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Applied Data Visualization with R and ggplot2
Develop informative and aesthetic visualizations that enable effective data analysis in less timeKey FeaturesDiscover structure of ggplot2, grammar of graphics, and geometric objectsStudy how to design and implement visualization from scratchExplore the advantages of using advanced plotsBook DescriptionApplied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.What you will learnSet up the R environment, RStudio, and understand structure of ggplot2Distinguish variables and use best practices to visualize themChange visualization defaults to reveal more information about dataImplement the grammar of graphics in ggplot2 such as scales and facetingBuild complex and aesthetic visualizations with ggplot2 analysis methodsLogically and systematically explore complex relationshipsCompare variables in a single visual, with advanced plotting methodsWho this book is forApplied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.
R 1,140.00
Empirical Modeling and Data Analysis for Engineers and Applied Scientists
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions.While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models.Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation)Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process.Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter.The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
R 3,297.00
Graph-Theoretic Concepts in Computer Science
This LNCS 13453 constitutes the thoroughly refereed proceedings of the 48th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2022.The 32 full papers presented in this volume were carefully reviewed and selected from a total of 96 submissions. The WG 2022 workshop aims to merge theory and practice by demonstrating how concepts from Graph Theory can be applied to various areas in Computer Science, or by extracting new graph theoretic problems from applications.
R 3,155.33
Basketball Data Science
Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features:One of the first books to provide statistical and data mining methods for the growing field of analytics in basketballPresents tools for modelling graphs and figures to visualize the dataIncludes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test caseProvides the source code and data so readers can do their own analyses on NBA teams and players
R 9,827.44
Data Structures, Algorithms, and Invariants
Data Structures, Algorithms, and Invariants: A Practical Guide provides students with the knowledge and understanding they need to make sensible decisions regarding the choice of an abstract data type (ADT) for particular use cases. The book is a practical guide that helps readers with the process of building a software application utilizing a variety of list-based, tree-based, and graph-based ADTs.The opening chapters discuss solving problems, the concept of computational complexity, different types of ADTs, and why they are needed in software development. Additional chapters address unordered, positional, and ordered ADTs, the process of sorting, ways of determining order, and graphs. Each chapter features an introduction, learning objectives, key terms, takeaways, QR codes that link out to additional content such as videos, and chapter review questions to support the reading and learning experience. Additionally, students gain access to a companion source code repository that provides implementations of many of the ADTs.Data Structures, Algorithms, and Invariants is an ideal textbook for data structure and algorithms courses in information science, data science, and programming.
R 3,143.19
Collaborative Computing: Networking, Applications and Worksharing
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024. The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows: Edge computing & Task scheduling Deep Learning and application Blockchain applications Security and Privacy Protection Representation learning & Collaborative working Graph neural networks & Recommendation systems Federated Learning and application
R 3,926.41
Applied Probability-Computer Science: The Interface Volume 1
These two volumes are the Proceedings of the first special interest meeting instigated and organized by the joint Technical Section and College in Applied Probability of ORSA and THlS. This meeting, which took place January 5-7, 1981 at Florida Atlantic University in Boca Raton, Florida, had the same name as these Proceedings: Applied Probability-Computer Science, the Interface. The goal of that conference was to achieve a meeting of, and a cross fertilization between, two groups of researchers who, from different starting points, had come to work on similar problems, often developing similar methodologies and tools. One of these groups are the applied probabilists, many of whom consider their field an offspring of mathematics, and who find their motivation in many areas of application. The other is that group of computer scientists who, over the years, have found an increasing need in their work for the use of probabilistic models. The most visible area of common methodology between these two groups is networks of queues, Hhich by itself could have been the theme of an entire conference. FunctionQl areas which are, or are becoming, sources of exciting problems are computer performance analysis, data base analysis, analysis of communication protocols, data networks, and mixed voice-data telephone networks. The reader can add to this list by going through the papers in these Proceedings.
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Data Science and Productivity Analytics
This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.
R 6,228.00
Data Science
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
R 9,361.53
Domain-Specific Computer Architectures for Emerging Applications
With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application.DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures.Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.
