 0 3 2 9
eBook
9 7 8 0 4 4 3 2 9 0 3 3 6

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.

Foundations of Cloud Computing

1st Edition
October 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.

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.

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.

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