Data mining Books
Springer International Publishing AG Intelligent Systems: 12th Brazilian Conference,
Book SynopsisThe three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows:Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis;Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. Table of ContentsMulti-objective Genetic Algorithms Applied to the Optimization of Expanded Genetic Codes.- Genetic Algorithms with Optimality Cuts to the Max-Cut Problem.- Assessment of robust multi-objective evolutionary algorithms on robust and noisy environments.- Binary Flying Squirrel Optimizer for Feature Selection.- Fitness Landscape Analysis of TPOT using Local Optima Network.- Optimization Strategies for BERT-based Named Entity Recognition.- FlexCon-CE: A Semi-supervised Method with an Ensemble-based Adaptive Confidence.- Single Image Super-Resolution Based on Capsule Neural Networks.- Development of a Deep Learning Model for the Classification of Mosquito Larvae Images.- A Simple and Low-cost Method for Leaf Surface Dimension Estimation Based on Digital Images.- Crop Row Line Detection with Auxiliary Segmentation Task.- Multiple object tracking in native bee hives: A case study with Jataí in the field.- An Open Source Eye Gaze Tracker system to perform remote user testing evaluations.- Who Killed the Winograd Schema Challenge?Sabiá: Portuguese Large Language Models.- Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese.- Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain.- LegalBert-pt: A Pretrained Language Model for the Brazilian Portuguese Legal Domain.- A Framework for Controversial Political Topics Identification using Twitter Data.- Leveraging Sign Language Processing with Formal SignWriting and Deep Learning ArchitecturesA clustering validation index based on semantic description.- Detecting Multiple Epidemic Sources in Network Epidemics using Graph Neural Networks.- Prediction of cancer-related miRNA targets using an integrative heterogeneous Graph Neural Network-based method.- Time series forecasting of COVID-19 cases in Brazil with GNN and mobility networks.- Federated Learning and Mel-spectrograms for Physical Violence Detection in Audio.- Police Report Similarity Search: a case study.- Evaluating Contextualized Embeddings for Topic Modeling in Public Bidding Domain.- A Tool for Measuring Energy Consumption in Data Stream Mining.- Improved Fuzzy Decision System for Energy Bill Reduction in the Context of the Brazilian White Tariff Scenario.- Exploring Artificial Intelligence methods for the automatic measurement of a new biomarker aiming at glaucoma diagnosis.- Investigation of deep Active Self-Learning algorithms applied to named entity recognition.
£61.74
Springer International Publishing AG Advanced Data Mining and Applications: 19th
Book SynopsisThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.Table of ContentsPharmaceutical Data Analysis.- Drug-target interaction prediction based on drug subgraph fingerprint extraction strategy and subgraph attention mechanism.- Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding.- Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction.- MTFL: Multi-task feature learning with joint correlation structure learning for Alzheimer’s disease cognitive performance prediction.- Multi-Level Transformer for Cancer Outcome Prediction in Large-Scale Claims Data.- Individual Functional Network Abnormalities Mapping via Graph Representation-based Neural Architecture Search.- A novel application of a mutual information measure for analysing temporal changes in healthcare network graphs.- Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation.- Text Classification.- ParaNet:Parallel Networks with Pre-trained Models for Text Classification.- Open Text Classification Based on Dynamic Boundary Balance.- A Prompt Tuning Method for Chinese Medical Text Classification.- TabMentor: Detect Errors on Tabular Data with Noisy Labels.- Label-aware Hierarchical Contrastive Domain Adaptation for Cross-network Node Classification.- Semi-supervised classification based on Graph Convolution Encoder Representations from BERT.- Global Balanced Text Classification for Stable Disease Diagnosis.- Graph.- Dominance Maximization in Uncertain Graphs.- LAGCL: Towards Stable and Automated Graph Contrastive Learning.- Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model.- Common-Truss-based Community Search on Multilayer Graphs.- Learning To Predict Shortest Path Distance.- Efficient Regular Path Query Evaluation with Structural Path Constraints.EnSpeciVAT: Enhanced SpeciVAT for Cluster Tendency Identification in Graphs.- Pessimistic Adversarially Regularized Learning for Graph Embedding.- M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning.
£56.99
Springer International Publishing AG Big Data and Artificial Intelligence: 11th
Book SynopsisThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. Table of ContentsKeynote Lectures.- Representation Learning for Dialog Models.- Sparsity, modularity, and structural plasticity in deep neural networks.- Artificial Intelligence in Healthcare.- Tuberculosis disease diagnosis using controlled super resolution.- GREAT AI in Medical Appropriateness and Value-Based-Care.- Large Language Models.- KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models.- SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q&A.- Revolutionizing High School Physics Education: A Novel Dataset.- Context-Enhanced Language Models for Generating Multi-Paper Citations.- GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs & Scholarly Papers.- Data Analytics for Low Resource Domains.- A Deep Learning Emotion Classification Framework for Low Resource Languages.- Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains.- Artificial Intelligence for Innovative Applications.- Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks.- IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI.- Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach.- Potpourri.- Fast similarity search in large-scale Iris databases using high-dimensional hashing.- Explaining Finetuned Transformers on Hate Speech Predictions using Layerwise Relevance Propagation.- Multilingual Speech Sentiment Recognition using Spiking Neural Networks.- FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal.- A Review of Approaches on Facets for Building IT-based Career Guidance Systems.
