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Classification Of Binary Vectors By Maximal Predictivity And Stochastic Complexity
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Book Synopsis IEEE International Symposium on Information Theory by :
Download or read book IEEE International Symposium on Information Theory written by and published by . This book was released on 1994 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :IEEE Information Theory Society Publisher :Institute of Electrical & Electronics Engineers(IEEE) ISBN 13 :9780780320161 Total Pages :540 pages Book Rating :4.3/5 (21 download)
Book Synopsis Proceedings by : IEEE Information Theory Society
Download or read book Proceedings written by IEEE Information Theory Society and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1994 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 1 by : Mohammed Atiquzzaman
Download or read book Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 1 written by Mohammed Atiquzzaman and published by Springer Nature. This book was released on 2023-07-04 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a selection of peer-reviewed papers presented at the 4th Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2022) conference, held in Bangkok, Thailand, on December 16–17. The contributions, prepared by an international team of scientists and engineers, cover the latest advances and challenges made in the field of big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
Book Synopsis Advances in Subsurface Data Analytics by : Shuvajit Bhattacharya
Download or read book Advances in Subsurface Data Analytics written by Shuvajit Bhattacharya and published by Elsevier. This book was released on 2022-05-18 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. - Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry - Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world - Offers an analysis of future trends in machine learning in geosciences
Book Synopsis Digital Technologies in Logistics and Infrastructure by : Igor Ilin
Download or read book Digital Technologies in Logistics and Infrastructure written by Igor Ilin and published by Springer Nature. This book was released on 2023-01-25 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of best selected research papers from the “Digital Technologies in Logistics and Infrastructure” conference (ICDT-2021). The topics of the presented papers are related to various aspects, problems, and solutions in maritime, transport, warehouse logistics, digital transformation, and information technologies in logistics, as well as digital transformation of infrastructure industries from theoretical and practical points of view. The authors of the conference are representatives of major companies, researchers, and scientists from Russia, Hungary, Armenia, Kazakhstan, Thailand, the Netherlands, and Finland. The conference proceedings are of interest to researchers and practitioners in the field of logistics and infrastructure in various sectors of digitalization.
Book Synopsis 30 MACHINE LEARNING ALGORITHMS 2024 Edition by : Diego Rodrigues
Download or read book 30 MACHINE LEARNING ALGORITHMS 2024 Edition written by Diego Rodrigues and published by Diego Rodrigues. This book was released on 2024-11-11 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 🚀 TAKE ADVANTAGE OF THE PROMOTIONAL LAUNCH OFFER OF THE YEAR 🚀 Become a master in machine learning with "30 MACHINE LEARNING ALGORITHMS: An Essential Guide for Students and Professionals - 2024 Edition". This book, created by Diego Rodrigues, is indispensable for students, developers, and professionals looking to excel in the dynamic field of artificial intelligence and data science. With a focus on practical applications, you will learn to implement the most powerful algorithms and transform data into valuable insights. Explore essential algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes, Gradient Boosting, XGBoost, LightGBM, K-Means, DBSCAN, Hierarchical Clustering, PCA, ICA, LDA, Q-Learning, SARSA, DQN, Neural Networks, CNN, RNN, LSTM, GAN, Apriori, FP-Growth, Lasso Regression, Ridge Regression, Elastic Net, and Transformer Neural Networks. With practical examples and detailed explanations, this guide will enable you to master advanced techniques and apply them confidently in real-world projects. This book is more than just a guide; it is your passport to the future of technology. Learn to build predictive models, optimize processes, discover hidden patterns in big data, and much more. Acquire skills that are in high demand and position yourself as a leader in artificial intelligence, data science, and deep learning. Don’t miss the opportunity to elevate your knowledge and stand out in the market. With this guide, you will not only learn but also apply, turning challenges into innovative solutions that will make you shine in the era of digital transformation. Machine learning algorithms artificial intelligence data science big data deep learning digital transformation technological innovation prediction predictive modeling data analysis process optimization operational efficiency Python Scikit-learn TensorFlow PyTorch Keras machine learning frameworks clustering algorithms neural networks computer vision natural language processing real-time data analysis intelligent automation software engineering cybersecurity RESTful APIs DevOps cloud computing AWS Google Cloud Microsoft Azure Docker Kubernetes Hadoop Spark SQL NoSQL sentiment analysis A/B testing SCRUM Agile prototyping Diego Rodrigues Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques cybersecurity skills cybersecurity industry global cybersecurity trends Kali Linux tools cybersecurity education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security cybersecurity challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
Book Synopsis Text Processing by : Prasenjit Majumder
Download or read book Text Processing written by Prasenjit Majumder and published by Springer. This book was released on 2018-01-25 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of a Workshop focussing on Text Processing, held at the Forum for Information Retrieval Evaluation, FIRE 2016, in Kolkata, India, in December 2016. 16 full papers have been selected for inclusion in the book out of 19 submissions. The papers refer to the following seven tracks: Consumer Health Information Search (CHIS), Detecting Paraphrases in Indian Languages (DPIL), Information Extraction from Microblogs Posted during Disasters, Persian Plagiarism Detection (PersianPlagDet), Personality Recognition in SOurce COde (PR-SOCO), Shared Task on Mixed Script Information Retrieval (MSIR), and Shared Task on Code Mix Entity Extraction in Indian Languages (CMEE-IL).
