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Principles of Big Graph: In-depth Insight
Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph.
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Lifetime Data: Models in Reliability and Survival Analysis
Statistical models and methods for lifetime and other time-to-event data are widely used in many fields, including medicine, the environmental sciences, actuarial science, engineering, economics, management, and the social sciences. For example, closely related statistical methods have been applied to the study of the incubation period of diseases such as AIDS, the remission time of cancers, life tables, the time-to-failure of engineering systems, employment duration, and the length of marriages. This volume contains a selection of papers based on the 1994 International Research Conference on Lifetime Data Models in Reliability and Survival Analysis, held at Harvard University. The conference brought together a varied group of researchers and practitioners to advance and promote statistical science in the many fields that deal with lifetime and other time-to-event-data. The volume illustrates the depth and diversity of the field. A few of the authors have published their conference presentations in the new journal Lifetime Data Analysis (Kluwer Academic Publishers).
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Computational Intelligence in Data Science
This book constitutes the refereed post-conference proceedings of the Third IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2020, held in Chennai, India, in February 2020.The 19 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: computational intelligence for text analysis; computational intelligence for image and video analysis; and data science.
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Ordered Algebraic Structures
'Et moi, ...si j'avait su comment en revenir, One service mathematics has rendered the je n'y serais point alit.' human race. It has put common sense back Jules Verne where it belongs. on the topmost shelf next to the dusty canister labelled 'discarded non- The series is divergent; therefore we may be sense'. able to do something with it. Eric T. Bcll o. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and nOD- linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ...'; 'One service logic has rendered com- puter science ...'; 'One service category theory has rendered mathematics ...'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.
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Conceptual Knowledge Structures
This book constitutes the refereed proceedings of the Third International Joint Conference on Conceptual Knowledge Structures, CONCEPTS 2026, held in Montpellier, France, during August 31– September 4, 2026.The 18 full papers and 7 short papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in following topical sections: Closing an Open Problem; Theory; Algorithms; Data Analysis; and Applications. Additionally, there are 11 Nectar papers included in this volume highlighting recent contributions from members of the conceptual structures community that have appeared in other venues.
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Computational Medicine in Data Mining and Modeling
This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more.This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter.Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.
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Statistical Analysis of Graph Structures in Random Variable Networks
This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.
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Constructive Computation in Stochastic Models with Applications
"Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic systems. It summarizes recent important advances in computational study of stochastic models from several crucial directions, such as stationary computation, transient solution, asymptotic analysis, reward processes, decision processes, sensitivity analysis as well as game theory. Graduate students, researchers and practicing engineers in the field of operations research, management sciences, applied probability, computer networks, manufacturing systems, transportation systems, insurance and finance, risk management and biological sciences will find this book valuable.Dr. Quan-Lin Li is an Associate Professor at the Department of Industrial Engineering of Tsinghua University, China.
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Topological Methods in Data Analysis and Visualization V
This collection of peer-reviewed workshop papers provides comprehensive coverage of cutting-edge research into topological approaches to data analysis and visualization. It encompasses the full range of new algorithms and insights, including fast homology computation, comparative analysis of simplification techniques, and key applications in materials and medical science. The book also addresses core research challenges such as the representation of large and complex datasets, and integrating numerical methods with robust combinatorial algorithms. In keeping with the focus of the TopoInVis 2017 Workshop, the contributions reflect the latest advances in finding experimental solutions to open problems in the sector. They provide an essential snapshot of state-of-the-art research, helping researchers to keep abreast of the latest developments and providing a basis for future work. Gathering papers by some of the world’s leading experts on topological techniques, the book represents a valuable contribution to a field of growing importance, with applications in disciplines ranging from engineering to medicine.
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Recent Trends in Data Type Specification
The algebraic specification of abstract data types has beena flourishing research topic in computer science since 1974.The main goal of this work isto evolve theoreticalfoundations and a methodology to support the design andformal development of reliable software.This volume gives the proceedings of the Eighth Workshop onSpecification of Abstract Data Types, held jointly with theThird COMPASS workshop near Paris in August 1991. The maintopics covered by the joint workshop are:- specification languagesand program development- algebraic specification of concurrency- theorem proving- object-oriented specifications- order-sorted algebras- abstract implementation and behavioral semantics.The volume contains four invited surveys and twelvecontributed papers, all of which underwent a carefulrefereeing process.
