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AI-Powered Child Behavior Monitoring With Secure Parental Consent and Deep Learning-Based Suspicious Activity Recognition
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Author(s): Vinod Mahor (Maulana Azad National Institute of Technology, India), Jaytrilok Choudhary (Maulana Azad National Institute of Technology, India)and Dhirendra Pratap Singh (Maulana Azad National Institute of Technology, India)
Copyright: 2026
Pages: 38
Source title:
Integrating Parental Consent and Child Engagement With Digital Protection Rules
Source Author(s)/Editor(s): Romil Rawat (LabGeoInf-Research LABoratory in GEOmatics and INFormation systems, National Research Council in Italy, Rome, Italy), Sanjaya Kumar Sarangi (Utkal University, India), A. Samson Arun Raj (Karunya Institute of Technology and Sciences, India), Janet Olivia Richmond (Karunya Institute of Technology and Sciences, India)and Purvee Bhardwaj (Rabindranath Tagore University, India)
DOI: 10.4018/979-8-3373-2716-7.ch012
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Abstract
Monitoring systems for child behavior that integrate intelligence with security and privacy protection must be prioritized amid the rising use of digital technology among children. The proposed framework offers an AI-based solution for child behavior monitoring by combining deep learning–based suspicious activity detection with security-certified parental consent mechanisms. The system employs a hybrid CNN-LSTM architecture, with a Convolutional Neural Network as its foundational component, to detect behavioral anomalies through complex spatiotemporal pattern analysis. The model achieves a training accuracy of 94.5% and a testing accuracy of 93.2% on the Children's Social Behavior Dataset, demonstrating strong predictive and generalization performance. To ensure privacy and security, the framework incorporates three advanced mechanisms. Real-time child behavior surveillance uses the DLBSAR algorithm which applies deep learning principles. It begins with consent validation. Overall, the framework presents a scalable and ethical solution for next-generation child behavior monitoring.
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