£47.49
Springer International Publishing AG Recent Trends and Future Challenges in Learning
Book SynopsisThis book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: “Avoiding drowning in the data: recent trends and future challenges in learning from data”. The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.Table of ContentsPreface.- Building hierarchies of factors with disjoint factor analysis.- Uncertainty in Latent Trait Models and dimensionality reduction methods for complex data: an analysis of taxpayer perception on the Fiscal System.- The predictivity of access tests for university success.- Asynchronous and synchronous-asynchronous particle swarms.- The impact of the Covid-19 pandemic on modelling volatility and risk analysis of returns in selected European financial markets.- Asymmetric binary regression models for imbalanced datasets: an application to students’ churn.- Computational models supporting decision-making in managing publication activity at Polish universities.- Stability of nonparametric methods for cognitive diagnostic assessment.- SMARTS: SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment.- The equitable and sustainable wellbeing through the pandemic. A first study to assess changes at local level in Italy.- Choice-Based Optimization under High-Dimensional MNL.- A first glance on co-evolution of Boolean networks to simulate the development of cross-talking systems in molecular biology.- Classification on polish fund market during COVID-19 pandemic - extreme risk modeling approach.
£87.99
Springer Computational Intelligence in Data Science
Book Synopsis.- Applications of AI/ML in Natural Language Processing..- Analyzing the Computational Efficiency of LLM Models for NLP Tweet classification during emergency crisis..- Drug Sentiment Analysis: A Comprehensive Study using Regression Models and Natural Language Processing..- Chatbot In Banking Sector Using Machine Learning and Natural Language Processing..- Lecter - A Large Language Model Chatbot for Cognitive Behavioral Therapy..- Evaluating the Language Translation Accuracy of GPT 3.5 using Prompt Engineering..- Multi-Camera Enhanced Real-Time Content- Aware Vehicle Detection..- COOL: Classification of Online Offensive Language using Machine Learning and Deep Learning..- Improved Evaluator for Subjective Answers Using Natural Language Processing..- Self-Harm Detection from Texts: A Comparative Study Utilizing BERT, Machine Learning, and Deep Learning Approaches..- Neuro-evolution based Language Model for Text Generation..-
£89.99
Springer Computational Intelligence in Data Science
Book Synopsis.- Applications of AI/ML in KDM, Cloud Computing & Security..- Healthify App Using Blockchain with Cloud..- A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System..- Intrusion Detection System Trends: An Overview of Current Advances in IoV & Communication Networks..- Automation Xtreme -A web automation AI Tool..- Defending the Digital Frontier URL-based Phishing Detection Extension..- Guarding the Digital Frontier: A Logistic Regression Approach to Malware Detection..- Hybrid Efficient IDS Against Adversarial Attacks in IoT Networks..- Data Analytics..- Real-Time Soil Moisture Sensing using Arduino for Automated Plant Irrigation System..- Campus Placement and Salary Prediction: Leveraging Machine Learning for Enhanced Employability..- Exploring Corrosion Detection: Deep Learning and Ensemble Approaches Analysis..- Comic Generation using AI - A Review..- Resid
£98.99
Springer Advanced Hybrid Information Processing
Book Synopsis.- A Graph Neural Network-based Deformation Monitoring Method for Supertall Buildings..- A Graph Neural Network-based Method for Detailed Feature Enhancement of UAV Aerial Images..- Anomaly Detection for Massive Data of Network Transmission Time Series Based on Graph Neural Network..- A Study on Adaptive Push of Agricultural Products Marketing Information Based on Combinatorial Neural Network..- A Deep Support Vector Machine-based Outlier Detection Method for Ocean Buoy Data..- A Deep Learning-based Method for Forecasting Retail Prices of Internationally Traded Goods..- Enterprise Information Fusion and Security Audit Method Based on DBSCAN Clustering..- An Isolated Random Forest Based Intrusion Detection Method for Wireless Network Nodes..- A Deep Learning Based Method for Detecting Outliers in a Database of Ideological and Political Education System..- A Balanced Scheduling Technique for Distributed Inference Resources Based on Edge Computing..- A Study on Optimal Motion Path Planning for Rural Logistics and Transportation Vehicles Based on Deep Reinforcement Learning..- A Deep Reinforcement Learning-Based Method for Economic Dispatch of Integrated Energy System..- A Financial Audit Data Integrity Verification Method Based on Differential Evolution Algorithm..- A Deep Reinforcement Learning-based Approach for Intelligent Recommendation of Digital Museums..- An Automatic English Online Translation Error Recognition Method Based on Reinforcement Learning and Evolutionary Computation..- A Personalized Resource Recommendation Method for Laboratory-Integrated Civics Teaching Based on Multi-source Heterogeneous Information Fusion..- A Deep Integrated Learning Mining Algorithm for Digital Trade Talent Development Model Labeled Demand Information..- A Multi-Objective Optimization Model for Joint Construction Parameters of Prefabricated Buildings Based on Improved Bee Colony Algorithm..- A Hierarchical Recurrent Genetic Algorithm-based Approach for Emotion Recognition of Physiological Signals in Badminton Players..- Classification of Difficult Movements in Competitive Aerobics Based on Quantum Genetic Algorithm..- Research on Multiple Backup Method for Enterprise Financial Data Based on Active Learning Algorithm..- A Chaotic Mapping-based Approach for Privacy-encrypted Storage of Information in Storage and Transportation Logistics Databases..- Native 3D Diffusion Networks Architectures, Optimization, and Emerging Trends in Generative Modelling..- Applications and Intelligent Systems..- The Design of an Online Teaching System for Independent Learning Network under Intelligent Cloud Architecture..- Design of Visual New Media Generation Art Interaction System Based on Processing and GPU..- A Real-time Control Method for Linked CNC Systems Based on Human-machine Hybrid Augmented Intelligence..- Design of Network Training Teaching Platform Based on Feedforward Neural Network and Virtual Simulation..- Research on the Effectiveness Assessment Method of E-commerce-enabled Rural Revitalization Based on Convolutional Neural Network..- The Digitalization Construction Path of Enterprise Financial Management Based on Cloud Computing Technology..- An Intelligent Fault Prediction Method for Distribution Grid Transformers Based on Digital Twins and Deep Learning.
£71.99
Springer International Publishing AG Recommender Systems: The Textbook
Book SynopsisThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.Trade Review“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)Table of ContentsAn Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
£44.99
Springer International Publishing AG Introduction to Computational Social Science: Principles and Applications
Book SynopsisThis textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.Trade Review“This book is organized in a rigorous manner: each chapter includes an introductory abstract, a short chronology of the main achievements related to the chapter’s topic, well-balanced formalized-intuitive knowledge content, a significant number of questions … and finally a list of future readings. … I think Claudio Cioffi-Revilla’s work hits its assumed target: to be an affordable textbook for students and, at the same time, a useful support manual for instructors interested in learning or teaching computational social science.” (Valentin V. Inceu, Computing Reviews, February, 2019)“This well-organized book provides a timely and comprehensive systematic introduction to CSS. The chapters are relatively independent. Therefore, readers may quickly grasp related information by reading chapters selectively. … this book is intended as a CSS textbook for graduate students … .” (Chenyi Hu, Computing Reviews, August 11, 2014) Table of ContentsIntroductionComputation and Social ScienceAutomated Information ExtractionSocial NetworksSocial Complexity I: Origins and MeasurementSocial Complexity II: LawsSocial Complexity III: TheoriesSimulations I: MethodologySimulations II: Variable-Oriented ModelsSimulations III: Object-Oriented Models
£92.10
Springer International Publishing AG Algorithmic Intelligence: Towards an Algorithmic
Book SynopsisIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.Part I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. A key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. The highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. Finally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.The book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.Table of ContentsPreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References
£170.99
Springer International Publishing AG ICT Innovations 2017: Data-Driven Innovation. 9th
Book SynopsisThis book constitutes the refereed proceedings of the 9th International Conference on Data-Driven Innovation, ICT Innovations 2017, held in Skopje, Macedonia, in September 2017. The 26 full papers presented were carefully reviewed and selected from 90 submissions. They cover the following topics: big data analytics, cloud computing, data mining, digital signal processing, e-health, embedded systems, emerging mobile technologies, multimedia, Internet of Things (IoT), machine learning, software engineering, security and cryptography, coding theory, wearable technologies, wireless communication, and sensor networks.Table of ContentsData-driven innovations, organized around topics such as increasing migration of socio-economic activities to the Internet.- The decline in the cost of data collection, storage and processing.- The generation and use of huge volumes of data.- Large datasets becoming a core asset in research and economy fostering new discoveries, new industries, new processes.
£42.74
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Uncertainty Modeling for Data Mining: A Label
Book SynopsisMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
£80.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Lehrbuch In-Memory Data Management: Grundlagen
Book SynopsisNeueste Errungenschaften in der Hard-und Software-Entwicklung, wie z. B. Multi-Core-CPUs und DRAM-Kapazitäten von mehreren Terabyte pro Server, förderten die Einführung einer revolutionären Technologie: das In-Memory Data Management. Diese Technologie unterstützt die flexible und extrem schnelle Analyse großer Mengen von Unternehmensdaten. Professor Hasso Plattner und seine Arbeitsgruppe am Hasso-Plattner-Institut in Potsdam, Deutschland, lehren die entsprechenden Konzepte seit Jahren und sorgen für ihre Sichtbarkeit in der Software-Industrie. Dieses Buch basiert auf dem ersten Online-Kurs der openHPI E-Learning-Plattform, die im Herbst 2012 mit mehr als 13.000 Lernenden ins Leben gerufen wurde. Das Buch richtet sich an Studierende der Informatik, speziell mit dem Schwerpunkt Software Engineering. sowie an Business-Experten, Entscheider, Software-Entwickler, IT-Experten und IT-Analysten. Themen sind - unter anderem - die physische Datenspeicherung und der Zugang, grundlegende Datenbank-Betreiber, Kompressions-Mechanismen und Algorithmen. Darüber hinaus werden Implikationen für zukünftige Enterprise-Anwendungen und deren Entwicklung diskutiert. Die Leser lernen, die radikalen Unterschiede und Vorteile der neuen Technologie gegenüber herkömmlichen zeilenorientierten und disk-basierten Datenbanken zu verstehen.Trade Review"... richtet sich folglich primär an Fachpublikum in der Praxis, bietet jedoch auch für Personen in angrenzenden Anwendungsbereichen einen fundierten Einstieg in die Materie." (in: Managementkompass In-Memory-Analytics, Heft 1, 2015)Table of ContentsDie Zukunft des Enterprise Computing.- Grundlagen der Datenbank-Speicher-Techniken.- Betreiber.- Speichertechniken.- Startschuss für eine neue Ära.