Book Synopsis Personalized and Precision Medicine Informatics by : Terrence Adam
Download or read book Personalized and Precision Medicine Informatics written by Terrence Adam and published by Springer Nature. This book was released on 2019-09-17 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book adopts an integrated and workflow-based treatment of the field of personalized and precision medicine (PPM). Outlined within are established, proven and mature workflows as well as emerging and highly-promising opportunities for development. Each workflow is reviewed in terms of its operation and how they are enabled by a multitude of informatics methods and infrastructures. The book goes on to describe which parts are crucial to discovery and which are essential to delivery and how each of these interface and feed into one-another. Personalized and Precision Medicine Informatics provides a comprehensive review of the integrative as well as interpretive nature of the topic and brings together a large body of literature to define the topic and ensure that this is the key reference for the topic. It is an unique contribution that is positioned to be an essential guide for both PPM experts and non-experts, and for both informatics and non-informatics professionals.
Book Synopsis Bayesian Theory and Applications by : Paul Damien
Download or read book Bayesian Theory and Applications written by Paul Damien and published by Oxford University Press. This book was released on 2013-01-24 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.
Book Synopsis Handbook of Approximate Bayesian Computation by : Scott A. Sisson
Download or read book Handbook of Approximate Bayesian Computation written by Scott A. Sisson and published by CRC Press. This book was released on 2018-09-03 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement. The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.
Book Synopsis Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence by : Yulin Wang
Download or read book Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence written by Yulin Wang and published by Springer Nature. This book was released on 2024 with total page 1042 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Predictive Modeling by : Max Kuhn
Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Author :Nevenka Žarkić-Joksimović Publisher :University of Belgrade, Faculty of Organizational Sciences ISBN 13 :8676803617 Total Pages :1161 pages Book Rating :4.6/5 (768 download)
Book Synopsis Proceedings of the XVI International symposium Symorg 2018 by : Nevenka Žarkić-Joksimović
Download or read book Proceedings of the XVI International symposium Symorg 2018 written by Nevenka Žarkić-Joksimović and published by University of Belgrade, Faculty of Organizational Sciences . This book was released on 2018-06-12 with total page 1161 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Perspectives on Social Welfare Applications Optimization and Enhanced Computer Applications by : Sivaram, Ponnusamy
Download or read book Perspectives on Social Welfare Applications Optimization and Enhanced Computer Applications written by Sivaram, Ponnusamy and published by IGI Global. This book was released on 2023-08-04 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer application systems are helpful for society to turn into a digital era of computing and interaction made more accessible and consistent. Further study in this field is required in order to ensure the applications are utilized appropriately. Perspectives on Social Welfare Applications Optimization and Enhanced Computer Applications discusses new computer applications and analyzes the existing ones to introduce a subsystem of the current system to make the social interactions towards digital world initiatives. This book provides a platform for scholars, researchers, scientists, and working professionals to exchange and share their computer application creation experiences and research results about all aspects of application software system development within computer science with emerging and advanced technologies. Covering topics such as applied computing, data science, and mobile computing, this premier reference source is ideal for industry professionals, computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students.
Book Synopsis Fundamentals and Methods of Machine and Deep Learning by : Pradeep Singh
Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Book Synopsis Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions by : Nicole Y. K. Li-Jessen
Download or read book Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions written by Nicole Y. K. Li-Jessen and published by Frontiers Media SA. This book was released on 2022-08-01 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Algorithms for Data Mining and Machine Learning by : Xin-She Yang
Download or read book Introduction to Algorithms for Data Mining and Machine Learning written by Xin-She Yang and published by Academic Press. This book was released on 2019-06-17 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages