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Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 2

Автор: Limpopo5 от 2024-03-09, 04:57:06
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Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 2Название: Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 2
Автор: Аtiqur Rаhmаn Аhаd, Sоzо Inоuе, Guillаumе Lореz
Издательство: CRC Press
Год: 2024
Страниц: 285
Язык: английский
Формат: pdf (true)
Размер: 18.6 MB

Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects. Activity recognition has been a hot research topic for some years now. One of its main applications is ambient assisted living, so that people in need of assistance might stay in their own environment longer than previously possible. There is a wide range of sensors of different types and applications being tested in this domain, ranging from ambient binary motion sensors and pressure mats to wearable gyroscopes or sensors monitoring body functions. This is also due to the many different levels of surveillance tested by the researchers. The library we selected to use here is Apache Beam and in-memory database Redis. There are two reasons to use Apache Beam: The first is the ease of implementation. Apache Beam can be written in programming languages such as Java or Python, and the process is represented as a pipeline, so it is intuitive and easy to implement. We used Machine Learning methods for classification and extracted statistical features from raw 3-D acceleration and 3-D angular velocity for each axis within the segment. In this work, we compared three types of classifiers, multi-class support vector machine (SVM), Random Forest (RF), and K Nearest Neighbors (KNN) from Scikit-learn, a Machine Learning library for Python. We chose to set all of the parameters of Machine Learning models to default values of Scikit-learn. Before training data with classifiers, we chose RF and KNN classifiers which calculate distances between different points in their algorithm.

Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 1

Автор: Limpopo5 от 2024-03-09, 04:33:41
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Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 1Название: Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 1
Автор: Аtiqur Rаhmаn Аhаd, Sоzо Inоuе, Guillаumе Lореz
Издательство: CRC Press
Год: 2024
Страниц: 457
Язык: английский
Формат: pdf (true)
Размер: 21.9 MB

The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. Sensors and cameras are exploited for the analysis and recognition of human activity and behavior. In the book Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors, we have divided across two volumes, 40 wonderful chapters under five parts: Part 1: Healthcare and Emotion (Chapters 1–7), Part 2: Mental Health (Chapters 8–14), Part 3: Nurse Care Records (Chapters 15–26), Part 4: Movement and Sensors (Chapters 27–36), and Part 5: Sports Activity Analysis (Chapters 37–40). In a complex problem such as stress detection, the application of some type of Machine Learning (ML) algorithms makes sense. The vast amount of data in a context where multiple variables, such as HR, HRV, GSR, and ST, might have different outputs based on each other, makes it a prime target for the ML approach. Some of the most common classification algorithms are Support Vector Machines (SVM), K-Nearest Neighbours (K-NN), Random Forests (RF), Decision Trees, and Naive Bayes (NB). The data processing and model development used different Python libraries such as Pandas, Tensorflow, and Keras. To recognize stress from the physiological data, we tested different Machine Learning algorithms. We implemented the approach using Python and the Scikit-learn library.

Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data

Автор: Limpopo5 от 2024-03-08, 18:08:07
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Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related DataНазвание: Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data
Автор: Dаniеl Меmmеrt
Издательство: Springer
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf (true), epub
Размер: 17.7 MB

In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer Science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities. This textbook aims to live up to the now broad diversity of Computer Science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages (R and Python) and visualisation, the textbook is framed by history and an outlook. Python is highly popular in the community of data scientists in general and sports analysts in particular because it is a open-source, dynamic, object-oriented, high-level programming language, which provides highly flexible and up-to-date functionalities due to its available modules and libraries.