£36.09
Springer Fachmedien Wiesbaden Beobachtungsmöglichkeiten im Domain Name System:
Book SynopsisDominik Herrmann zeigt, dass die Betreiber von Nameservern, die im Internet zur Auflösung von Domainnamen in IP-Adressen verwendet werden, das Verhalten ihrer Nutzer detaillierter nachvollziehen können als bislang gedacht. Insbesondere können sie maschinelle Lernverfahren einsetzen, um einzelne Internetnutzer an ihrem charakteristischen Verhalten wiederzuerkennen und über lange Zeiträume unbemerkt zu überwachen. Etablierte Verfahren eignen sich allerdings nicht zur Anonymisierung der Namensauflösung. Daher schlägt der Autor neue Techniken zum Selbstdatenschutz vor und gibt konkrete Handlungsempfehlungen.Table of ContentsGrundlagen des Domain Name System, relevante Bedrohungen und etablierte Sicherheitsmechanismen.- Beobachtungsmöglichkeiten im Domain Name System: Rekonstruktion der besuchten Webseiten und der verwendeten Software sowie verhaltensbasierte Verkettung von Sitzungen.- Techniken zum Schutz vor Beobachtung und Verkettung mittels datenschutzfreundlicher Techniken.
£49.49
Springer Fachmedien Wiesbaden Social-Media-Analyse – mehr als nur eine
Book SynopsisDie Autoren legen beispielhafte Analysemethoden von Social-Media-Daten dar: deskriptive und Data-Mining-Methoden. Mit deren Hilfe werden kundenorientierte Geschäftsmaßnahmen eingeleitet und ein stetiges Abwägen zwischen vollautomatisierten und manuellen, kostenintensiven Reports gesteuert. Das Werk liefert eine Übersicht zu aktuell diskutierten Themen wie begleitende Emotionen, Vernetzung der interagierenden User oder Verbindung von Themen. Als Gewinn für ein Unternehmen müssen die Analysen durch eine strategische Prozedur geleitet werden, um Erkenntnisse in konkrete Handlungsempfehlungen zu überführen. Neben den Potenzialen durch die Anwendung komplexerer Analysemethoden gibt es auch konzeptionelle, technische und ethische Herausforderungen, wie die Autoren veranschaulichen.Trade Review Table of Contents
£11.77
Springer Fachmedien Wiesbaden Netzbasierte Ansätze zur natürlichsprachlichen
Book SynopsisFür Leser, die bereits die Grundlagen der Wissensverarbeitung und Computernetzwerke beherrschen, gibt das Buch einen Überblick über innovative Verfahren, die die automatisierte Suche, Recherche, Klassifikation und Verwaltung von Texten im Kontext dezentraler Systeme und vor allem im WWW erlauben. Besondere Aufmerksamkeit wird dabei auf eine personalisierte Verarbeitung gerichtet, die auch zeitliche Aspekte, wie z. B. das digitale Vergessen, einbeziehen. An vielen Stellen werden auf interessante und neuartige Art und Weise Analogien aus anderen Wissensgebieten, so z. B. zur Verarbeitung von Informationen und zum Lernen im menschlichen Gehirn sowie der Natur schlechthin genutzt.Table of ContentsWissensverarbeitung im menschlichen Gehirn - Lernen - Netzwerke für die Textanalyse - Digitale Updates und digitales Vergessen - Exploration von Netzwerkstrukturen - Konzepte des Text Minings in dezentralen Systemen - Informationsmanagement im Web
£26.59
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Transactions on Large-Scale Data- and
Book SynopsisThe LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.Table of ContentsClock-G: Temporal graph management system.- TSPredIT: Integrated tuning of data preprocessing and time series prediction models.- A guide to the Tucker tensor decomposition for data mining: exploratory analysis, clustering and classification.- Challenges for Healthcare Data Analytics over Knowledge Graphs.- From Database Repairs to Causality in Databases and Beyond.
£49.49
Springer Verlag, Singapore Deep Reinforcement Learning: Fundamentals, Research and Applications
Book SynopsisDeep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Table of Contents
£139.99
Springer Verlag, Singapore Text Data Mining
Book SynopsisThis book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.Table of ContentsChapter 1. Introduction.- Chapter 2. Data Annotation and Preprocessing.- Chapter 3. Text Representation.- Chapter 4. Text Representation with Pretraining and Fine-tuning.- Chapter 5. Text classification.- Chapter 6. Text Clustering.- Chapter 7. Topic Model.- Chapter 8. Sentiment Analysis and Opinion Mining.- Chapter 9. Topic Detection and Tracking.- Chapter 10. Information Extraction.- Chapter 11. Automatic Text Summarization.
