Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics, Belgacem Ben Youssef, Mohamed Maher Ben Ismail
Автор: Choo Kim-Kwang Raymond, Dehghantanha Ali Название: Handbook of Big Data Analytics and Forensics ISBN: 3030747522 ISBN-13(EAN): 9783030747527 Издательство: Springer Рейтинг: Цена: 21953.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
Описание: Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:- A comprehensive overview of the various fields of application of data science- Case studies from practice to make the described concepts tangible- Practical examples to help you carry out simple data analysis projectsThe book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues:- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies- Computer vision: How can we gain insights from images and videos with data science?- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.- ML and AI in production: How to turn experimentation into a working data science product?- Presenting your results: Essential presentation techniques for data scientistsContributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / G?nther Zauner
Описание: As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Автор: Bhattacharya Shuvajit, Di Haibin Название: Advances in Subsurface Data Analytics ISBN: 0128222956 ISBN-13(EAN): 9780128222959 Издательство: Elsevier Science Рейтинг: Цена: 19370.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: `Through her courageous resistance, she has, for a moment, drawn global attention to the ongoing struggle of Saudi women. The striking image of a young woman, wielding nothing but a cellphone, facing down the force of an oppressive government is an apt metaphor for this fraught moment in Saudi Arabia`s history.`THE WASHINGTON POST
Описание: Highlights the marriage between business intelligence and knowledge management through the use of agile methodologies. Through its fifteen chapters, this offers perspectives on the integration between process modeling, agile methodologies, business intelligence, knowledge management, and strategic management.
Описание: As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Описание: This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
Описание: This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making.
Описание: Chapter 1: Introduction to Deep Learning-based Technological Applications.- Chapter 2: Vision to Language: Methods, Metrics and Datasets.- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis.- Chapter 4: Deep Learning Approaches in Food Recognition.- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding.- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks.- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area.- Chapter 8: Deep Learning Methods in Electroencephalography.
Описание: Chapter 1: Static 2D and 3D GraphsChapter Goal: This chapter introduces the basics of tabulating data and constructing staticgraphical representations. To begin with, it exhibits an approach of extracting and tabulating data by implementing the pandas and sqlalchemy library. Subsequently, it reveals a prevalent 2D and 3D charting recognized as Matplotlib, then exhibits a technique of constructing basic charts (i.e. box-whisker plot, histogram, line plot, and scatter plot).● Tabulating Data● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Plot● 3D Charting● Conclusion Chapter 2: Interactive ChartingChapter Goal: This chapter introduces an approach for constructing interactive charts byimplementing the most prevalent library, recognized as Plotly.● Plotly● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Ploto Bar Charto Pie Charto Sunburst● 3D Charting● Conclusion Chapter 3: Containing functionality in Interactive GraphsChapter Goal: This chapter extends to the preceding chapter. It introduces an approach toupdating interactive graphs to improve user experience. For instance, you will learn how to add buttons and range sliders, among other functionalities. Besides that, it exhibits an approach for integrating innumerable graphs into one graph with some functionality.● Updating Graph Layout● Updating Plotly Axes● Including Range Slider● Including Buttons to a Graph● Styling Interactive Graphs● Updating Plotly X-Axis● Color Sequencing● Subplots● Conclusions Chapter 4: Essentials of HTMLChapter Goal: This chapter introduces the most prevalent markup language for developingwebsites. It acquaints you with the essentials of designing websites. Besides that, it contains a richset of code and examples to support you in getting started with coding using HTML.● The Communication between a Web Browser and Web Server● Domain Hostingo Shared Hostingo Managed Hosting● HyperText Markup Languageo HTML Elements▪ Headings▪ Paragraphs▪ Div▪ Span▪ Buttons▪ Text Box▪ Input▪ File Upload▪ Label▪ Form▪ Meta Tag● Practical Example● Conclusion Chapter 5: Python Web Frameworks and ApplicationsChapter Goal: The preceding chapter acquainted you with interactive visualization using Plotly. This chapter introduces key Python web frameworks (i.e., flask and dash) and how they differ.Besides that, it provides practical examples and helps you get started with Python web development.● Web Frameworks● Web Applications● Flasko WSGIo Werkzeugo Jinjao Installing Flasko Initializing a Flask Web Applicationo Flask Application Codeo Deploy a Flask Web Application● Dasho Installing Dash Dependencieso Initializing a Dash Web Applicationo Dash Application Codeo Deploy a Dash Web Application● Jupyter Dash● Conclusion Chapter 6: Dash Bootstrap ComponentsChapter Goal: This chapter covers dash_bootstrap_component. It is a Python library from the Plotly family, which enables us to have key bootstrap func
Автор: Kumar, Pardeep, Singh, Amit Kumar Название: Machine Learning for Intelligent Multimedia Analytics ISBN: 9811594910 ISBN-13(EAN): 9789811594915 Издательство: Springer Рейтинг: Цена: 20733.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1. Secure Multimodal Access with 2D and 3D Ears.- Chapter 2. Efficient and Low Overhead Detection of Brain Diseases using Deep Learning based Sparse MRI Image Classification.- Chapter 3. Continual Deep Learning Framework for Medical Media Screening and Archival.- Chapter 4. KannadaRes-NeXt: a Deep Residual Network for Kannada Numeral Recognition.- Chapter 5. Secure Image Transmission in Wireless Network using Conventional Neural Network and DOST.- Chapter 6. Robust General Twin Support Vector Machine with Pinball Loss Function.- Chapter 7. Noise Resilient Thresholding based on Fuzzy Logic and Non-linear Filtering.- Chapter 8. Deep Learning Methods for Audio Events Detection.- Chapter 9. A Framework for Multi-lingual Scene Text Detection using K-means++ and Memetic Algorithms.- Chapter 10. Recent Advancements in Medical Imaging: A Machine Learning Approach.- Chapter 11. Solving Image Processing Critical Problems using Machine Learning.- Chapter 12. Spoken Language Identification of Indian Languages using MFCC Features.- Chapter 13. Performance Evaluation of One-Class Classifiers (OCC) for Damage Detection in Structural Health Monitoring.- Chapter 14. Brain Tumor Classification in MRI Images using Transfer Learning.- Chapter 15. Semantic based Vectorization Technique for Hindi Language.
Описание: This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
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