Kids Cybersecurity Using Computational Intelligence Techniques

Podrobná bibliografie
Korporativní autor: SpringerLink (Online service)
Další autoři: Yafooz, Wael M. S. (Editor), Al-Aqrabi, Hussain (Editor), Al-Dhaqm, Arafat (Editor), Emara, Abdelhamid (Editor)
Shrnutí:VI, 282 p. 93 illus., 74 illus. in color.
text
Jazyk:angličtina
Vydáno: Cham : Springer International Publishing : Imprint: Springer, 2023.
Vydání:1st ed. 2023.
Edice:Studies in Computational Intelligence, 1080
Témata:
On-line přístup:https://doi.org/10.1007/978-3-031-21199-7
Médium: Elektronický zdroj Kniha
Obsah:
  • Part 1: State-of-the-art
  • Everyday Cyber Safety for Students
  • Machine Learning Approaches for Kids’ E-learning Monitoring
  • Factors influencing on online education outcomes– an empirical study based on Kids’ parents
  • Review on the Social Media Management Techniques against kids Harmful Information
  • Review of Information Security Management Frameworks
  • Database Forensics Field and Children Crimes
  • From exhibitionism to addiction, or cyber threats among children and adolescents
  • Part II: Cyberbullying and Kids cyber security
  • Protection of Users Kids on Twitter Platform using Naïve Bayes
  • The Impact of Fake News Spread on Social Media on The Children in Indonesia During Covid-19
  • A Preventive Approach to Weapons Detection for Children Using Quantum Deep Learning
  • Learning Arabic for Kids online Using Google Classroom
  • Child Emotion Recognition Via Custom Lightweight CNN Architecture
  • Cybercrime Sentimental Analysis for Child YouTube Video Dataset Using Hybrid Support VectorMachine With Ant Colony Optimization Algorithm
  • Cyberbullying Awareness Through Sentiment Analysis Based On Twitter
  • The Impact of Fake News on Kid’s Life from the Holy Al-Qur'an Perspective
  • Early Prediction of Dyslexia Risk Factors in Kids through Machine Learning Techniques
  • Development of Metamodel for Information Security Risk Management
  • Detecting Kids Cyberbullying Using Transfer Learning Approach from Transformer Fine-Tuning Models
  • YouTube Sentiment Analysis: Performance Model Evaluation.