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Dynamic Mixed Models for Familial Longitudinal Data
This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalizedestimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up.The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents theillustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to thesociety including his special services for society’s annual meetings.
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Theory and Applications of Relational Structures as Knowledge Instruments
Relational structures abound in our daily environment: relational databases, data mining, scaling procedures, preference relations, etc. As the documentation of scientific results achieved within the European COST Action 274, TARSKI, this book advances the understanding of relational structures and the use of relational methods in various application fields.The 12 revised full papers were carefully reviewed and selected for presentations. The papers are devoted to mechanization of relational reasoning, relational scaling and preferences, and algebraic and logical foundations of real world relations.
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Modeling and Using Context
This book constitutes the proceedings of the 9thInternational and Interdisciplinary Conference on Modeling and Using Context,CONTEXT 2015, held in Larnaca, Cyprus, in November 2015.The 33 full papers and 13 short papers presented werecarefully reviewed and selected from 91 submissions. The main theme of CONTEXT2015 was "Back to the roots", focusing on the importance ofinterdisciplinary cooperations and studies of the phenomenon. Context, contextmodeling and context comprehension are central topics in linguistics,philosophy, sociology, artificial intelligence, computer science, art, law,organizational sciences, cognitive science, psychology, etc. and are alsoessential for the effectiveness of modern, complex and distributed softwaresystems.CONTEXT 2015 embedded also a Doctoral Symposium, andthree workshops; Smart University 3.0; CATI: Context Awareness and TactileDesign for Mobile Interaction; and SHAPES 3.0: The Shape of Things.
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Algorithms and Architectures for Parallel Processing
The six-volume set, LNCS 15251-15256, constitutes the refereed proceedings of the 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024, held in Macau, China, during October 29-31, 2024. The 91 full papers, 35 short papers and 5 workshop papers included in these proceedings were carefully reviewed and selected from 265 submissions. They focus on the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems.
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Algorithms and Architectures for Parallel Processing
The six-volume set, LNCS 15251-15256, constitutes the refereed proceedings of the 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024, held in Macau, China, during October 29-31, 2024. The 91 full papers, 35 short papers and 5 workshop papers included in these proceedings were carefully reviewed and selected from 265 submissions. They focus on the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems.
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Algorithms and Architectures for Parallel Processing
The six-volume set, LNCS 15251-15256, constitutes the refereed proceedings of the 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024, held in Macau, China, during October 29-31, 2024. The 91 full papers, 35 short papers and 5 workshop papers included in these proceedings were carefully reviewed and selected from 265 submissions. They focus on the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems.
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Operating Systems In Depth
This book is designed for a one-semester operating-systems course for advanced undergraduates and beginning graduate students. Prerequisites for the course generally include an introductory course on computer architecture and an advanced programming course. The goal of this book is to bring together and explain current practice in operating systems. This includes much of what is traditionally covered in operating-system textbooks: concurrency, scheduling, linking and loading, storage management (both real and virtual), file systems, and security. However, the book also covers issues that come up every day in operating-systems design and implementation but are not often taught in undergraduate courses. For example, the text includes: Deferred work, which includes deferred and asynchronous procedure calls in Windows, tasklets in Linux, and interrupt threads in Solaris.The intricacies of thread switching, on both uniprocessor and multiprocessor systems.Modern file systems, such as ZFS and WAFL.Distributed file systems, including CIFS and NFS version 4. The book and its accompanying significant programming projects make students come to grips with current operating systems and their major operating-system components and to attain an intimate understanding of how they work.
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Algorithms and Architectures for Parallel Processing
The six-volume set, LNCS 15251-15256, constitutes the refereed proceedings of the 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024, held in Macau, China, during October 29-31, 2024. The 91 full papers, 35 short papers and 5 workshop papers included in these proceedings were carefully reviewed and selected from 265 submissions. They focus on the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems.
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Advances in Non-destructive Evaluation
This book comprises the proceedings of the Conference and Exhibition on Non Destructive Evaluation, (NDE 2019). The contents of the book encompass a vast spectrum from Conventional to Advanced NDE including novel methods, instrumentation, sensors, procedures and data analytics as applied to all industry segments for quality control, periodic maintenance, life estimation, structural integrity and related areas. This book will be a useful reference for students, researchers and practitioners.
