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Books in Mathematics

The Mathematics collection presents a range of foundational and advanced research content across applied and discrete mathematics, including fields such as Computational Mathematics; Differential Equations; Linear Algebra; Modelling & Simulation; Numerical Analysis; Probability & Statistics.

BooksJournals

    • Synthetic Media, Deepfakes, and Cyber Deception

      • 1st Edition
      • May 1, 2026
      • Cameron H. Malin + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 8 8 7 1
      • eBook
        9 7 8 0 4 4 3 2 3 8 8 8 8
      Synthetic Media, Deepfakes, and Cyber Deception: Attacks, Analysis, and Defenses introduces the only analytical Synthetic Media Analysis Framework (SMAF) to help describe cyber threats and help security professionals anticipate and analyze attacks. This framework encompasses seven dimensions: Credibility, Control, Medium, Interactivity, Familiarity, Intended Target, and Evocation. Synthetic media is a broad term that encompasses the artificial manipulation, modification, and production of information, covering a spectrum from audio-video deepfakes to text-based chatbots. Synthetic media provides cyber attackers and scammers with a game-changing advantage over traditional ROSE attacks because they have the potential to convincingly impersonate close associates through text, imagery, voice, and video.This burgeoning threat has yet to be meaningfully addressed through any written treatment on the topic. The book is co-authored by three cyber influence and deception experts who have gained deep knowledge and experience on the topic through diverse, true operational pathways and backgrounds. The diversity and perspectives of the author team makes the content in the book the broadest and deepest treatment of synthetic media attacks available to readers.

    • Functional Analysis: Theory and Applications

      • 1st Edition
      • May 1, 2026
      • Ravi P. Agarwal + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 1 7 4 9
      • eBook
        9 7 8 0 4 4 3 3 3 1 7 5 6
      An applied understanding of functional analysis is essential for students pursuing research or careers in pure mathematics, applied mathematics, mathematical physics, and engineering, among other disciplines.Function... Analysis: Theory and Applications offers a comprehensive exploration of functional analysis. Authored by esteemed mathematicians with extensive expertise in the field, this book thoroughly introduces fundamental concepts in functional analysis, including Banach spaces, Hilbert spaces, operator theory, nonlinear analysis, linear operators, and normed spaces, and implements these in real-world problems across various scientific and engineering disciplines.The book's rigorous mathematical treatment is combined with worked examples, exercises and solutions, visual aids, application case studies, and future directions across all chapters to reinforce learning, while appendices offer supplementary materials, proofs of theorems, and tables of important results, among other resources.

    • Measure and Integration

      • 1st Edition
      • April 1, 2026
      • Rudi Weikard + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 3 9 0 2
      • eBook
        9 7 8 0 4 4 3 2 7 3 9 1 9
      Measure and Integration: Examples, Concepts, and Applications instructs in core proofs, theorems, and approaches of real analysis, as illustrated via compelling exercises and carefully crafted, practical examples. From chapter one onward, students are asked to apply concepts to reinforce understanding and gain applied experience in real analysis. In particular, exercises challenge students to use key proofs of major real analysis theorems to encourage independent thinking and problem solving, and new areas of research powered by real analysis are introduced. Following early chapters on core concepts and approaches of real analysis, the authors apply real analysis across integration on product spaces, radon functionals, bounded variation and lebesgue-stieltjes measures, convolutions, probability, and differential equations, among other topics. Advanced exercises are also included at the end of each chapter, with exercise difficulty level noted for instructors, and solutions included in an appendix.

    • Graph Theory

      • 1st Edition
      • April 1, 2026
      • Sovan Samanta + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 9 4 1 7
      • eBook
        9 7 8 0 4 4 3 3 3 9 4 2 4
      Graph theory is a rapidly evolving and expanding mathematical discipline, with new discoveries, challenges, and techniques emerging every year. Graph Theory: Fundamentals and Applications provides a fully up-to-date and accessible introduction to graph theory, covering both the classical and the modern topics, as well as algorithms and evolving challenges addressed by discipline. Based on the latest syllabi and research trends worldwide, this book includes practical, solved problems that are user friendly to undergraduate, postgraduate, and PhD students, and acts as a key aid in learning the fundamentals and the frontiers of graph theory, as well as developing independent problem-solving and critical thinking skills. This book includes clear instruction in graph representation, basic graph operations, graph connectivity, trees and forests, matching theory, planar graphs and graph drawing, algebraic graph theory, graph traversals, network flows, topological graph theory, and cryptography, among other topics. Each chapter features key term definitions, proofs and algorithms, summary points, and unique exercises to reinforce learning, as well as open problems and research challenges that present unsolved or conjectural problems in graph theory for discussion. Supporting student and instructor sites offer additional exercises, solutions, examples, and case studies in graph theory applications.

    • Quantitative Biology

      • 1st Edition
      • March 7, 2026
      • Alonso Ogueda + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 4 5 2 7
      • eBook
        9 7 8 0 4 4 3 2 7 4 5 3 4
      Quantitative Biology introduces and implements quantitative and data-driven approaches for analyzing biological and bio-inspired systems, covering the foundations of mathematical modeling, analysis, and computation. The book presents a practical mix of both theory and computation for a variety of biological applications, with tied-in, engaging project activities, instruction, programming language, and technological tools. Modeling approaches in the book combine mathematical foundations, statistical reasoning, and computational thinking, with application in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and more, with a later chapter considering applications across societal challenges. Each chapter includes exposure to models and modeling, a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style, helping readers go beyond replication models and into prediction and data-driven discovery. A companion website also features interactive code to accompany projects across each chapter.

    • Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

      • 1st Edition
      • March 1, 2026
      • Manuel González Canché
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 9 6 1 0
      • eBook
        9 7 8 0 4 4 3 2 1 9 6 0 3
      Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

    • Introduction to Fuzzy Mathematics

      • 1st Edition
      • March 1, 2026
      • John Mordeson + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 0 9 7 7
      • eBook
        9 7 8 0 4 4 3 4 4 0 9 8 4
      Introduction to Fuzzy Mathematics: With Applications to Global Problems offers a modern approach to solving complex challenges by applying fuzzy mathematics to real-world situations. This book provides readers with the foundational tools necessary to address pressing issues, such as medical diagnostics, sustainability, refugee crises, and combating human trafficking. With a focus on practical application, each chapter features research projects and exercises that encourage hands-on learning. By integrating theoretical concepts with actionable techniques, the book empowers readers to use fuzzy mathematics as a means of understanding and addressing global problems in a nuanced and innovative way.The text is organized to move from fundamental topics, including fuzzy sets, evidence theory, and implication operators, to advanced applications in areas like sustainability, climate change, and public health models. It further explores the mathematics behind refugee dynamics and delves into fuzzy algebraic structures, geometry, topology, and graph theory. This comprehensive resource is suitable for researchers, practitioners, and policymakers, enhancing their ability to apply mathematical rigor to complex issues.

    • Trigonometry

      • 1st Edition
      • February 23, 2026
      • João Ricardo Branco + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 5 1 0 1
      • eBook
        9 7 8 0 4 4 3 4 4 5 1 1 8
      Trigonometry: From Theory to Application introduces the basics of trigonometry and key areas of practice, fully considering in straightforward, pragmatic terms the characterization of triangles, coordinate transport, and coordinate systems, with emphasis on interpreting key concepts and applying them. This book begins with a review of basic trigonometric functions and their properties, and moves on to trigonometric identities, equations, and practical applications. Each chapter is interconnected, ensuring that students build a solid and cumulative understanding of the concepts, with detailed examples, varied exercises, and full-color illustrations. Special sections dedicated to real-world and technological problems demonstrate how trigonometry is applied in everyday situations, inspiring and motivating students to see the relevance of trigonometry beyond the classroom, across fields ranging from engineering to physics and beyond.

    • Boundary Value Problems and Partial Differential Equations

      • 7th Edition
      • February 18, 2026
      • David L. Powers + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 8 8 2 0
      • eBook
        9 7 8 0 4 4 3 2 9 8 8 3 7
      For over fifty years, Boundary Value Problems and Partial Differential Equations, Seventh Edition has provided advanced students an accessible and practical introduction to deriving, solving, and interpreting explicit solutions involving partial differential equations with boundary and initial conditions. Fully revised and now in its Seventh Edition, this valued text aims to be comprehensive without affecting the accessibility and convenience of the original. The resource’s main tool is Fourier analysis, but the work covers other techniques, including Laplace transform, Fourier transform, numerical methods, characteristics, and separation of variables, as well, to provide well-rounded coverage. Mathematical modeling techniques are illustrated in derivations, which are widely used in engineering and science. In particular, this includes the modeling of heat distribution, a vibrating string or beam under various boundary conditions and constraints. New to this edition, the text also now uniquely discusses the beam equation. Throughout the text, examples and exercises have been included, pulled from the literature based on popular problems from engineering and science. These include some "outside-the-box" exercises at the end of each chapter, which provide challenging and thought-provoking practice that can also be used to promote classroom discussion. Chapters also include Projects, problems that synthesize or dig more deeply into the material that are slightly more involved than standard book exercises, and which are intended to support team solutions. Additional materials, exercises, animations, and more are also accessible to students via links and in-text QR codes to support practice and subject mastery.

    • Quantum Machine Learning (QML): Platform, Tools and Applications

      • 1st Edition
      • Volume 140
      • February 1, 2026
      • English
      • Hardback
        9 7 8 0 4 4 3 2 2 3 8 2 2
      • eBook
        9 7 8 0 4 4 3 2 2 3 8 3 9
      Quantum Machine Learning (QML): Platform, Tools and Applications, Volume 140 in the Advances in Computers series, explores the intersection of quantum computing and artificial intelligence, highlighting the latest advances that promise to revolutionize computational science. This volume introduces foundational concepts in quantum computing and circuits, building toward the practical implementation of quantum machine learning (QML) algorithms. Chapters address challenges such as the gradient vanishing problem in variational quantum circuits, and explore powerful optimization methods enabled by quantum mechanics. The volume also covers advanced applications including quantum approaches to smart grid management, quantum Monte Carlo simulations, and predictive modeling in numerical solvers using quantum neural networks. Real-world relevance is underscored through discussions of transformative quantum algorithms and their potential to reshape machine learning, enabling unprecedented performance in data analysis, optimization, and beyond.

Related subjects

Mathematics and applied mathematics

Statistics and probability