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1-10 of 75 results in All results

Mathematical Modeling for Big Data Analytics

  • 1st Edition
  • January 1, 2025
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 7 3 5 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 7 3 6 - 9
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.

Data Science in the Medical Field

  • 1st Edition
  • September 27, 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 in the Medical Field focuses on the potential tools that can be used in data science to identify signs of illness at extremely early stages. Today's rapid advancements in data science provide the potential to influence and improve fundamental services in the healthcare sector, including many important applications across the healthcare spectrum. As every human body produces two terabytes of data each day, including brain activity, stress level, heart rate, blood sugar level, and many other data points, this book discusses how data science can help clinicians and researchers handle the massive volume of data to better track patient health.

Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

  • 1st Edition
  • July 13, 2024
  • Siddhartha Bhattacharyya + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 3 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 2 - 1
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

Cognitive Science, Computational Intelligence, and Data Analytics

  • 1st Edition
  • June 6, 2024
  • Vikas Khare + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 9 - 0
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

Smart Spaces

  • 1st Edition
  • March 18, 2024
  • Fatos Xhafa + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 4 6 2 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 4 6 3 - 0
Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems.People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks – a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

Object-Oriented Analysis and Design for Information Systems

  • 2nd Edition
  • March 16, 2024
  • Raul Sidnei Wazlawick
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 7 3 9 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 7 4 0 - 2
Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

Data Fusion Techniques and Applications for Smart Healthcare

  • 1st Edition
  • March 12, 2024
  • Amit Kumar Singh + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 2 3 3 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 2 3 4 - 6
Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry, with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. The book can be used as a reference for practicing engineers, scientists, and researchers, but it will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications.Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, X-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI.

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.

Mathematical Methods in Data Science

  • 1st Edition
  • January 6, 2023
  • Jingli Ren + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 6 7 9 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 6 8 0 - 6
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

Data Analytics for Social Microblogging Platforms

  • 1st Edition
  • November 4, 2022
  • Soumi Dutta + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 8 5 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 3 0 - 7
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

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