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Advances in Computer Science - ASIAN 2005. Data Management on the Web
The series of annual Asian Computing Science Conferences (ASIAN) was ini- ated in 1995 by AIT, INRIA and UNU/IIST to provide a forum for researchers in computer science from the Asian region and to promote interaction with - searchers from other regions. The ?rst nine conferences were held respectively in Bangkok, Singapore, Katmandu, Manila, Phuket, Penang, Hanoi, Mumbai and Chiang Mai. The 10th ASIAN conference was held in Kunming, China, December 7-9, 2005. Each year, the conference focuses on a di?erent theme at the cutting edge of computer science research. The theme of ASIAN 2005 was data management on the Web. Four distinguished speakers were invited to the conference: M- iano Consens (University of Toronto), Richard Hull (Bell Laboratories, Lucent Technologies),TovaMilo(TelAvivUniversity)andPrabhakarRaghavan(Yahoo Research and Stanford University). This year, 91 papers were submitted from 11 countries. The Program C- mittee selected 17 regular papers and 21 short papers. This volume contains the papers based on the invited talks, the regular papers, and the short papers.We wish to thank the Program Committee and the external referees for their work in selecting the contributed papers.
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2025
The 16-volume set LNCS 15960 - 15975 constitutes the refereed proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea, during September 23-27, 2025. The total of 1027 papers included in the proceedings was carefully reviewed and selected from 3447 submissions. They were organized in topical parts as follows: Part I, LNCS Volume 15960: Multimodal Fusion and Contextual Reasoning in Medical Imaging Part II, LNCS Volume 15961: Surgical Navigation, Scene Understanding, and Video Modeling Part III, LNCS Volume 15962: Learning and Augmented Reality for Surgical and Endoscopic Applications (I) Part IV, LNCS Volume 15963: Learning and Augmented Reality for Surgical and Endoscopic Applications (II) Part V, LNCS Volume 15964: Graph-Based Methods in Medical Imaging Part VI, LNCS Volume 15965: Datasets and Methods for Image Quality Enhancement Part VII, LNCS Volume 15966: Trustworthy and Responsible AI for Medical Imaging Part VIII, LNCS Volume 15967: Multimodal Learning for Diagnosis, Risk Prediction, and Survival Analysis Part IX, LNCS Volume 15968: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (I) Part X, LNCS Volume 15969: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (II) Part XI, LNCS Volume 15970: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (III) Part XII, LNCS Volume 15971: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (IV) Part XIII, LNCS Volume 15972: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (I) Part XIV, LNCS Volume 15973: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II) Part XV, LNCS Volume 15974: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (III) Part XVI, LNCS Volume 15975: Statistical Techniques in Medical Imaging: Causality, Imputation, Weak Supervision, and Other Methods
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Univariate, Bivariate, and Multivariate Statistics Using R
A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical softwareProvides a resource for students and professionals in the social, behavioral, and natural sciencesPuts the emphasis on the computational tools used in the discovery of empirical patternsFeatures a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projectsShows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.
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Mining Text Data
Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
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Self-Assembly of Nano- and Micro-structured Materials Using Colloidal Engineering
Self-assembly of Nano- and Micro-structured Materials Using Colloidal Engineering, Volume 12, covers the recent breakthroughs in the design and manufacture of functional colloids at the micro- and nanoscale level. In addition, it provides analyses on how these functionalities can be exploited to develop self-assembly pathways towards nano- and micro-structured materials. As we seek increasingly complex functions for colloidal superstructures, in silico design will play a critical role in guiding experimental fabrication by reducing the element of trial-and-error that would otherwise be involved. In addition to novel experimental approaches, recent developments in computational modelling are also presented, along with an overview of the arsenal of designing tools that are available to the modern materials scientist.
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Analysis and Computation of Microstructure in Finite Plasticity
This book addresses the need for a fundamental understanding of the physical origin, the mathematical behavior and the numerical treatment of models which include microstructure. Leading scientists present their efforts involving mathematical analysis, numerical analysis, computational mechanics, material modelling and experiment. The mathematical analyses are based on methods from the calculus of variations, while in the numerical implementation global optimization algorithms play a central role. The modeling covers all length scales, from the atomic structure up to macroscopic samples. The development of the models ware guided by experiments on single and polycrystals and results will be checked against experimental data.
