Machine Vision for Industry 4.0
portes grátis
Machine Vision for Industry 4.0
Applications and Case Studies
Chatterjee, Prasenjit; Raut, Roshani; Krit, Salahddine
Taylor & Francis Ltd
05/2024
302
Mole
9780367641641
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Chapter 1. Challenges in Industry 4.0 for Machine Vision: A Conceptual Framework, A Review, and Numerous case studies, Chapter 2. Practical issues in robotics internet of things, Chapter 3. Role of sensing techniques in precision agriculture, Chapter 4. Perspectives on Deep Learning Techniques for Industrial IoT, Chapter 5. Missing person locator and identifier using artificial intelligence and supercomputing techniques proposal, Chapter 6. Inclusion of Impaired People in Industry 4.0: An Approach to Recognize Orders of Deaf-mute Supervisors through an Intelligent Sign Language Recognition System, Chapter 7. A Deep Learning Approach to Classify the Causes of Depression from Reddit Posts, Chapter 8. Psychiatric ChatBOT for COVID-19 using Machine Learning Approaches, Chapter 9. An Analysis of Drug - Drug Interaction (DI) using Machine Learning Techniques in Drug Development Process, Chapter 10. Image Processing based Fire Detection using IOT devices, Chapter 11. Crowd Estimation in Train by using Machine Vision, Chapter 12. Analysis of Machine Learning Algorithm to predict Wine Quality, Chapter 13. Machine Vision in Industry 4.0: Applications, Challenges and Future Directions, Chapter 14. Industry 5.0: The Integration of Modern Technologies
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Cyber Physical Systems;industrial internet of things;Machine Vision;internet of things;Industrial IoT;robotics internet of things;SVM;object recogntiion;RGB;smart industry;LCD;deep learning;Machine Vision Methods;video analytics;DDI Prediction;convolution neural network;IoT Framework;Ml Algorithm;Machine Vision System;Tcp;RNN;Arduino UNO;Vice Versa;RGB Space;Random Forest;Precision Agriculture;Employed Bees;AdaBoost Classifier;Machine Vision Technologies;RGB Color Model;IoT Gadget;Deep Neural Network Model;Data Augmentation
Chapter 1. Challenges in Industry 4.0 for Machine Vision: A Conceptual Framework, A Review, and Numerous case studies, Chapter 2. Practical issues in robotics internet of things, Chapter 3. Role of sensing techniques in precision agriculture, Chapter 4. Perspectives on Deep Learning Techniques for Industrial IoT, Chapter 5. Missing person locator and identifier using artificial intelligence and supercomputing techniques proposal, Chapter 6. Inclusion of Impaired People in Industry 4.0: An Approach to Recognize Orders of Deaf-mute Supervisors through an Intelligent Sign Language Recognition System, Chapter 7. A Deep Learning Approach to Classify the Causes of Depression from Reddit Posts, Chapter 8. Psychiatric ChatBOT for COVID-19 using Machine Learning Approaches, Chapter 9. An Analysis of Drug - Drug Interaction (DI) using Machine Learning Techniques in Drug Development Process, Chapter 10. Image Processing based Fire Detection using IOT devices, Chapter 11. Crowd Estimation in Train by using Machine Vision, Chapter 12. Analysis of Machine Learning Algorithm to predict Wine Quality, Chapter 13. Machine Vision in Industry 4.0: Applications, Challenges and Future Directions, Chapter 14. Industry 5.0: The Integration of Modern Technologies
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Cyber Physical Systems;industrial internet of things;Machine Vision;internet of things;Industrial IoT;robotics internet of things;SVM;object recogntiion;RGB;smart industry;LCD;deep learning;Machine Vision Methods;video analytics;DDI Prediction;convolution neural network;IoT Framework;Ml Algorithm;Machine Vision System;Tcp;RNN;Arduino UNO;Vice Versa;RGB Space;Random Forest;Precision Agriculture;Employed Bees;AdaBoost Classifier;Machine Vision Technologies;RGB Color Model;IoT Gadget;Deep Neural Network Model;Data Augmentation
