Data mining Books
Business Expert Press Obtaining Value from Big Data for Service Systems, Volume II: Big Data Technology
Book SynopsisVolume II of this series discusses the technology used to implement a big data analysis capability within a service-oriented organization. It discusses the technical architecture necessary to implement a big data analysis capability, some issues and challenges in big data analysis and utilization that an organization will face, and how to capture value from it.It will help readers understand what technology is required for a basic capability and what the expected benefits are from establishing a big data capability within their organization.
£21.80
Springer Nature Switzerland AG Data Science for Economics and Finance:
Book SynopsisThis open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. Table of Contents
£33.24
Springer Nature Switzerland AG Data Analysis and Classification: Methods and
Book SynopsisThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.Table of ContentsPart 1: Methodology Chapter 1 - Evaluation of Two-Step Spectral Clustering Algorithm for Large Untypical Data Sets (Andrzej Dudek) Chapter 2 - Determining the Number of Groups in Cluster Analysis Using Classical Indexes and Stability Measures – Comparison of Results (Dorota Rozmus)Chapter 3 - Identification of the Words Most Frequently Used by Different Generations of Twitter Users (Agata Majkowska, Kamila Migdał-Najman, Krzysztof Najman and Katarzyna Raca)Chapter 4 - Classification Algorithms Applications for Information Security on the Internet: a Review (Michał Bryś)Chapter 5 - Outlier Detection with the Use of Isolation Forests (Krzysztof Najman and Krystian Zieliński)Part 2: Application in FinanceChapter 6 - Propositions of Transformations of Asymmetrical Nominants into Stimulants on the Example of Chosen Financial Ratios ( Barbara Batóg and Katarzyna Wawrzyniak)Chapter 7 - Gini Regression in The Capital Investment Risk Assessment – Sensitivity Risk Measures in Portfolio Analysis (Grażyna Trzpiot).- Part 3: Application in EconomicsChapter 8 - Enterprise Dark Data (Katarzyna Raca)Chapter 9 - The Significance of Medical Science Issues in Research Papers Published in the Field of Economics (Urszula Cieraszewska, Monika Hamerska, Paweł Lula and Marcela Zembura)Chapter 10 - Application of Duration Analysis Methods in the Study of the Exit of a Real Estate Sale Offer from the Offer Database System (Ewa Putek-Szeląg, Anna Gdakowicz)Chapter 11 - Is Society Ready for Long-Term Investments? – Profiles of Electricity Users in Silesia (Sylwia Słupik and Joanna Trzęsiok) Chapter 12 - The Use of the Spatial Taxonomic Measure of Development to Assess the Tourist Attractiveness of Districts of the Lesser Poland Province(Jacek Wolak).- Part 4: Application in Social Issues Chapter 13 - Models of Competing Events in Assessing the Effects of the Transition of Unemployed People Between the States of Registration and De-registration (Beata Bieszk-Stolorz).- Chapter 14 - Direct Adjusted Survival Probabilities in the Analysis of Finding a Job by the Unemployed Depending on Their Individual Characteristics(Wioletta Grzenda)Chapter 15 - Europe 2020 Strategy – Objective Evaluation of Realization and Subjective Assessment by Seniors as Beneficiaries of Social Assumptions (Klaudia Przybysz, Agnieszka Stanimir and Marta Wasiak)Chapter 16 - Do Seniors Get to the Disco by Bike or in a Taxi? – Classification of Seniors According to Their Preferred Means of Transport (Joanna Kos-Łabędowicz and Joanna Trzęsiok)Part 5: Application with COVID-19 Data Chapter 17 - The Impact of the COVID-19 Pandemic on the Economies of European Countries in the Period January-September 2020 Based on Economic Indicators (Ewelina Nojszewska and Agata Sielska)Chapter 18 - Modelling the Risk of Foreign Divestment in the Visegrad Group Countries During the COVID-19 Pandemic (Marcin Salamaga) Chapter 19 - Analysis of COVID-19 Dynamics in EU Countries Using the Dynamic Time Warping Method and ARIMA Models (Joanna Landmesser).
£104.99
Springer Nature Switzerland AG Deep Learning in Data Analytics: Recent
Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.
£132.99
Springer Nature Switzerland AG Harnessing the Power of Analytics
Book SynopsisThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.Table of ContentsChapter 1. Introduction to Analytics and Data Science. Chapter 2. Data Types Structure & Data Preparation Process. Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance. Chapter 5. Decision Trees & Ensemble. Chapter 6. Regression Models. Chapter 7. Neural Networks. Chapter 8. Model Deployment.