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Applied Numerical Methods Using MATLAB
This new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB® This accessible book makes use of MATLAB® software to teach the fundamental concepts for applying numerical methods to solve practical engineering and/or science problems. It presents programs in a complete form so that readers can run them instantly with no programming skill, allowing them to focus on understanding the mathematical manipulation process and making interpretations of the results. Applied Numerical Methods Using MATLAB®, Second Edition begins with an introduction to MATLAB usage and computational errors, covering everything from input/output of data, to various kinds of computing errors, and on to parameter sharing and passing, and more. The system of linear equations is covered next, followed by a chapter on the interpolation by Lagrange polynomial. The next sections look at interpolation and curve fitting, nonlinear equations, numerical differentiation/integration, ordinary differential equations, and optimization. Numerous methods such as the Simpson, Euler, Heun, Runge-kutta, Golden Search, Nelder-Mead, and more are all covered in those chapters. The eighth chapter provides readers with matrices and Eigenvalues and Eigenvectors. The book finishes with a complete overview of differential equations. Provides examples and problems of solving electronic circuits and neural networksIncludes new sections on adaptive filters, recursive least-squares estimation, Bairstow's method for a polynomial equation, and moreExplains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectorsAimed at students who do not like and/or do not have time to derive and prove mathematical results Applied Numerical Methods Using MATLAB®, Second Edition is an excellent text for students who wish to develop their problem-solving capability without being involved in details about the MATLAB codes. It will also be useful to those who want to delve deeper into understanding underlying algorithms and equations.
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Algorithms and Architectures for Parallel Processing
The six-volume set, LNCS 15251-15256, constitutes the refereed proceedings of the 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024, held in Macau, China, during October 29–31, 2024.The 91 full papers, 35 short papers and 5 workshop papers included in these proceedings were carefully reviewed and selected from 265 submissions. They focus on the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems.
R 2,951.58
Advances in Computing and Data Sciences
This two-volume set (CCIS 1045 and CCIS 1046) constitutes the refereed proceedings of the Third International Conference on Advances in Computing and Data Sciences, ICACDS 2019, held in Ghaziabad, India, in April 2019.The 112 full papers were carefully reviewed and selected from 621 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations.
R 2,806.42
Medical Image Computing and Computer Assisted Intervention – MICCAI 2025
The 16-volume set LNCS 15960 - 15975 constitutes the refereed proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea, during September 23-27, 2025. The total of 1027 papers included in the proceedings was carefully reviewed and selected from 3447 submissions. They were organized in topical parts as follows: Part I, LNCS Volume 15960: Multimodal Fusion and Contextual Reasoning in Medical Imaging Part II, LNCS Volume 15961: Surgical Navigation, Scene Understanding, and Video Modeling Part III, LNCS Volume 15962: Learning and Augmented Reality for Surgical and Endoscopic Applications (I) Part IV, LNCS Volume 15963: Learning and Augmented Reality for Surgical and Endoscopic Applications (II) Part V, LNCS Volume 15964: Graph-Based Methods in Medical Imaging Part VI, LNCS Volume 15965: Datasets and Methods for Image Quality Enhancement Part VII, LNCS Volume 15966: Trustworthy and Responsible AI for Medical Imaging Part VIII, LNCS Volume 15967: Multimodal Learning for Diagnosis, Risk Prediction, and Survival Analysis Part IX, LNCS Volume 15968: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (I) Part X, LNCS Volume 15969: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (II) Part XI, LNCS Volume 15970: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (III) Part XII, LNCS Volume 15971: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (IV) Part XIII, LNCS Volume 15972: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (I) Part XIV, LNCS Volume 15973: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II) Part XV, LNCS Volume 15974: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (III) Part XVI, LNCS Volume 15975: Statistical Techniques in Medical Imaging: Causality, Imputation, Weak Supervision, and Other Methods
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Machine and Deep Learning Using MATLAB
MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning)Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response)Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing stepsRetraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2025