£49.49
Springer Verlag, Singapore Enabling Smart Urban Services with GPS Trajectory
Book SynopsisWith the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.Table of Contents1. Trajectory data map-matching 1.1 Introduction 1.2 Definitions and problem formulation 1.3 SD-Matching algorithm 1.4 Evaluations 1.5 Conclusions and discussions 2. Trajectory data compression 2.1 Introduction 2.2 Basic concepts and system overview 2.3 HCC algorithm 2.4 System implementation 2.5 Evaluations 2.6 Conclusions 3. Trajectory data protection 3.1 Introduction 3.2 Preliminary 3.3 Trajectory protection mechanism 3.4 Performance evaluations 3.5 Conclusions Part II: Enabling Smart Urban Services: Travellers 4. TripPlanner: Personalized trip planning leveraging heterogeneous trajectory data 4.1 Introduction 4.2 TripPlanner System 4.3 Dynamic network modelling 4.4 The two-phase approach 4.5 System evaluations 4.6 Conclusions and future work 5. ScenicPlanner: Recommending the most beautiful driving routes 5.1 Introduction 5.2 Preliminary 5.3 The two-phase approach 5.4 Experimental evaluations 5.5 Conclusion and future work Part III: Enabling Smart Urban Services: Drivers 6. GreenPlanner: Planning fuel-efficient driving routes 6.1 Introduction 6.2 Basic concepts and problem formulation 6.3 Personal fuel consumption model building 6.4 Fuel-efficient driving route planning 6.5 Evaluations 6.6 Conclusions and future work 7. Hunting or waiting: Earning more by understanding taxi service strategies 7.1 Introduction 7.2 Empirical study 7.3 Taxi strategy formulation 7.4 Understanding taxi service strategies 7.5 Conclusions Part IV: Enabling Smart Urban Services: Passengers 8. iBOAT: Real-time detection of anomalous taxi trajectories from GPS traces 8.1 Introduction 8.2 Preliminaries and problem definition 8.3 Isolation-based online anomalous trajectory detection 8.4 Empirical evaluations 8.5 Fraud behaviour analysis 8.6 Conclusions and future work 9. Real-Time imputing trip purpose leveraging heterogeneous trajectory data 9.1 Introduction 9.2 Basic concepts and problem statement 9.3 Imputing trip purposes 9.4 Enabling real-time response 9.5 Evaluations 9.6 Conclusions and future work Part V: Enabling Smart Urban Services: Urban Planners 10. GPS environment friendliness estimation with trajectory data 10.1 Introduction 10.2 Basic concepts 10.3 Methodology 10.4 Experiments 10.5 Limitations and future work 10.6 Conclusions 11. B-Planner: Planning night bus routes using taxi trajectory data 11.1 Introduction 11.2 Candidate bus stop identification 11.3 Bus route selection 11.4 Experimental evaluations 11.5 Conclusions and future work 12. VizTripPurpose: Understanding city-wide passengers’ travel behaviours 12.1 Introduction 12.2 System overview 12.3 Trip2Vec model 12.4 User interfaces 12.5 Case studies 12.6 Conclusions and future work Part VI: Enabling Smart Urban Services: Beyond People Transportation 13. CrowdDeliver: Arriving as soon as possible 13.1 Introduction 13.2 Basic concepts, assumptions and problem statement 13.3 Overview of CrowdDeliver 13.4 Two-phase approach 13.5 Evaluations 13.6 Conclusions and future work 14. CrowdExpress: Arriving by the user-specified deadline 14.1 Introduction 14.2 Preliminary, problem statement and system overview 14.3 Offline package transport network building 14.4 Online taxi scheduling and package routing 14.5 Experimental evaluations 14.6 Conclusions and future work Part VII: Open Issues and Conclusions 15. Open Issues 16. Conclusions
£132.99
Springer Verlag, Singapore Data Science: 7th International Conference of
Book SynopsisThis 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.Table of ContentsBig 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.
£98.99
Springer Verlag, Singapore Data Science: 7th International Conference of
Book SynopsisThis 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.Table of ContentsSocial Media and Recommendation Systems.- Data Security and Privacy.- Applications of Data Science.- Education research, methods and materials for data science and engineering.- Research demo.