£71.24
Springer Nature Switzerland AG Recurrent Neural Networks: From Simple to Gated
Book SynopsisThis textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.Table of ContentsIntroduction1. Network Architectures2. Learning Processes3. Recurrent Neural Networks (RNN)4. Gated RNN: The Long Short-Term Memory (LSTM) RNN5. Gated RNN: The Gated Recurrent Unit (GRU) RNN6. Gated RNN: The Minimal Gated Unit (MGU) RNN
£42.74
Springer Nature Switzerland AG Advanced Analytics and Learning on Temporal Data:
Book SynopsisThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. Table of ContentsOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.
£44.99
Springer Nature Switzerland AG Machine Learning for Text
Book SynopsisThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.
£47.49
Springer International Publishing AG Business Intelligence: 7th International
Book SynopsisThis book constitutes the proceedings of the 7th International Conference on Business Intelligence, CBI 2022, which took place in Khouribga, Morocco, during May 26-28, 2022. The 23 full papers included in this book were carefully reviewed and selected from a total of 68 submissions. They were organized in topical sections as follows: decision support and artificial intelligence; business intelligence and database; and optimization and dynamic programming.Table of ContentsDecision Support and Artificial Intelligence.- Optimization Focused On Parallel Fuzzy Deep Belief Neural Network For Opinion Mining.- A Convolutional Neural Networks-Based Approach For Potato Disease Classification.- Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolu-tional Neural Networks.- Decision Boundary to improve the sensitivity of deep neural networks models. - Facial Expression Recognition Using a Hybrid ViT-CNN Aggregator.- Machine Learning Approach to Automate Decision Support on Information System Attacks.- Deep Reinforcement Learning for Bitcoin Trading.- An exploration of student grade retention prediction using machine learning algorithms.- Deep Learning Model For Educational Recommender Systems.- Comparative Study of Deep Learning Models for detection and classification of intracranial hemorrhage.- Business Intelligence and Database.- Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. -Mining frequents itemset and association rules in diabetic dataset.- Automatic text summarization for Moroccan Arabic dialect using an artificial intelligence approach.- Automatic Change Detection based on the Independent Component Analysis and Fuzzy C-means Methods.- Sentiment analysis decision system for tracking climate change opinion in Twitter.- Analysis of Decision Tree Algorithms for Diabetes Prediction.- How far can Deep Learning improve Arabic Part of Speech Tagging.- Optimization and Dynamic programming.- Analysis of Several Algorithms for DOA Estimation in Two Different Communication Models by a Comparative Study.- A Novel hybrid Approach for improving the accuracy of the Supervised Link Prediction based on Graph Structure Features in Social Networks. - Intelligent system based on GAN model for decision support in brain Tumor segmentation.- Hospital room management for Covid-19 patients using Petri nets.- Dimensionality reduction of MI-EEG data via convolutional autoencoders with a low size dataset.- Car tracking technique for DLES Project.
£58.49
Springer International Publishing AG Document Analysis Systems: 15th IAPR
Book SynopsisThis book constitutes the refereed proceedings of the 15th IAPR International Workshop on Document Analysis Systems, DAS 2022, held in La Rochelle, France, in May 2022.The full papers presented were carefully reviewed and selected from numerous submissions addressing key techniques of document analysis.
£89.99
Springer International Publishing AG Database Systems for Advanced Applications.