Security Strategies in Windows Platforms and Applications, 4th Edition

Автор: Limpopo5 от 2024-03-08, 04:47:03
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Security Strategies in Windows Platforms and Applications, 4th EditionНазвание: Security Strategies in Windows Platforms and Applications, 4th Edition
Автор: Rоbеrt Shimоnski, Мiсhаеl G. Sоlоmоn
Издательство: Jones & Bartlett Learning
Год: 2024
Страниц: 904
Язык: английский
Формат: epub
Размер: 22.8 MB

Revised and updated to keep pace with this ever-changing field, Security Strategies in Windows Platforms and Applications, Fourth Edition focuses on new risks, threats, and vulnerabilities associated with the Microsoft Windows operating system, placing a particular emphasis on Windows 11, and Windows Server 2022. The Fourth Edition highlights how to use tools and techniques to decrease risks arising from vulnerabilities in Microsoft Windows operating systems and applications. The book also includes a resource for readers desiring more information on Microsoft Windows OS hardening, application security, and incident management. With its accessible writing style, and step-by-step examples, this must-have resource will ensure readers are educated on the latest Windows security strategies and techniques. This book has also been fully updated to reflect current technology trends such as cloud, AI/ML, DevOps and the use of Microsoft Azure.

Effective Machine Learning Teams: Best Practices for Ml Practitioners (Final)

Автор: Limpopo5 от 2024-03-07, 20:41:49
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Effective Machine Learning Teams: Best Practices for Ml Practitioners (Final)Название: Effective Machine Learning Teams: Best Practices for Ml Practitioners (Final)
Автор: Dаvid Таn, Аdа Lеung, Dаvid Соlls
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 402
Язык: английский
Формат: pdf (true), epub (true)
Размер: 15.1 MB, 10.1 MB

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions.

GoLang for Machine Learning: A Hands-on-Guide to Building Efficient, Smart and Scalable ML Models with Go Programming

Автор: Limpopo5 от 2024-03-06, 18:42:05
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GoLang for Machine Learning: A Hands-on-Guide to Building Efficient, Smart and Scalable ML Models with Go ProgrammingНазвание: GoLang for Machine Learning: A Hands-on-Guide to Building Efficient, Smart and Scalable ML Models with Go Programming
Автор: Еvаn Аtkins
Издательство: Independently published
Год: 2024
Страниц: 155
Язык: английский
Формат: pdf
Размер: 19.1 MB

Go, the high-performance language from Google, is rapidly gaining traction in the Machine Learning (ML) world. Its speed, concurrency, and built-in features make it ideal for building efficient, scalable ML models. But where do you start? This book is written by a seasoned developer and Machine Learning expert, providing you with practical, hands-on guidance based on real-world experience. After reading this book, you'll be equipped with the knowledge and tools to create robust, performant models without sacrificing clarity or maintainability. Hands-on projects covering various Machine Learning tasks, from regression and classification to image recognition and natural language processing. This book is designed for programmers with some coding experience who are interested in applying Go to Machine Learning. Whether you're a data scientist, software engineer, or simply curious about Go's potential, this guide will empower you to create impactful ML models. Stop struggling with slow, complex ML frameworks. Start building efficient, scalable models with Go. Get your copy of GoLang for Machine Learning today and embark on your journey to smarter, faster AI!

Adversarial Multimedia Forensics

Автор: Limpopo5 от 2024-03-06, 07:46:07
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Adversarial Multimedia ForensicsНазвание: Adversarial Multimedia Forensics
Автор: Еhsаn Nоwrооzi, Каssеm Каllаs, Аlirеzа Jоlfаеi
Издательство: Springer
Серия: Advances in Information Security
Год: 2024
Страниц: 298
Язык: английский
Формат: pdf (true), epub
Размер: 45.9 MB

This book explores various aspects of digital forensics, security and Machine Learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addresses techniques and methodologies related to digital image forensics. It discusses advanced techniques for image manipulation detection, including the use of Deep Learning architectures to generate and manipulate synthetic satellite images. This book also explores methods for face recognition under adverse conditions and the importance of forensics in criminal investigations. Additionally, the book highlights anti-forensic measures applied to photos and videos, focusing on their effectiveness and trade-offs. Intended for those in Computer Science, engineering, and other related fields, this book explores the challenges and opportunities of the adversarial side of multimedia forensics. Some Machine Learning and signal processing basics are helpful but are not required. The goal is to equip readers with the knowledge to navigate this complex space and drive innovation in this exciting, ever-changing field.