£80.99
Springer Verlag, Singapore Network Behavior Analysis: Measurement, Models,
Book SynopsisThis book provides a comprehensive overview of network behavior analysis that mines Internet traffic data in order to extract, model, and make sense of behavioral patterns in Internet “objects” such as end hosts, smartphones, Internet of things, and applications. The objective of this book is to fill the book publication gap in network behavior analysis, which has recently become an increasingly important component of comprehensive network security solutions for data center networks, backbone networks, enterprise networks, and edge networks.The book presents fundamental principles and best practices for measuring, extracting, modeling and analyzing network behavior for end hosts and applications on the basis of Internet traffic data. In addition, it explains the concept and key elements (e.g., what, who, where, when, and why) of communication patterns and network behavior of end hosts and network applications, drawing on data mining, machine learning, information theory, probabilistic graphical and structural modeling to do so. The book also discusses the benefits of network behavior analysis for applications in cybersecurity monitoring, Internet traffic profiling, anomaly traffic detection, and emerging application detections.The book will be of particular interest to researchers and practitioners in the fields of Internet measurement, traffic analysis, and cybersecurity, since it provides a spectrum of innovative techniques for summarizing behavior models, structural models, and graphic models of Internet traffic, and explains how to leverage the results for a broad range of real-world applications in network management, security operations, and cyber-intelligent analysis. After finishing this book, readers will 1) have learned the principles and practices of measuring, modeling, and analyzing network behavior on the basis of massive Internet traffic data; 2) be able to make sense of network behavior for a spectrum of applications ranging from cybersecurity and network monitoring to emerging application detection; and 3) understand how to explore network behavior analysis to complement traditional perimeter-based firewall and intrusion detection systems in order to detect unusual traffic patterns or zero-day security threats using data mining and machine learning techniques. To ideally benefit from this book, readers should have a basic grasp of TCP/IP protocols, data packets, network flows, and Internet applications.Table of ContentsChapter 1: Introduction.- Chapter 2: Background of Network Behavior Modeling and Analysis.- Chapter 3: Behavior Modeling of Network Traffic.- Chapter 4: Structural Modeling of Network Traffic.- Chapter 5: Graphic Modeling of Network Traffic.- Chapter 6: Real-Time Network Behavior Analysis.- Chapter 7: Applications.- Chapter 8: Research Frontiers of Network Behavior Analysis.
£113.99
Springer Verlag, Singapore Knowledge Discovery from Multi-Sourced Data
Book SynopsisThis book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “label” or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.Table of ContentsChapter 1 Introduction 1.1 Knowledge Discovery 1.2 Main Challenges 1.3 Book Overview Chapter 2 Functional-dependency-based truth discovery for isomorphic data 2.1 Handling independent constraints 2.2 Handling inter-related constraints 2.3 Inter-source data aggregation 2.4 Update source weights Chapter 3 Denial-constraint-based truth discovery for isomorphic data Describe the truth discovery strategies for isomorphic data based on denial constraints 4.1 Denial constraint transformation 4.2 Optimized solution 4.3 Scalable strategies Chapter 4 Pattern discovery for heterogeneous data 4.1 Problem definition for multi-source heterogeneous data 4.2 Optimization framework 4.3 PatternFinder algorithm 4.4 The optimized grouping strategy Chapter 5 Deep fact discovery for text data 5.1 Fact extraction via mining patterns 5.2 The CNN-LSTM architecture 5.3 The fact encoder and pattern embedding 5.4 Training and inference
£42.74
Springer Verlag, Singapore Computational Methods and Data Engineering:
Book SynopsisThe book features original papers from International Conference on Computational Methods and Data Engineering (ICCMDE 2021), organized by School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, during November 25–26, 2021. The book covers innovative and cutting-edge work of researchers, developers, and practitioners from academia and industry working in the area of advanced computing.Table of ContentsChapter 1. A Graph Based Extractive Assamese Text Summarization.- Chapter 2. Internet of Things (IoT) for Secure Data and M2M Communications.- Chapter 3. Deveelopment of Walking Assistants for Visually Challenged Person.- Chapter 4. A PERFORMANCE STUDY OF PREDICTION MODELS FOR DIABETES PREDICTION USING MACHINE LEARNING.- Chapter 5. Orthopantomogram (OPG) Image Analysis using Bounding Box Algorithm.- Chapter 6. Design and Analysis of an Improved Artificial Neural Network Controller for the Energy Efficiency Enhancement of Wind Power Plant.- Chapter 7. Detection of Renal Calculi using Convolutional Neural Networks.- Chapter 8. Question Answering and Text Generation using BERT and GPT-2 Model.- Chapter 9. Improved Lenet Model for Flower Classification Using GPU Computing.- Chapter 10. Distributed computing over the Next Generation Mobile Communication Network (NGMCN) is an emerging technology.- Chapter 11. The ELF Tribe: Redefining Primary Education in a Post-COVID Era.- Chapter 12. An Extreme Machine Learning Mdel for Evaluating Landslide Hazard Zonation in Nilgiris District, Causative Factors and Risk Assessment using Earth Observation Techniques.- Chapter 13. Analysis of Cross-Site Scripting Vulnerabilities in Various Day-to-Day Web Applications.- Chapter 14. Detecting Cyberbullying with text classification using 1DCNN and Glove Embeddings.- Chapter 15. A Bayesian Network Based Software Requirement Complexity Prediction Model.
£161.99
Springer Verlag, Singapore Smart Data Intelligence: Proceedings of ICSMDI
Book SynopsisThis book presents high-quality research papers presented at 2nd International Conference on Smart Data Intelligence (ICSMDI 2022) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during April 2022. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, healthcare, information, and communication applications.Table of ContentsDetection and Analysis of Sentiments on Twitter Using Machine Learning Algorithm.- Malware Attack Detection on IoT Devices Using Machine Learning.- BeSafe: IoT Based Safety Band.- Stock Market Analysis and forecasting with Statistical and Deep Learning Methods.- The paradigm shift in Higher Education from traditional learning to digitalisation.- Designing a socially intelligent system by cognitive modeling of human-environment interaction.- Web-Based recycle Waste Management for Ecommerce.