Book SynopsisThis volume constitutes the papers of several workshops which were held in conjunction with the 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held as virtual event in April 2022. The 30 revised full papers presented in this book were carefully reviewed and selected from 65 submissions. DASFAA 2022 presents the following five workshops: · First workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2021) · 6th International Workshop on Graph Data Management and Analysis (GDMA 2022) · First International Workshop on Blockchain Technologies (IWBT2022) · 8th International Workshop on Big Data Management and Service (BDMS 2022) · First workshop on Managing Air Quality Through Data Science · 7th International Workshop on Big Data Quality Management (BDQM 2022). Table of ContentsAn Algorithm for Mining Fixed-Length High Utility Itemsets.- A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine.- Pattern Mining: Current Challenges and Opportunities.- Why not to Trust Big Data: Identifying Existence of Simpson’s Paradox Localized Metric Learning for Large Multi-Class Extremely Imbalanced Face Database.- Top-k dominating queries on incremental datasets.- Collaborative Blockchain based Distributed Denial of Service Attack Mitigation approach with IP Reputation System.- Model-Driven Development of Distributed Ledger Applications Towards a Blockchain Solution for Customs Duty-Related Fraud.- Securing Cookies/Sessions through Non-Fungible Tokens.- Chinese Spelling Error Detection and Correction Based on Knowledge Graph Construction and Application of Event Logic Graph: A Survey.- Enhancing Low-resource Languages Question Answering with Syntactic Graph.- Profile Consistency Discrimination.- H-V：An Improved Coding Layout based on Erasure Coded Storage System.- Astral: An Autoencoder-based Model for Pedestrian Trajectory Prediction of Variable-Length.- A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail.- Fast Vehicle Track Counting in Traffic Video.- Summary A Traffic Summarization System using Semantic Words.- Attention_Cooperated_Reinforcement_Learning_for_Multi_agent_Path_Planning.- Big Data-driven Stable Task Allocation in Ride-hailing Services.- Weighted_Mean_Field_Multi_Agent_Reinforcement_Learning_via_Reward_Attribution_Decomposition.- Evaluating Presto and SparkSQL with TPC-DS.- Optimizing the Age of Sensed Information in Cyber-Physical Systems.- Aggregate Query Result Correctness using pattern Tables.- Time Series Data Quality Enhancing based on pattern Alignment.- Research on Feature extraction method of data quality intelligent detection.- Big Data Resources to Support Research Opportunities on Air Pollution Analysis in India.- Air Quality Data Collection in Hyderabad Using Low-cost Sensors: Initial Experiences.- Visualizing Spatio-Temporal Variation of Ambient Air Pollution in Four Small Towns in India.
£66.49
Springer International Publishing AG Elements of Data Science, Machine Learning, and
Book SynopsisThe textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.Table of Contents1. Introduction2. Introduction to learning from data3. Part 1: General topics4. Prediction models5. Error measures6. Resampling7. Data types8. Part 2: Core methods9. Maximum Likelihood & Bayesian analysis10. Clustering11. Dimension Reduction12. Classification13. Hypothesis testing14. Linear Regression15. Model Selection16. Part 3: Advanced topics17. Regularization18. Deep neural networks19. Multiple hypothesis testing20. Survival analysis21. Generalization error22. Theoretical foundations23. Conclusion.
£52.24
Springer International Publishing AG Neural Information Processing: 29th International
Book SynopsisThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.
£75.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£62.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£107.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.Table of ContentsBig data.- Toward Explainable Recommendation Via Counterfactual Reasoning.- Online Volume Optimization for Notifications via Long Short-Term Value Modeling.- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.- Financial data.- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets.- Let the model make financial senses: a Text2Text generative approach for financial complaint identification.- Information retrieval and search.- Web-scale Semantic Product Search With Large Language Models.- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval.- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion.- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval.- Isotropic Representation Can Improve Dense Retrieval.- Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification.- Internet of Things.- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection.- Medical and biological data.- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation.- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation.- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge.- Multimedia and multimodal data.- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance.- Dynamically-Scaled Deep Canonical Correlation Analysis.- TCR: Short Video Title Generation and Cover Selection with Attention Refinement.- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval.- Recommender systems.- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations.- Interest Driven Graph Structure Learning for Session-Based Recommendation.- Multi-behavior Guided Temporal Graph Attention Network for Recommendation.- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation.- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities.- Global-Aware External Attention Deep Model for Sequential Recommendation.- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking.- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation.- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval.- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation.- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling.
£98.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£49.49
Springer International Publishing AG Formal Concept Analysis: 17th International
Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Formal Concept Analysis, ICFCA 2023, which took place in Kassel, Germany, in July 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The International Conference on Formal Concept Analysis serves as a platform for researchers from FCA and related disciplines to showcase and exchange their research findings. The papers are organized in two topical sections, first "Theory" and second "Applications and Visualization".Table of ContentsTheory: Approximating fuzzy relation equations through concept lattices.- Doubly-Lexical Order Supports Standardisation and Recursive Partitioning of Formal Context.- Graph-FCA Meets Pattern Structures.- On the commutative diagrams among Galois connections involved in closure structures.- Scaling Dimension.- Three Views on Dependency Covers from an FCA Perspective.- A Triadic Generalisation of the Boolean Concept Lattice.- Applications and Visualization: Computing witnesses for centralising monoids on a three-element set.- Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees.- Examples of clique closure systems.- On the maximal independence polynomial of the covering graph of the hypercube up to n=6.- Relational Concept Analysis in Practice: Capitalizing on Data Modeling using Design Patterns.- Representing Concept Lattices with Euler Diagrams.