R 2,674.00
Free and Open Source Software in Modern Data Science and Business Intelligence
Computer software and technologies are advancing at an amazing rate. The accessibility of these software sources allows for a wider power among common users as well as rapid advancement in program development and operating information. Free and Open Source Software in Modern Data Science and Business Intelligence: Emerging Research and Opportunities is a critical scholarly resource that examines the differences between the two types of software, integral in the FOSS movement, and their effect on the distribution and use of software. Featuring coverage on a wide range of topics, such as FOSS Ecology, graph mining, and project tasks, this book is geared towards academicians, researchers, and students interested in current research on the growing importance of FOSS and its expanding reach in IT infrastructure.
R 7,338.60
Graph-Theoretic Concepts in Computer Science
The 34th International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2008) took place in Van Mildert College at Durham University, UK, 30 June – 2 July 2008. The approximately 80 participants came from va- ous countries all over the world, among them Australia, Brazil, Canada, Chile, Czech Republic, France, Greece, Hungary,Israel, Italy, Japan, The Netherlands, Norway, Poland, Spain, Switzerland, UK and the USA. WG 2008 continued the series of 33 previous WG conferences. Since 1975, the WG conference has taken place 21 times in Germany, four times in The Netherlands, twice in Austria as well as once in Italy, Slovakia, Switzerland, the Czech Republic, France, Norway and now in the UK. The WG conference traditionally aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computer science, or by extracting new problems from applications. The goal is to present recent researchresults and to identify and exploredirections of future research. The continuing interest in the WG conferences was re?ected in the number and quality of submissions; 76 papers were submitted and in an evaluation p- cess with four reports per submission, 30 papers were accepted by the Program Committee for the conference. Due to the high number of submissions and the limited schedule of 3 days, various good papers could not be accepted. Therewereexcellent invited talks by Giuseppe Di Battista(UniversitàRoma Tre,Italy)onalgorithmicaspectsof(un)-stableroutingintheInternet,byLeszek G?sieniec (University of Liverpool, UK) on memory-e?cient graph exploration, andbyMartinGrohe(Humboldt-UniversitätzuBerlin,Germany)onalgorithmic meta theorems.
R 1,572.54
Graph-Based Representations in Pattern Recognition
This book constitutes the refereed proceedings of the 14th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2025, held in Caen, France, in June 2025.The 25 full papers presented here were carefully reviewed and selected from 33 submissions. They are organized as per the following topical sections: Cybersecurity based on Graph models; Graph based bioinformatics; Graph similarities and graph patterns; GNN: shortcomings and solutions; Graph learning and computer vision.
R 5,416.71
Emerging Trends in Decision Support: Methodology, Algorithms and Applications
This book includes the peer-reviewed proceedings of the Sixth Edition of the International Colloquium MOAD24 which was held at Mouloud Mammeri University of Tizi-Ouzou, Algeria, Oct 20-22, 2024. It consists of nine parts which cover the main aspects of decision support such as analysis, predictive, mathematical, algorithmic, operational, and economic. The methods developed in this book are based on optimization, graph theory, combinatorics, game theory, control theory, probability and statistics, simulation, stochastic processes, data mining, machine learning, artificial intelligence, fuzzy set theory and mathematical modelling. Its outcomes can be implemented for analysis and prediction in different areas of human activities such as management, economics, knowledge extraction from big data, system control, cyber security, medicine, engineering, and politics. This book can be a good support for decision makers, R&D departments, and researchers in related fields.
R 9,402.17
Applied Time Series Analysis for the Social Sciences
Explore this indispensable and comprehensive guide to time series analysis for students and practitioners in a wide variety of disciplines Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference delivers an accessible guide to time series analysis that includes both theory and practice. The coverage spans developments from ARIMA intervention models and generalized least squares to the London School of Economics (LSE) approach and vector autoregression. Designed to break difficult concepts into manageable pieces while offering plenty of examples and exercises, the author demonstrates the use of lag operator algebra throughout to provide a better understanding of dynamic specification and the connections between model specifications that appear to be more different than they are. The book is ideal for those with minimal mathematical experience, intended to follow a course in multiple regression, and includes exercises designed to build general skills such as mathematical expectation calculations to derive means and variances. Readers will also benefit from the inclusion of: A focus on social science applications and a mix of theory and detailed examples provided throughout An accompanying website with data sets and examples in Stata, SAS and R A simplified unit root testing strategy based on recent developments An examination of various uses and interpretations of lagged dependent variables and the common pitfalls students and researchers face in this area An introduction to LSE methodology such as the COMFAC critique, general-to-specific modeling, and the use of forecasting to evaluate and test models Perfect for students and professional researchers in the political sciences, public policy, sociology, and economics, Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference will also earn a place in the libraries of post graduate students and researchers in public health, public administration and policy, and education.