£208.99
Springer Verlag, Singapore Proceedings of International Conference on Data
Book SynopsisThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.Table of ContentsCancer Prognosis by Using Machine Learning and Data Science: A systematic review.- Understanding Agriculture Scenario of Punjab: A Qualitative Research of Crop parameter in relation to fertilizer usage.- Face Detection Based Border Security System Using Haar-Cascade and LBPH Algorithm.- Proposed Experimental Design of a Portable COVID-19 Screening Device Using Cough Audio Samples.- Big Data Framework for Analytics Business Intelligence.- Technological Impacts of AI on Hospitality and Tourism Industry.- Improvement of Real-Time Kinematic Positioning Using Kalman Filter-Based Singular Spectrum Analysis during Geomagnetic Storm for Thailand sector.
£189.99
Springer-Verlag GmbH Neural Information Processing
£71.99
Springer-Verlag GmbH Neural Information Processing
£71.99
Springer-Verlag GmbH Neural Information Processing
£71.99
Springer-Verlag GmbH Neural Information Processing
£71.99
Springer Neural Information Processing
Book Synopsis
£61.74
Springer Nature Switzerland AG Neural Information Processing
Book Synopsis
£71.99
Springer Nature Switzerland AG Neural Information Processing
Book Synopsis
£71.99
Springer Verlag, Singapore Data Science: 9th International Conference of
Book SynopsisThis 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.Table of ContentsApplications of Data Science.- Construction of Software Design and Programming Practice Course in Information and Communication Engineering.- A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture.- CAD-based Research on the Design of a Standard Unit Cabinet for Custom Furniture of the Cabinet Type.- An Improved War Strategy Optimization Algorithm for Big Data Analytics.- Research on Path Planning of Mobile Robots Based on Dyna-RQ.- Small Target Helmet Wearing Detection Algorithm based on Improved YOLO V5.- Research on Dance Evaluation Technology based on Human Posture Recognition.- Multiple-channel Weight-based CNN Fault Diagnosis Method.- Big Data Management and Applications.- Design and Implementation of Key-Value Database for Ship Virtual Test Platform Based on Distributed System.- Big Data Mining and Knowledge Management.- Research on Multi-modal Time Series Data Prediction Method Based on Dualstage Attention Mechanism.- Prediction of Time Series Data with Low Latitude Features.- Lightweight and Efficient Attention-based Superresolution Generative Adversarial Networks.- The Multisource Time Series Data Granularity Conversion Method.- Outlier Detection Model Based on Autoencoder and Data Augmentation for High-Dimensional Sparse Data.- Dimension Reduction Based on Sampling.- Complex Time Series Analysis Based on Conditional Random Fields.- Feature Extraction of Time Series Data Based on CNNCBAM.- Optimization of a Network Topology Generation Algorithm based on Spatial Information Network.- Data Visualization.- MBTIviz: A Visualization System for Research on Psycho-demographics and Personality.- Data-driven Security.- Distributed Implementation of SM4 Block Cipher Algorithm based on SPDZ Secure Multi-party Computation Protocol.- DP-ASSGD: Differential Privacy Protection Based on Stochastic Gradient Descent Optimization.- Study on Tourism Workers ’ Intercultural Communication Competence.- A Novel Federated Learning with Bidirectional Adaptive Differential Privacy.- Chaos-Based Construction of LWEs in Lattice-Based Cryptosystems.- Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve.- Infrastructure for Data Science.- Two-dimensional Code Transmission System Based on Side Channel Feedback.- An Updatable and Revocable Decentralized Identity Management Scheme based on Blockchain.- Cloud-Edge Intelligent Collaborative Computing Model based on Transfer Learning in IoT.- Design and Validation of a Hardware-in-the-loop based Automated Driving Simulation Test Platform.- Machine Learning for Data Science.- Improving Transferability Reversible Adversarial Examples based on Flipping Transformation.- Rolling Iterative Prediction for Correlated Multivariate Time Series.- Multimedia Data Management and Analysis.- Video Popularity Prediction Based on Knowledge Graph and LSTM Network.- Design and Implementation of Speech Generation and Demonstration Research Based on Deep Learning.- Testing and Improvement of OCR Recognition Technology in Export-OrientedChinese Dictionary APP.
