Intelligent Home Surveillance System using Convolution Neural Network Algorithms

Sathya, R.; Bharathi, V.C.; Ananthi, S.; Vaidehi, K.; Sangeetha, S.

doi:10.1109/icesc57686.2023.10193402

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2024

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Cited by 6 publications

References 15 publications

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A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

Remya,

Ranjana

2024

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

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A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

Remya,

Ranjana

2024

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

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Challenges and Opportunities in Implementing AI-Driven Surveillance for Women's Wellbeing

Kalyanaraman,

Ponnusamy,

Subramanian

2024

Wearable Devices, Surveillance Systems, and AI for Women's Wellbeing

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This research explores the difficulties and positive aspects of using AI-driven surveillance to improve women's wellbeing. The authors delve into the challenges, such as concerns about privacy and ethics, as well as societal and technical obstacles. On the flip side, the authors highlight opportunities, emphasizing how these technologies can empower women and enhance safety measures. The study incorporates case studies to provide real-world examples and extracts lessons from both successful and challenging implementations. Ethical considerations, including privacy and fairness, are thoroughly examined. The findings contribute recommendations for policies, ethical guidelines, and potential areas for future research in this evolving field. Overall, this research aims to shed light on the complex landscape of AI-driven surveillance for women's wellbeing, offering insights to guide future developments and implementations.

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Adversarial Defense Mechanisms for Detecting and Mitigating Cyber-Attacks in Wireless Sensor Networks

Umeshbhai,

Kanubhai,

Bilal

et al. 2024

Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)

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This chapter introduces ML-Defend, a novel defense mechanism tailored for detecting and mitigating cyber-attacks in wireless sensor networks (WSNs). By combining support vector machines (SVMs) for anomaly detection and convolutional neural networks (CNNs) for classification, ML-Defend harnesses the complementary strengths of these algorithms. This hybrid approach significantly enhances the system's ability to detect and mitigate cyber threats, thereby bolstering the security of WSNs. Through simulations and experiments, the authors demonstrate the efficacy of ML-Defend in accurately identifying and neutralizing attacks while minimizing false positives. This amalgamation of SVMs and CNNs presents a promising avenue for bolstering the cyber-defense capabilities of wireless sensor networks, ensuring their resilience against evolving threats in dynamic environments.

show abstract

Intelligent Home Surveillance System using Convolution Neural Network Algorithms

Cited by 6 publications

References 15 publications

A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

A Meta-Learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

Challenges and Opportunities in Implementing AI-Driven Surveillance for Women's Wellbeing

Adversarial Defense Mechanisms for Detecting and Mitigating Cyber-Attacks in Wireless Sensor Networks

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