R 2,605.00
Graph-Theoretic Concepts in Computer Science
This book constitutes the revised selected papers of the 43rd International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2017, held in Eindhoven, The Netherlands, in June 2017.The 31 full papers presented in this volume were carefully reviewed and selected from 71 submissions. They cover a wide range of areas, aiming at connecting theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. Another focus is on presenting recent results and on identifying and exploring promising directions of future research.
R 2,198.71
Collaborative Computing: Networking, Applications and Worksharing
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024. The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows: Edge computing & Task scheduling Deep Learning and application Blockchain applications Security and Privacy Protection Representation learning & Collaborative working Graph neural networks & Recommendation systems Federated Learning and application
R 3,910.55
Graph-Theoretic Concepts in Computer Science
This book constitutes the revised selected papers of the 44th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2018, held in Cottbus, Germany, in June 2018.The 30 full papers presented in this volume were carefully reviewed and selected from 66 submissions. They cover a wide range of areas, aiming at connecting theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. Another focus is on presenting recent results and on identifying and exploring promising directions of future research.
R 2,189.76
Data Science
This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.
R 5,079.73
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
The 4-volume set CCIS 2558 – 2561 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, which took place in Vilnius, Lithuania, in September 2024. The 132 full papers included in these proceedings were carefully reviewed and selected from a total of 272 submissions to the workshops. The papers were organized topical sections as follows:Part I: Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM Workshop 2024), Machine Learning for Sustainable Power Systems (ML4SPS), Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD) and Workshop on Bias and Fairness in AI (BIAS 2024).Part II: Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIM[1]LAI), 2nd Workshop on Hybrid Human-Machine Learning and Decision Making HLDM’24, 15th International Workshop on Machine Learning and Music (MML 2024), Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2024).Part III: 21st International Workshop on Mining and Learning with Graphs (MLG 2024), 2nd International Workshop on Deep Learning meets Neuromorphic Hard[1]ware (website), International Workshop on Machine Learning for Irregular Time Series (ML4ITS 2024), Workshop on Machine Learning for Earth Observation (MACLEAN 2024) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2024) and International Workshop and Tutorial on Data-Centric Artificial Intelligence (DEARING).Part IV: Workshop on Data Science for Social Good (SoGood 2024), 7th Workshop on Machine Learning for CyberSecurity (MLCS 2024), Tutorial and Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI), Workshop on MIning DAta for financial applicationS (MIDAS), Workshop on Advancements in Federated Learning (WAFL), Workshop on Mining and Learning Real-world Dynamics via High-order Networks (MLH).
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Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
R 3,155.20
Data Science and Complex Networks
This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models.The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as alearning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.
R 3,210.00
Proceedings of the 13th International Conference on Information Systems and Computing Technology
This proceedings volume highlights the latest advancements in information systems and computing technology, showcasing innovative research and practical applications. Readers will find valuable insights into emerging technologies such as machine learning, deep learning, knowledge graphs, and UAV-based solutions. Topics include object detection, intelligent systems, federated learning, remote sensing, and next-generation network optimization, with a strong focus on real-world applications like medical imaging, transportation, and cybersecurity. The volume provides readers with a comprehensive understanding of cutting-edge techniques and their potential to address pressing challenges. Its interdisciplinary approach makes it a resourceful guide for staying updated on technological progress. This book is ideal for researchers, and professionals in computing, engineering, and AI domains seeking to expand their knowledge and apply innovative solutions to their work. It serves as a bridge between theoretical research and practical implementations, fostering collaboration across diverse fields.
R 12,318.51
Data Science
This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023.The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections:Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis.Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.
