New Developments In Unsupervised Outlier Detection
DOWNLOAD eBooks
Download New Developments In Unsupervised Outlier Detection PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get New Developments In Unsupervised Outlier Detection book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
New Developments In Unsupervised Outlier Detection
DOWNLOAD eBooks
Author : Xiaochun Wang
language : en
Publisher: Springer Nature
Release Date : 2020-11-24
New Developments In Unsupervised Outlier Detection written by Xiaochun Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-24 with Technology & Engineering categories.
This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
Outlier Detection Techniques And Applications
DOWNLOAD eBooks
Author : N. N. R. Ranga Suri
language : en
Publisher: Springer
Release Date : 2019-01-10
Outlier Detection Techniques And Applications written by N. N. R. Ranga Suri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-10 with Technology & Engineering categories.
This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.
Beginning Anomaly Detection Using Python Based Deep Learning
DOWNLOAD eBooks
Author : Suman Kalyan Adari
language : en
Publisher: Apress
Release Date : 2023-12-19
Beginning Anomaly Detection Using Python Based Deep Learning written by Suman Kalyan Adari and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Computers categories.
This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.
New Trends In Databases And Information Systems
DOWNLOAD eBooks
Author : Mārīte Kirikova
language : en
Publisher: Springer
Release Date : 2017-09-08
New Trends In Databases And Information Systems written by Mārīte Kirikova and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-08 with Computers categories.
This book constitutes the thoroughly refereed short papers, workshops and doctoral consortium papers of the 21th European Conference on Advances in Databases and Information Systems, ADBIS 2017, held in Nicosia, Cyprus, in September 2017. The 25 full and 4 short workshop papers and the 12 short papers of the main conference were carefully reviewed and selected from 160 submissions. The papers from the following workshops have been included in the proceedings: the first workshop on Data-Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017; the first workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017; the first international workshop on Data Science: Methodologies and Use-Cases, DaS 2017; the second international workshop on Semantic Web for Cultural Heritage, SW4CH 2017.
New Trends In Computational Vision And Bio Inspired Computing
DOWNLOAD eBooks
Author : S. Smys
language : en
Publisher: Springer Nature
Release Date : 2020-09-27
New Trends In Computational Vision And Bio Inspired Computing written by S. Smys and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-27 with Computers categories.
This volume gathers selected, peer-reviewed original contributions presented at the International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC) conference which was held in Coimbatore, India, on November 29-30, 2018. The works included here offer a rich and diverse sampling of recent developments in the fields of Computational Vision, Fuzzy, Image Processing and Bio-inspired Computing. The topics covered include computer vision; cryptography and digital privacy; machine learning and artificial neural networks; genetic algorithms and computational intelligence; the Internet of Things; and biometric systems, to name but a few. The applications discussed range from security, healthcare and epidemic control to urban computing, agriculture and robotics. In this book, researchers, graduate students and professionals will find innovative solutions to real-world problems in industry and society as a whole, together with inspirations for further research.
The State Of The Art In Intrusion Prevention And Detection
DOWNLOAD eBooks
Author : Al-Sakib Khan Pathan
language : en
Publisher: CRC Press
Release Date : 2014-01-29
The State Of The Art In Intrusion Prevention And Detection written by Al-Sakib Khan Pathan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-29 with Computers categories.
The State of the Art in Intrusion Prevention and Detection analyzes the latest trends and issues surrounding intrusion detection systems in computer networks, especially in communications networks. Its broad scope of coverage includes wired, wireless, and mobile networks; next-generation converged networks; and intrusion in social networks.Presenti
Hands On Unsupervised Learning Using Python
DOWNLOAD eBooks
Author : Ankur A. Patel
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-02-21
Hands On Unsupervised Learning Using Python written by Ankur A. Patel and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
New Trends In Information And Communications Technology Applications
DOWNLOAD eBooks
Author : Abbas M. Al-Bakry
language : en
Publisher: Springer Nature
Release Date : 2020-08-12
New Trends In Information And Communications Technology Applications written by Abbas M. Al-Bakry and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-12 with Computers categories.
This book constitutes refereed proceedings of the 4th International Conference on New Trends in Information and Communications Technology Applications, NTICT 2020, held on June 15, 2020. The NTICT conference was planned to take place in Baghdad on March 11-12, 2019, but due to the COVID-19 pandemic the conference has been postponed on June 15, 2020 and moved to the virtual format. The 15 full papers and 3 short papers presented were thoroughly reviewed and selected from 90 qualified submissions. The volume presents the latest research results in such areas as network protocols, overlay and other logical network structures, wireless access networks, computer vision, machine learning, artificial Intelligence, data mining, control methods.
Information Communication And Computing Technology
DOWNLOAD eBooks
Author : Costin Badica
language : en
Publisher: Springer Nature
Release Date : 2020-11-04
Information Communication And Computing Technology written by Costin Badica and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Conference on Information, Communication and Computing Technology, ICICCT 2020, held in New Delhi, India*, in May 2020. The 24 full papers and one short paper presented in this volume were carefully reviewed and selected from 220 submissions. The papers are organized in topical sections on data communication & networking; advanced computing using machine learning. *The conference was held virutally due to the COVID-19 pandemic.
Similarity Search And Applications
DOWNLOAD eBooks
Author : Tomáš Skopal
language : en
Publisher: Springer Nature
Release Date : 2022-09-27
Similarity Search And Applications written by Tomáš Skopal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-27 with Computers categories.
This book constitutes the refereed proceedings of the 15th International Conference on Similarity Search and Applications, SISAP 2022, held in Bologna, Italy in October 2022. SISAP 2022 is an annual international conference for researchers focusing on similarity search challenges and related theoretical/practical problems, as well as the design of content-based similarity search applications. The 15 full papers presented together with 8 short and 2 doctoral symposium papers were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Applications; Foundations; Indexing and Clustering; Learning; Doctoral Symposium.