£42.74
Springer International Publishing AG Hypothesis Generation and Interpretation: Design
Book SynopsisThis book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on “social infrastructure” applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases. The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.Table of ContentsBasic Concept.- Hypothesis.- Science and Hypothesis.- Regression.- Machine Learning and Integrated Approach.- Hypothesis Generation by Difference.- Methods for Integrated Hypothesis Generation.- Interpretation.
£143.99
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 ContentsEmbracing data irregularities in multivariate time series with Recurrent and Graph Neural Networks.- Regulation and Ethics of Facial Recognition Systems: an analysis of cases in the Court of Appeal in the State of São Paulo.- A combinatorial optimization model and polynomial time heuristic for a problem of finding specific structural patterns in networks.- Efficient Density-Based Models for Multiple Machine Learning Solutions over Large Datasets.- Exploring Text Decoding Methods for Portuguese Legal Text Generation.- Community Detection for Multi-Label Classification.- A Monte Carlo Algorithm for Time-Constrained General Game Playing.- Alpha-MCMP: trade-offs between probability and cost in SSPs with the MCMP criterion.- Specifying Preferences over Policies using Branching Time Temporal Logic.- Logic-based Explanations for Linear Support Vector Classifiers with Reject Option.- The Multi-Attribute Fairer Cover Problem.- A Custom Bio-Inspired Algorithm for the Molecular Distance Geometry Problem.- Allocating Dynamic and Finite Resources to a Set of Known Tasks.- A Multi-algorithm approach to the optimization of thermal power plants operation.- An incremental MaxSAT-based model to learn interpretable and balanced classification rules.- d-CC Integrals: generalizing CC-integrals by restricted dissimilarity functions with applications to fuzzy-rule based systems.- FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction.- Hierarchical Time-aware Approach for Video Summarization.- Analyzing college student dropout risk prediction in real data using walk-forward validation.- The artificial intelligence as a technological resource in the application of tasks for the development of joint attention in children with autism.- Machine teaching: an explainable machine learning model for individualized education.- BLUEX: a benchmark based on Brazilian Leading Universities Entrance eXams.- Towards Generating P-Contrastive Explanations for Goal Selection in extended-BDI Agents.- Applying Theory of Mind to Multi-Agent Systems: A Systematic Literature Review.- A Spin-off Version of Jason for IoT and Embedded Multiagent Systems.- Hybrid Multilevel Explanation: A new approach for explaining regression models.- Explainability of COVID-19 Classification Models using Dimensionality Reduction of SHAP values.- An explainable model to support the decision about the appropriate therapy protocol for AML.- Bayes and Laplace versus the world: A new label attack approach in federated environments based on Bayesian Neural Networks.- MAT-Tree: A Tree-based Method for Multiple Aspect Trajectory Clustering.
£61.74
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 ContentsTransformer Model for Fault Detection From Brazilian Pre-Salt Seismic Data.- Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders.- Dog Face Recognition Using Vision Transformer.- Convolutional neural networks for the molecular detection of Covid-19.- Hierarchical Graph Convolutional Networks for Image Classification.- Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach.- Enhancing Stock Market Predictions through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-Market Analysis.- Ensemble architectures and efficient fusion techniques for Convolutional Neural Networks: an analysis on resource optimization strategies.- Dog Face Recognition using Deep Feature Embeddings.- Clinical oncology textual notes analysis using machine learning and deep learning.- EfficientDeepLab For Automated Trachea Segmentation On Medical Images.- Multi-Label Classification of Pathologies in Chest Radiograph Images Using DenseNet.- Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers.- Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence.- Deep Reinforcement Learning for Voltage Control in Power Systems.- Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging.- Occluded Face In-painting Using Generative Adversarial Networks - A ReviewClassification of facial images to assist in the diagnosis of Autism Spectrum Disorder: a study on the effect of face detection and landmark identification algorithms.- Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design.- AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification.- Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset.- Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification.- A Feature-based Out-of-Distribution Detection Approach in Skin Lesion Classification.- A framework for characterizing what makes an instance hard to classify.- Physicochemical Properties for Promoter Classification.- Critical analysis of AI indicators in terms of weighting and aggregation approaches.- Estimating Code Running Time Complexity with Machine LearningThe Effect of Statistical Hypothesis Testing on Machine Learning Model Selection.
£61.74
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