R 3,926.56
Data Science
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020.The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
R 2,235.17
Operator Calculus On Graphs: Theory And Applications In Computer Science
This pioneering book presents a study of the interrelationships among operator calculus, graph theory, and quantum probability in a unified manner, with significant emphasis on symbolic computations and an eye toward applications in computer science.Presented in this book are new methods, built on the algebraic framework of Clifford algebras, for tackling important real world problems related, but not limited to, wireless communications, neural networks, electrical circuits, transportation, and the world wide web. Examples are put forward in Mathematica throughout the book, together with packages for performing symbolic computations.
R 5,247.19
Discovery Science
The two-volume set LNAI 15243 + 15244 constitutes the proceedings of the 27th International Conference on Discovery Science, DS 2024, which took place in Pisa, Italy, during October 14-16, 2024. The 53 full papers presented in the proceedings were carefully reviewed and selected from 121 submissions. They were organized in topical sections as follows: Part I: LLM, Text Analytics, and Ethical Aspects of AI; Natural Language Processing, Sequential Data and Science Discovery; Data-Driven Science Discovery Methodologies; Graph Neural Network, Graph Theory, Unsupervised Learning and Regression; Part II: Tree-Based Models and Causal Discovery; Security and Anomaly Detection; Computer Vision and Explainable AI; Classification Models; SoBigData++: City for Citizens and Explainable AI; SoBigData++: Societal Debates and Misinformation Analysis.
R 3,151.82
Graph-Based Modelling in Science, Technology and Art
This book presents interdisciplinary, cutting-edge and creative applications of graph theory and modeling in science, technology, architecture and art. Topics are divided into three parts: the first one examines mechanical problems related to gears, planetary gears and engineering installations; the second one explores graph-based methods applied to medical analyses as well as biological and chemical modeling; and the third part includes various topics e.g. drama analysis, aiding of design activities and network visualisation. The authors represent several countries in Europe and America, and their contributions show how different, useful and fruitful the utilization of graphs in modelling of engineering systems can be. The book has been designed to serve readers interested in the subject of graph modelling and those with expertise in related areas, as well as members of the worldwide community of graph modelers.
R 4,926.17
Trends in Applied Knowledge-Based Systems and Data Science
This book constitutes the refereed conference proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, held in Morioka, Japan, in August 2-4, 2016.The 80 revised full papers presented were carefully reviewed and selected from 168 submissions. They are organized in topical sections: data science; knowledge base systems; natural language processing and sentiment analysis; semantic Web and social networks; computer vision; medical diagnosis system and bio-informatics; applied neural networks; innovations in intelligent systems and applications; decision support systems; adaptive control; soft computing and multi-agent systems; evolutionary algorithms and heuristic search; system integration for real-life applications.
R 3,157.52
Graph-Theoretic Concepts in Computer Science
The 28th International Workshop on Graph-Theoretic Concepts in Computer ? Science (WG 2002) was held in Cesky ´ Krumlov, a beautiful small town in the southern part of the Czech Republic on the river Vltava (Moldau), June 13–15, 2002. The workshop was organized by the Department of Applied Mathematics of the Faculty of Mathematics and Physics of Charles University in Prague. Since 1975, WG has taken place in Germany 20 times, twice in Austria and The Netherlands, and once in Italy, Slovakia, and Switzerland. As in previous years, the workshop aimed at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in Computer Science, or by extracting new problems from applications.The workshop was devoted to the theoretical and practical aspects of graph concepts in computer science, and its contributed talks showed how recent research results from algorithmic graph theory can be used in computer science and which graph-theoretic questions arise from new developments in computer science. Altogether 61 research papers were submitted and reviewed by the program committee. The program committee represented the wide scienti?c spectrum, and in a careful reviewing process with four reports per submission it selected 36papersforpresentationattheworkshop.Thereferees’commentsaswellasthe numerous fruitful discussions during the workshop have been taken into account by the authors of these conference proceedings.
R 2,015.00
Applied Reconfigurable Computing. Architectures, Tools, and Applications
This book constitutes the proceedings of the 18th International Symposium on Applied Reconfigurable Computing, ARC 2022, held as a virtual event, in September 2022. The 13 full papers presented in this volume were reviewed and selected from 16 submissions. The papers cover a broad spectrum of applications of reconfigurable computing, from driving assistance, data and graph processing acceleration, computer security to the societal relevant topic of supporting early diagnosis of Covid infectious conditions.
