Advanced Data Mining Tools and Methods for Social Computing, de Sourav, Dey Sandip, Bhattacharyya Siddhartha
Автор: Chirag Shah Название: A Hands-On Introduction to Data Science ISBN: 1108472443 ISBN-13(EAN): 9781108472449 Издательство: Cambridge Academ Рейтинг: Цена: 7286.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A practical introduction to data science with a low barrier entry, this textbook is well-suited to students from a range of disciplines. Assuming no prior knowledge of the subject, the hands-on exercises and real-life application of popular data science tools are accessible even to students without a strong technical background.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Описание: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. . Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.
Описание: Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets.
Описание: The first part is advances in systems and it is dedicated to applications of the artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy system, autonomous and multi-agent systems, machine learning, other intelligent systems and related areas.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Описание: This book constitutes the post-conference proceedings of the 4th International Conference on Advances in Computing and Data Sciences, ICACDS 2020, held in Valletta, Malta, in April 2020.*The 46 full papers were carefully reviewed and selected from 354 submissions.
Описание: This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.*The 103 full papers were carefully reviewed and selected from 781 submissions.
Описание: An Energy-Efficient Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Devices in IoT.- Fund Utilization under Parliament Local Development Scheme: Machine Learning base Approach.- Implementing Automatic Ontology Generation for the New Zealand Open Government Data: An Evaluative Approach.- Blockchain based Framework to Maintain Chain of Custody (CoC) in a Forensic Investigation. - A light SRGAN for Up-Scaling of Low Resolution and High Latency Images.- Energy Efficient Clustering Routing Protocol and ACO Algorithm in WSN.- Efficient Social Distancing Detection using Object Detection and Triangle Similarity.- Explaining a Black-Box Sentiment Analysis Model with Local Interpretable Model Diagnostics Explanation (LIME).- Spelling Checking and Error Corrector System for Marathi Language Text using Minimum Edit Distance Algorithm.- A Study on Morphological Analyser for Indian Languages: A Literature Perspective.- Cyber Safety Against Social Media Abusing.- Predictive Rood Pattern Search for Efficient Video Compression.- An Effective Approach For Classifying Acute Lymbphoblastic Luekemia Using Hybrid Hierarchial Classifiers.- Abnormal Blood Vessels Segmentation for Proliferative Diabetic Retinopathy Screening Using Convolutional Neural Network. - Predictive Programmatic Classification Model to Improve Ad-Campaign Click Through Rate.- Live stream processing techniques to assist unmanned, regulated railway crossings.- Most Significant Bit-Plane based Local Ternary Pattern for Biomedical Image Retrieval.- Facial Monitoring Using Gradient Based Approach.- Overlapped Circular Convolution based feature extraction algorithm for classification of high dimensional datasets.- Binary Decision Tree Based Packet Queuing Schema for Next Generation Firewall.- Automatic Tabla Stroke Source Separation Using Machine Learning.- Classification of Immunity Booster Medicinal Plants using CNN: A Deep Learning Approach.- Machine Learning Model Interpretability in NLP and Computer Vision Applications.- Optimal Sizing and Siting of Multiple Dispersed Generation System using Metaheuristic Algorithm.- Design of a Fused Triple Convolutional Neural Network for Malware Detection: A Visual Classification Approach.- Mobile Agent Security using Lagrange Interpolation with Multilayer Perception Neural Network.- Performance Analysis of Channel coding techniques for 5G networks.- An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product.- A Study on Energy-Aware Virtual Machine Consolidation Policies in Cloud Data Centers using Cloudsim Toolkit.- Predicting Insomnia Using Multilayer Stacked Ensemble Model. - A novel encryption scheme based on Fully Homomorphic Encryption and RR-AES along with privacy preservation for vehicular network.- Key-Based Decoding for Coded Modulation Schemes in the presence of ISI.- Optimizing the Performance of KNN Classifier for Human Activity Recognition.- Face Recognition with Disguise and makeup Variations Using Image Processing and Machine Learning.- Attention-based deep Fusion Network for Retinal Lesion Segmentation in Fundus Image.- Visibility improvement in hazy conditions via a deep learning based image fusion approach.- Performance of Reinforcement Learning Simulation: x86 v/s ARM.- A Performance Study of Probabilistic Possibilistic Fuzzy C-Means Clustering Algorithm.- Optimized Random Forest Algorithm with Parameter Tuning for Predicting Heart Disease.- Machine Learning Based Techniques for Detection of Renal Calculi in Ultrasound Images.- Unsupervised Change Detection in Remote Sensing Images Using CNN Based Transfer Learning.- Biological Sequence Embedding based Classification for MERS and SARS.- Supply Path Optimization in Video Advertising Landscape.- Stack-based CNN Approach to Covid-19 Detection.- Performance Analysis of Various Classifiers for Social Intimidating Activities Detection.- Technique for Enhancing the efficiency and security of lightweight IoT
Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.
Описание: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
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