Digital Trust

Name: Digital Trust | Online Safety, Identification Models, Ethical Digital Environments
Brand: De Gruyter
Price: 59.95 EUR
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Online Safety, Identification Models, Ethical Digital Environments

Pascal Muam Mah Mahmoud Ahmed Nasr(Author)

De Gruyter (Publisher)

1st Edition

Published on 4. February 2026

XX, 108 pages

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978-3-11-222975-0 (ISBN)

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Content

Intro
Abstract
Preface
Contents
List of Figures
List of Tables
List of abbreviation
Author contribution
1 Deep learning and NLP for teenagers' safety on social media: facial-age detection and content monitoring
1.1 Introduction
1.2 Literature review
1.2.1 Psychological culture behind social media
Does our culture influence our moral actions?
1.2.2 Significance of face-age detection on social media platforms for teenagers' safety and health
1.2.3 NLP knowledge graph for teenagers social media characteristics & content features safety
1.2.4 Harmful keywords & phrases associated with social media platform
1.2.5 Harmful sentiment keywords & phrases impact level on teenagers health
1.3 Applied method
1.4 Results and evaluation
1.4.1 Social media sentiment impact analysis of sub-themes on teenagers
1.4.2 Classification report metrics for age groups
1.4.3 Report: classification metrics for age groups
1.4.4 Confusion matrix under 13-18+
1.4.5 Facial age detection under 13-18
1.5 Potential innovations overview
1.5.1 Innovations on social media space
1.6 Conclusion
2 Enhancing data security: AI, IoT, and RFID-based solutions for safer social media interactions
2.1 Introduction
2.2 Literature review
2.2.1 Internet everywhere (Internet of things, GPS, and RFID)
2.2.2 Internet of things (IoTs) and radio frequency identification (RFID)
2.2.3 Thin technology between IoTs, GPS, and RFID (chipless sensors)
2.2.4 Internet everywhere (IEw)
2.2.5 Internet of things (IoTs) and global positioning system (GPS)
2.3 Applied method
2.3.1 Facial-age detection stages
2.3.1.1 Image preprocessing
2.3.1.2 Feature extraction
2.3.1.3 Classification
2.3.1.4 Softmax transformation
2.3.1.5 Classification decision
2.3.1.6 Loss function (cross-entropy loss)
2.3.1.7 Metrics for evaluation
2.3.1.8 Summary of full model prediction
2.3.2 IoT and RFID integration
2.3.2.1 Transformation into internet everywhere
2.3.2.2 Registration form
2.3.2.3 Personal ID code transformation strategy to satellite system
2.3.3 Cloud-based architecture
2.3.3.1 Location estimate for an internet users
2.3.3.2 Location weighted authentication
2.3.3.3 Cloud-based weighted location experimentation
2.3.3.4 Model graph of an online user based location and connection category
2.4 Deep learning filters for internet users
2.4.1 True filters
2.4.2 Falsified mixed filters
2.5 Results
2.5.1 Classification report
2.5.2 Classification metrics for each class
2.5.3 Input image classification
2.5.4 Metric progress over multiple runs
2.5.5 Confusion matrix
2.5.6 ROC curve
2.5.7 Significance of metric progress over multiple runs
2.6 Discussion
2.6.1 Limitation of facial-age detection
2.6.2 Limitation of the current user ID
2.6.3 Significance of a global unify internet ID
2.7 Forecast risk assessment impact of social media platforms on teenagers
2.8 Conclusion
3 Unmasking cybercrime: the role of deep learning and NLP in combating internet and social media threats
3.1 Introduction
3.2 Literature review
3.2.1 Global identification
3.2.2 Challenges of unique identifications to prevent social media crimes
3.3 Applied method
Data preprocessing (handling missing values & encoding)
Feature matrix and target vector preparation
Train-test split
Neural network architecture
Loss function (mean squared error for regression)
Optimization (gradient descent)
Model evaluation (test loss and accuracy)
3.4 Analysis
3.4.1 Impact of internet and social media on scam reporting and financial losses 2024
3.5 Assessment of various internet sites and social media platforms
3.6 Proposed unique identification
3.7 Unmasking internet & social media challenges
3.7.1 Face-age detection
3.7.2 Three-step model for cybercrime identification on social media and the internet
3.8 Conclusion
4 Internet of things psychological impact on knowledge acquisition based on natural language processing and virtual reality
4.1 Introduction
4.2 Literature review
4.2.1 Definition of key terms
4.2.2 Natural language processing and internet of things psychological impact
4.2.3 Supervised learning innovative expectations on virtual reality
4.2.4 Natural language processing content for internet-based data extraction
4.2.5 Virtual reality on user's engagement
4.2.6 Types of virtual reality technologies that swim across human physiology
4.2.7 Internet of things elements of virtual reality that impact users psychology
4.3 Applied method
4.3.1 Scoring m-model steps for data process mining
4.3.2 Behavior oriented drive and influential function
Definitions and formula
Example: push factors of e-services
4.4 Results
4.4.1 Data
4.4.2 Parts of speech identifier
4.4.3 Statistical data
Solutions
NLP definition by IBM
NLP definition by Wikipedia
NLP definition by University of York
NLP definition by Science-Direct
4.4.4 Scoring m-model content analyses of the definitions
4.5 Conclusion
General conclusion
Summary
Future direction
Bibliography
Index

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