R 2,350.00
Data Science for Fake News
This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools.The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news.The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
R 5,330.23
New Trends in Database and Information Systems
This book constitutes short papers, Doctoral Consortium and workshop papers which were presented at the 29th European Conference on New Trends in Databases and Information Systems, ADBIS 2025, which took place in Tampere, Finland, during September 23-26, 2025. This CCIS proceedings book contains 14 short papers and 3 demo papers from the main ADBIS 2025 conference. In addition, 24 out of 48 papers submitted to the workshops are included in this book. The papers have been organized in topical sections as follows: Query Optimization; Spatio-Temporal & Graph Data; Data Sharing & Synthesis; Entity Resolution & Integration; Doctoral Consortium School Invited Talks; MADEISD 2025: 7th Workshop on Modern Approaches in Data Engineering and Information System Design; DOING 2025: 6th Workshop on Intelligent Data - From Data to Knowledge;K-GALS 2025: 4th Workshop on Knowledge Graphs Analysis on a Large Scale; CAIMA 2025: 1st Workshop on Cooperative AI Models and Applications; ERGA 2025: 1st Workshop on Entity Resolution and Graph Alignment; FEHDA 2025: 1st Workshop on Fairness Exploration in Heterogeneous Data and Algorithms; and IT4TOCI 2025: 1st Workshop on Information Technology for Tourism and Culture Industries.
R 3,177.84
Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.
R 10,612.67
Data Science
Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the "business side" of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring themWhen Big Data is the best approach to use for a project, and when it actually ties analysts’ handsCutting edge Artificial Intelligence, as well as classical approaches that work better for many problemsHow many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.
R 2,571.00
Graph-Theoretic Concepts in Computer Science
This book constitutes revised selected papers from the 41st International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2015, held in Garching, Germany, in June 2015. The 32 papers presented in this volume were carefully reviewed and selected from 79 submissions. They were organized in topical sections named: invited talks; computational complexity; design and analysis; computational geometry; structural graph theory; graph drawing; and fixed parameter tractability.
R 2,210.77
Art of Data Science
Although change is constant in business and analytics, some fundamental principles and lessons learned are truly timeless, extending and surviving beyond the rapid ongoing evolution of tools, techniques, and technologies. Through a series of articles published over the course of his 30+ year career in analytics and technology, Doug Gray shares the most important lessons he has learned – with colleagues and students as well – that have helped to ensure success on his journey as a practitioner, leader, and educator.The reader witnesses the Analytical Sciences profession through the mind’s eye of a practitioner who has operated at the forefront of analytically inclined organizations, such as American Airlines and Walmart, delivering solutions that generate hundreds of millions of dollars annually in business value, and an educator teaching students and conducting research at a leading university. Through real‑world project case studies, first‑hand stories, and practical examples, we learn the foundational truth underlying successful analytics applications. From bridging theory and practice, to playing a role as a consultant in digital transformation, to understanding how analytics can be economically transformational, identifying required soft skills like leadership skills, and understanding the reasons why data science projects often fail, the reader can better visualize and understand the nuanced, multidimensional nature of Analytical Sciences best practices, projects, and initiatives.The readers will gain a broad perspective on where and how to find success with Analytical Sciences, including the ability to ensure that we apply the right tool, at the right time and right place, and sometimes in different industries.Finally, through the author’s own career synopsis on becoming a practitioner and leader, and his distilled insights, the reader is offered a view into the future that analytics holds, along with some invaluable career advice regarding where to focus, how to make good choices, and how to measure success individually and organizationally.
R 5,631.86
Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming languageToday’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.
R 1,348.00
Applied Spatial Ecology and GIScience
Applied Spatial Ecology and GIScience: Foundations, Methods, Case Studies, and Lab Exercises bridges ecological knowledge with GIScience principles that make modern spatial analysis possible. Written for students and practitioners in ecology, natural resources, geography, conservation science, and environmental science, this book moves from core concepts—scale, spatial data models, coordinate systems, uncertainty, cartography, and spatial reasoning—to the analytical workflows used to understand principles of conservation and natural resource management at scales that matter. Each methods chapter is paired with hands-on, step-by-step exercises and case studies that translate theory into practice using widely available geospatial data and contemporary GIS tools with descriptions of ArcGIS Pro workflows. Readers will build skills through applied problems related to everything from land change analysis, landscape pattern metrics (including Fragstats), and hydrologic terrain processing to neutral landscape modeling, spatial interpolation of animal population abundances, and mapping of species distributions. Other concepts covered include 3D visualization, climate-zone shift mapping, the assessment of ecosystem productivity and ecoregions, biodiversity mapping, quantifying species–area relationships, and hotspot analysis. Applied Spatial Ecology and GIScience serves as a classroom-ready guide grounded in GIScience, is transparent in its methodological approaches, and focuses on natural resource management and conservation planning.
