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Beginning machine learning in the browser : quick-start guide to gait analysis with JavaScript and TensorFlow.js / / Nagender Kumar Suryadevara
| Beginning machine learning in the browser : quick-start guide to gait analysis with JavaScript and TensorFlow.js / / Nagender Kumar Suryadevara |
| Autore | Suryadevara Nagender Kumar |
| Pubbl/distr/stampa | [Place of publication not identified] : , : APress, , [2021] |
| Descrizione fisica | 1 online resource (xiv, 182 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning |
| ISBN | 1-4842-6843-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Preface -- Chapter 1: Web Development -- Machine Learning Overview -- Web Communication -- Organizing the Web with HTML -- Web Development Using IDEs/Editors -- Building Blocks of Web Development -- HTML and CSS Programming -- Dynamic HTML -- Cascading Style Sheets -- Inline Style Sheets -- Embedded Style Sheets -- External Style Sheets -- JavaScript Basics -- Including the JavaScript -- Where to Insert JS Scripts -- JavaScript for an Event-Driven Process -- Document Object Model Manipulation -- Introduction to jQuery -- Summary -- References -- Chapter 2: Browser-Based Data Processing -- JavaScript Libraries and API for ML on the Web -- W3C WebML CG (Community Group) -- Manipulating HTML Elements Using JS Libraries -- p5.js -- Drawing Graphical Objects -- Manipulating DOM Objects -- DOM onEvent(mousePressed) Handling -- Multiple DOM Objects onEvent Handling -- HTML Interactive Elements -- Interaction with HTML and CSS Elements -- Hierarchical (Parent-Child) Interaction of DOM Elements -- Accessing DOM Parent-Child Elements Using Variables -- Graphics and Interactive Processing in the Browser Using p5.js -- Interactive Graphics Application -- Object Instance, Storage of Multiple Values, and Loop Through Object -- Getting Started with Machine Learning in the Browser Using ml5.js and p5.js -- Design, Develop, and Execute Programs Locally -- Method 1: Using Python - HTTP Server -- Method 2: Using Visual Studio Code Editor with Node.js Live Server -- Summary -- References -- Chapter 3: Human Pose Estimation in the Browser -- Human Pose at a Glance -- PoseNet vs. OpenPose -- Human Pose Estimation Using Neural Networks -- DeepPose: Human Pose Estimation via Deep Neural Networks -- Efficient Object Localization Using Convolutional Networks.
Convolutional Pose Machines -- Human Pose Estimation with Iterative Error Feedback -- Stacked Hourglass Networks for Human Pose Estimation -- Simple Baselines for Human Pose Estimation and Tracking -- Deep High-Resolution Representation Learning for Human Pose Estimation -- Using the ml5.js:posenet() Method -- Input, Output, and Data Structure of the PoseNet Model -- Input -- Output -- .on() Function -- Summary -- References -- Chapter 4: Human Pose Classification -- Need for Human Pose Estimation in the Browser -- ML Classification Techniques in the Browser -- ML Using TensorFlow.js -- Changing Flat File Data into TensorFlow.js Format -- Artificial Neural Network Model in the Browser Using TensorFlow.js -- Trivial Neural Network -- Example 1: Neural Network Model in TensorFlow.js -- Example 2: A Simple ANN to Realize the "Not AND" (NAND) Boolean Operation -- Human Pose Classification Using PoseNet -- Setting Up a PoseNet Project -- Step 1: Including TensorFlow.js and PoseNet Libraries in the HTML Program (Main File) -- Step 2: Single-Person Pose Estimation Using a Browser Webcam -- PoseNet Model Confidence Values -- Summary -- References -- Chapter 5: Gait Analysis -- Gait Measurement Techniques -- Gait Cycle Measurement Parameters and Terminology -- Web User Interface for Monitoring Gait Parameters -- index.html -- Real-Time Data Visualization of the Gait Parameters (Patterns) on the Browser -- Determining Gait Patterns Using Threshold Values -- Summary -- References -- Chapter 6: Future Possibilities for Running AI Methods in a Browser -- Introduction -- Additional Machine Learning Applications with TensorFlow -- Face Recognition Using face-api.js -- Hand Pose Estimation -- Summary -- References -- Conclusion -- Index. |
| Record Nr. | UNINA-9910483611403321 |
Suryadevara Nagender Kumar
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| [Place of publication not identified] : , : APress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Smart Homes : Design, Implementation and Issues / / by Nagender Kumar Suryadevara, Subhas Chandra Mukhopadhyay
| Smart Homes : Design, Implementation and Issues / / by Nagender Kumar Suryadevara, Subhas Chandra Mukhopadhyay |
| Autore | Suryadevara Nagender Kumar |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (184 p.) |
| Disciplina | 004.68 |
| Collana | Smart Sensors, Measurement and Instrumentation |
| Soggetto topico |
Computational intelligence
Signal processing Image processing Speech processing systems Artificial intelligence Electronics Microelectronics Geriatrics Computational Intelligence Signal, Image and Speech Processing Artificial Intelligence Electronics and Microelectronics, Instrumentation Geriatrics/Gerontology |
| ISBN | 3-319-13557-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Smart Home related Research -- Design and Deployment of WSN in a Home Environment and Real-Time Data Fusion -- ADLs Recognition of an Elderly person and Wellness Determination -- Forecasting the Behaviour of an Elderly Person Using WSN Data -- Sensor Activity Pattern (SAP) Matching Process and Outlier Detection -- Conclusion and Future Works. |
| Record Nr. | UNINA-9910299676903321 |
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Suryadevara Nagender Kumar
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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