WSN and IoT: An Integrated Approach for Smart Applications

Автор: Limpopo5 от 2024-03-04, 05:25:32
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WSN and IoT: An Integrated Approach for Smart ApplicationsНазвание: WSN and IoT: An Integrated Approach for Smart Applications
Автор: Shаlli Rаni, Аshu Таnеjа
Издательство: CRC Press
Год: 2024
Страниц: 407
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB

Nowadays, all of us are connected through a large number of sensor nodes, smart devices, and wireless terminals. For these Internet of Things (IoT) devices to operate seamlessly, the Wireless Sensor Network (WSN) needs to be robust to support huge volumes of data for information exchange, resource optimization, and energy efficiency. This book provides in-depth information about the emerging paradigms of IoT and WSN in new communication scenarios for energy-efficient and reliable information exchange between a large number of sensor nodes and applications. WSN and IoT: An Integrated Approach for Smart Applications discusses how the integration of IoT and WSN enables an efficient communication flow between sensor nodes and wireless terminals and covers the role of Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL), and blockchain technologies which give way to intelligent networks. This book presents how technological advancement is beneficial for real-time applications involving a massive number of devices and discusses how the network carries huge amounts of data allowing information to be communicated over the Internet.

Artificial Intelligence in Vision-Based Structural Health Monitoring

Автор: Limpopo5 от 2024-03-02, 13:32:46
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Artificial Intelligence in Vision-Based Structural Health MonitoringНазвание: Artificial Intelligence in Vision-Based Structural Health Monitoring
Автор: Кhаlid М. Моsаlаm, Yuqing Gао
Издательство: Springer
Год: 2024
Страниц: 396
Язык: английский
Формат: pdf (true)
Размер: 15.9 MB

This book provides a comprehensive coverage of the state-of-the-art Artificial Intelligence (AI) technologies in vision-based structural health monitoring (SHM). In this data explosion epoch, AI-aided SHM and rapid damage assessment after natural hazards have become of great interest in civil and structural engineering, where using Machine Learning and Deep Learning in vision-based SHM brings new research direction. As researchers begin to apply these concepts to the structural engineering domain, especially in SHM, several critical scientific questions need to be addressed: (1) What can AI solve for the SHM problems? (2) What are the relevant AI technologies? (3) What is the effectiveness of the AI approaches in vision-based SHM? (4) How to improve the adaptability of the AI approaches for practical projects? (5) How to build a resilient AI-aided disaster prevention system making use of the vision-based SHM? This book introduces and implements the state-of-the-art Machine Learning and Deep Learning technologies for vision-based SHM applications. During this time the SHM field has adopted many data-driven algorithms from disparate fields such as radar and sonar detection, Artificial Intelligence/Machine Learning, speech-pattern recognition, statistical decision theory, and econometrics.

MATLAB Machine Learning Recipes: A Problem-Solution Approach, 3rd Edition

Автор: Limpopo5 от 2024-03-02, 13:00:24
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MATLAB Machine Learning Recipes: A Problem-Solution Approach, 3rd EditionНазвание: MATLAB Machine Learning Recipes: A Problem-Solution Approach, 3rd Edition
Автор: Мiсhаеl Раluszеk, Stерhаniе Тhоmаs
Издательство: Apress
Год: 2024
Страниц: 458
Язык: английский
Формат: pdf
Размер: 16.8 MB

Harness the power of MATLAB to resolve a wide range of Machine Learning challenges. This new and updated third edition provides examples of technologies critical to Machine Learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in Machine Learning. It also has material that will allow those with an interest in other technology areas to see how Machine Learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

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