Digital Twin Technology
portes grátis
Digital Twin Technology
Elhoseny, Mohamed; Khari, Manju; Chaudhary, Gopal
Taylor & Francis Ltd
11/2024
240
Mole
Inglês
9780367677978
15 a 20 dias
358
Descrição não disponível.
Chapter 1: Digital Twin Technology: An Evaluation
Chapter 2: Digital Twin: Towards Internet of Drones
Chapter 3: Digital Twin in Agriculture Sector: Detection of Disease using Deep Learning
Chapter 4: Crop Diseases Detection and Prevention using AI and Machine Learning Techniques
Chapter 5: Architecture of Digital Twin for Network Forensic Analysis Using NMAP and WireShark
Chapter 6: Wind catchers as earth building: Digital Twins vs green sustainable architecture
Chapter 7: Digital Twin and the Detection and Location of DoS attacks to Secure Cyber-Physical UAS
Chapter 8: Digital twin techniques in Recognition of Human Action using the fusion of Convolutional Neural Network
Chapter 9: eVote - A Decentralised Voting Platform
Chapter 10: Nessus: A vulnerability scanner tool in network forensic
Chapter 11: Case Studies Related to Depression Detection Using Deep Learning Techniques
Chapter 2: Digital Twin: Towards Internet of Drones
Chapter 3: Digital Twin in Agriculture Sector: Detection of Disease using Deep Learning
Chapter 4: Crop Diseases Detection and Prevention using AI and Machine Learning Techniques
Chapter 5: Architecture of Digital Twin for Network Forensic Analysis Using NMAP and WireShark
Chapter 6: Wind catchers as earth building: Digital Twins vs green sustainable architecture
Chapter 7: Digital Twin and the Detection and Location of DoS attacks to Secure Cyber-Physical UAS
Chapter 8: Digital twin techniques in Recognition of Human Action using the fusion of Convolutional Neural Network
Chapter 9: eVote - A Decentralised Voting Platform
Chapter 10: Nessus: A vulnerability scanner tool in network forensic
Chapter 11: Case Studies Related to Depression Detection Using Deep Learning Techniques
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Digital Twin;IP Address;RNN;Wind Catcher;CNN Model;Network Forensic;IoT Device;Human Action Recognition;Smart Homes;SVM;Dl Algorithm;Cps;Network Gps;EVM;Fog Computing;DoS Attack;Depression Detection;IoT Data;RGB Camera;IoT System;Ml Algorithm;Port Screening;India's Gdp;MITM Attack;IoT Product
Chapter 1: Digital Twin Technology: An Evaluation
Chapter 2: Digital Twin: Towards Internet of Drones
Chapter 3: Digital Twin in Agriculture Sector: Detection of Disease using Deep Learning
Chapter 4: Crop Diseases Detection and Prevention using AI and Machine Learning Techniques
Chapter 5: Architecture of Digital Twin for Network Forensic Analysis Using NMAP and WireShark
Chapter 6: Wind catchers as earth building: Digital Twins vs green sustainable architecture
Chapter 7: Digital Twin and the Detection and Location of DoS attacks to Secure Cyber-Physical UAS
Chapter 8: Digital twin techniques in Recognition of Human Action using the fusion of Convolutional Neural Network
Chapter 9: eVote - A Decentralised Voting Platform
Chapter 10: Nessus: A vulnerability scanner tool in network forensic
Chapter 11: Case Studies Related to Depression Detection Using Deep Learning Techniques
Chapter 2: Digital Twin: Towards Internet of Drones
Chapter 3: Digital Twin in Agriculture Sector: Detection of Disease using Deep Learning
Chapter 4: Crop Diseases Detection and Prevention using AI and Machine Learning Techniques
Chapter 5: Architecture of Digital Twin for Network Forensic Analysis Using NMAP and WireShark
Chapter 6: Wind catchers as earth building: Digital Twins vs green sustainable architecture
Chapter 7: Digital Twin and the Detection and Location of DoS attacks to Secure Cyber-Physical UAS
Chapter 8: Digital twin techniques in Recognition of Human Action using the fusion of Convolutional Neural Network
Chapter 9: eVote - A Decentralised Voting Platform
Chapter 10: Nessus: A vulnerability scanner tool in network forensic
Chapter 11: Case Studies Related to Depression Detection Using Deep Learning Techniques
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