R 4,414.00
Mathematics and Computer Science III
Mathematics and Computer Science III contains invited and contributed papers on combinatorics, random graphs and networks, algorithms analysis and trees, branching processes, constituting the Proceedings of the Third International Colloquium on Mathematics and Computer Science, held in Vienna in September 2004. It addresses a large public in applied mathematics, discrete mathematics and computer science, including researchers, teachers, graduate students and engineers.
R 3,082.42
Probability and Statistics for Data Science
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.Prerequisites are calculus, some matrix algebra, and some experience in programming.Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
R 2,863.88
Data Science in Applications
This book provides a forum for presenting and discussing new and promising ideas in the broadly understood data science field and scope. Data science is a broad discipline. Related and partially overlapping fields are data mining, pattern recognition, neurocomputing, statistics, mathematics, data visualisation, databases, data processing, knowledge discovery in databases, big data analysis, computer science, cloud computing, machine learning, and artificial intelligence. Recent research has focused on unlocking the new possibilities of artificial intelligence, not only from a theoretical point of view but also from an applied perspective. In light of recent developments and trends in these fields, the topics covered in the book have been expanded and extended to include various aspects of artificial intelligence (AI), advanced data analysis, data analytics, machine learning, and multimedia, both from a theoretical and a practical application perspective. All of these are coming together, so we are seeing a variety of AI-driven approaches. This book contains 12 chapters by data science researchers. They are divided into "AI-supported multimedia systems" and "Developments, challenges, and applications of advanced data analysis and machine learning." The first part of the book contains chapters that discuss various aspects of multimedia systems, notably text, voice, and image, in particular from the point of view of how new developments in AI, advanced data analyses, etc., can provide new, effective and efficient, tools and techniques, maybe even imply research and implementation breakthroughs. The second part of the book, "Developments, challenges and applications of advanced data analysis and machine learning," is concerned with various aspects, problems, solutions, and applications for new sophisticated tools and techniques of data analyses, data analytics, and machine learning providing tools and techniques to take advantage of what is available in data.
R 9,142.14
Topology- and Graph-Informed Imaging Informatics
This book constitutes the proceedings of the First International Workshop on Topology- and Graph-Informed Imaging Informatics, TGI3 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.The 13 full papers presented here were carefully reviewed and selected. These papers focus on the application of Topological Data Analysis (TDA) techniques, along with other computational techniques, for summarizing, analyzing, quantifying, and visualizing complex medical data for a more effective and efficient analysis.
R 2,150.97
Applied Information Science, Engineering and Technology
The objective of the book is to give a selection from the papers, which summarize several important results obtained within the framework of the József Hatvany Doctoral School operating at the University of Miskolc, Hungary. In accordance with the three main research areas of the Doctoral School established for Information Science, Engineering and Technology, the papers can be classified into three groups. They are as follows: (1) Applied Computational Science; (2) Production Information Engineering (IT for Manufacturing included); (3) Material Stream Systems and IT for Logistics. As regards the first area, some papers deal with special issues of algorithms theory and its applications, with computing algorithms for engineering tasks, as well as certain issues of data base systems and knowledge intensive systems. Related to the second research area, the focus is on Production Information Engineering with special regard to discrete production processes. In the second research area the papers show some new integrated systems suitable for optimizing discrete production processes in a top-down way. The papers connecting with the third research field deal with different issues of materials stream systems and logistics, taking into consideration of applied mathematical models and IT-tools. The book makes an effort to ensure certain equilibrium between theory and practice and to show some new approach both from theoretical modelling aspect, as well as experimental and practical point of view.
R 3,028.21