£71.24
Springer Verlag, Singapore Neural Information Processing: 30th International
Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsText to Image Generation with Conformer-GAN.- MGFNet: A Multi-Granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-Identification.- Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution.- Hi-Stega : A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation.- Effi-Seg: Rethinking EfficientNet Architecture for Real-time Semantic Segmentation.- Quantum Autoencoder Frameworks for Network Anomaly Detection.- Spatially-Aware Human-Object Interaction Detection with Cross-Modal Enhancement.- Intelligent trajectory tracking control of unmanned parafoil system based on SAC optimized LADRC.- CATS: Connection-aware and Interaction-based Text Steganalysis in Social Networks.- Syntax Tree Constrained Graph Network for Visual Question Answering.- CKR-Calibrator: Convolution Kernel Robustness Evaluation and Calibration.- SGLP-Net: Sparse Graph Label Propagation Network for Weakly-Supervised Temporal Action Localization.- VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment.- Spiking Reinforcement Learning for Weakly-supervised Anomaly Detection.- Resource-aware DNN Partitioning for Privacy-sensitive Edge-Cloud Systems.- A frequency reconfigurable multi-mode printed antenna.- Multi-view Contrastive learning for Knowledge-aware Recommendation.- PYGC: a PinYin Language Model Guided Correction Model for Chinese Spell Checking.- Empirical Analysis of Multi-label Classification on GitterCom using BERT.- A lightweight safety helmet detection network based on bidirectional connection module and Polarized Self-Attention.- Direct Inter-Intra View Association for Light Field Super-Resolution.- Responsive CPG-Based Locomotion Control for Quadruped Robots.- Vessel Behavior Anomaly Detection using Graph Attention Network.- TASFormer: Task-aware Image Segmentation Transformer.- Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval.- TKGR-RHETNE：A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context.- High-Resolution Self-Attention with Fair Loss for Point Cloud Segmentation.- Transformer-based Video Deinterlacing Method.- SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack.- CSEC: A Chinese Semantic Error Correction Dataset for Written Correction.- Contrastive Kernel Subspace Clustering.- UATR: An Uncertainty Aware Two-stage Refinement Model for Targeted Sentiment Analysis.- AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs.- Text-based Person Re-ID by Saliency Mask and Dynamic Label Smoothing.- Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information.- Learnable Color Image Zero-Watermarking Based on Feature Comparison.- P-IoU: Accurate Motion Prediction based Data Association for Multi-Object Tracking.- WCA-VFnet:a dedicated complex forest smoke fire detector.- Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification.- Reversible Data Hiding Based on Adaptive Embedding with Local Complexity.- Generalized Category Discovery with Clustering Assignment Consistency.- CInvISP: Conditional Invertible Image Signal Processing Pipeline.- Ignored Details in Eyes: Exposing GAN-generated Faces by Sclera.- A Developer Recommendation Method Based on Disentangled.- Graph Convolutional Network.- Novel Method for Radar Echo Target Detection.
£66.49
Taylor & Francis Ltd Big Data Mining and Analytics Components of Strategic Decision Making
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£54.14
Taylor & Francis Ltd Data Mining Mobile Devices
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£54.14
Taylor & Francis Ltd InternetScale Pattern Recognition New Techniques for Voluminous Data Sets and Data Clouds
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£56.99
Taylor & Francis Ltd Data Mining for Bioinformatics
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£56.99
Taylor & Francis Ltd Clustering A Data Recovery Approach Second Edition
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£58.89
Taylor & Francis Ltd A Practitioners Guide to Resampling for Data Analysis Data Mining and Modeling
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£47.99
Taylor & Francis Ltd Data Mining in Biomedical Imaging Signaling and Systems
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£54.14
Taylor & Francis Ltd Quality Aspects in Spatial Data Mining
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£58.89
Taylor & Francis Ltd Data Analytics Applications in Gaming and
Book SynopsisThe last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject.Data Analytics Applications in Gaming andTable of ContentsPart 1 – Introduction to game data mining. Part 2 – Data mining for games user research. Part 3 – Data mining for game technology.Part 4 – Visualization of large-scale game data.
£42.74
Cambridge University Press The Text Mining Handbook
Book SynopsisPresents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world, mission-critical applications.Trade Review' … buy the book. This book is definitely worth having in your book shelf as a handy reference.' IAPR NewsletterTable of Contents1. Introduction to text mining; 2. Core text mining operations; 3. Text mining preprocessing techniques; 4. Categorization; 5. Clustering; 6. Information extraction; 7. Probabilistic models for Information extraction; 8. Preprocessing applications using probabilistic and hybrid approaches; 9. Presentation-layer considerations for browsing and query refinement; 10. Visualization approaches; 11. Link analysis; 12. Text mining applications; Appendix; Bibliography.
£71.24
Cambridge University Press Genomes Browsers and Databases DataMining Tools for Integrated Genomic Databases
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£78.85
Cambridge University Press Darkweb Cyber Threat Intelligence Mining
Book SynopsisThis book examines cyber threat intelligence obtained from the center of the malicious hacking underworld - the dark web. It studies these communities both qualitatively and quantitatively, leveraging techniques from data mining, machine learning and AI, and offering insights to both cybersecurity practitioners and researchers.Trade Review'Darkweb Cyber Threat Intelligence Mining represents a tipping point in cyber security. It is a must-read for anyone involved in the modern cyber struggle.' George Cybenko, Dartmouth College, New Hampshire, from the Foreword'The book is well written and well structured. The authors provide interesting facts on the darknet economy, its community, and its underling rules, such as trust-based platforms and the related problems of its participants.' Steffen Wendzel, Computing ReviewsTable of Contents1. Introduction; 2. Moving to proactive cyber threat intelligence; 3. Understanding darkweb malicious hacker forums; 4. Automatic mining of cyber intelligence from the dark web; 5. Analyzing products and vendors in malicious hacking markets; 6. Using game theory for threat intelligence; 7. Application – protecting industrial control systems; 8. Conclusion – the future of darkweb cyber threat intelligence.
£56.99