The 16-volume set LNCS 15960 - 15975 constitutes the refereed proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea, during September 23-27, 2025. The total of 1027 papers included in the proceedings was carefully reviewed and selected from 3447 submissions. They were organized in topical parts as follows: Part I, LNCS Volume 15960: Multimodal Fusion and Contextual Reasoning in Medical Imaging Part II, LNCS Volume 15961: Surgical Navigation, Scene Understanding, and Video Modeling Part III, LNCS Volume 15962: Learning and Augmented Reality for Surgical and Endoscopic Applications (I) Part IV, LNCS Volume 15963: Learning and Augmented Reality for Surgical and Endoscopic Applications (II) Part V, LNCS Volume 15964: Graph-Based Methods in Medical Imaging Part VI, LNCS Volume 15965: Datasets and Methods for Image Quality Enhancement Part VII, LNCS Volume 15966: Trustworthy and Responsible AI for Medical Imaging Part VIII, LNCS Volume 15967: Multimodal Learning for Diagnosis, Risk Prediction, and Survival Analysis Part IX, LNCS Volume 15968: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (I) Part X, LNCS Volume 15969: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (II) Part XI, LNCS Volume 15970: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (III) Part XII, LNCS Volume 15971: Core Techniques in Medical Imaging: Segmentation, Registration, Synthesis, Reconstruction, and Other Emerging Methods (IV) Part XIII, LNCS Volume 15972: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (I) Part XIV, LNCS Volume 15973: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (II) Part XV, LNCS Volume 15974: Adapting Foundation Models for Medical Imaging: LLMs, VLMs, and Cross-Domain Generalization (III) Part XVI, LNCS Volume 15975: Statistical Techniques in Medical Imaging: Causality, Imputation, Weak Supervision, and Other Methods
R 3,582.83
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
R 4,393.24
Introduction to Public Key Infrastructures
The introduction of public key cryptography (PKC) was a critical advance in IT security. In contrast to symmetric key cryptography, it enables confidential communication between entities in open networks, in particular the Internet, without prior contact. Beyond this PKC also enables protection techniques that have no analogue in traditional cryptography, most importantly digital signatures which for example support Internet security by authenticating software downloads and updates. Although PKC does not require the confidential exchange of secret keys, proper management of the private and public keys used in PKC is still of vital importance: the private keys must remain private, and the public keys must be verifiably authentic. So understanding so-called public key infrastructures (PKIs) that manage key pairs is at least as important as studying the ingenious mathematical ideas underlying PKC. In this book the authors explain the most important concepts underlying PKIs and discuss relevant standards, implementations, and applications. The book is structured into chapters on the motivation for PKI, certificates, trust models, private keys, revocation, validity models, certification service providers, certificate policies, certification paths, and practical aspects of PKI.This is a suitable textbook for advanced undergraduate and graduate courses in computer science, mathematics, engineering, and related disciplines, complementing introductory courses on cryptography. The authors assume only basic computer science prerequisites, and they include exercises in all chapters and solutions in an appendix. They also include detailed pointers to relevant standards and implementation guidelines, so the book is also appropriate for self-study and reference by industrial and academic researchers and practitioners.
R 2,731.65
Computer Aided Learning and Instruction in Science and Engineering
This book constitutes the refereed proceedings of the Third International Conference on Computer Aided Learning and Instruction in Science and Engineering, CALICSE '96, held in San Sebastián, Spain in July 1996.The 42 revised full papers presented in the book were selected from a total of 134 submissions; also included are the abstracts of full papers of four invited talks and 17 poster presentations. The papers are organized in topical sections on learning environments: modelling and design, authoring and development tools and techniques, CAL in distance learning, multimedia and hypermedia in CAL, and applications in science and engineering.
R 1,588.56
Structure of the Relational Database Model
This book presents an overview of the most fundamental aspects of the theory that underlies the Relational Database Model. As such it is self-contained though experience with formal models and abstract data manipulating on the one hand and with the practical use of a relational system on the other hand can help the reader. Such experience will offer the reader a better understanding of and a motivation for the different concepts, theories and results mentioned in the book. We have focussed on the most basic concepts and aspects of the relational model, without trying to give a complete overview of the state of the art of database theory. Recently a lot of books on databases in general and on the relational model in particular have been published. Most of them describe the use of database systems. 'Some clarify how information has to be structured and organized before it can be used to build applications. Others help the user in writing down his applications or in finding tricky ways to optimize the running time or the necessary space. Another category of books treat more fundamental and more general aspects such as the description of the relational model, independent of any implementation, the decomposition in normal forms or the global design of distributed databases. Few, however, are the books that describe in a formal way some of the subjects mentioned above.
R 1,635.10
