Browsing by Author "Jackson, Beulah"

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    Next-gen non-invasive glucose monitoring using microwave sensors and ai-based thumb positioning analysis
    (IEEE, 2025-06-25) Uma, S.; Soundharya, P.; Charisma, S.; Jackson, Beulah; Ganapathi, G.; Ali, Guma
    Because of discomfort and accuracy problems, the existing non-invasive glucose monitoring devices include optical sensors and finger-prick tests have long been under doubt for their viability. Variations in skin type and environmental interference might make these techniques unreliable. To get past these constraints, the study presents a fresh approach combining artificial intelligence (AI)-based thumb positioning analysis with microwave sensors. By spotting changes in the dielectric characteristics of the skin brought on by glucose, the microwave sensors offer exact, invasive, real-time readings. Through best sensor alignment, AI removes thumb position errors. Performance assessment indicates substantially better outcomes than the existing systems with an R2 of 0.98, RMSE of 7.1 mg/dL, and MAE of 5.2 mg/dL. The great flexibility of the proposed system to a wide spectrum of demographics and excellent user compliance (95%) underlines its possibility for efficient and comfortable diabetes control. The technique represents a substantial development in non-invasive glucose monitoring.
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    Next-Gen smart homes: AI-enhanced Li-Fi for superior efficiency and protection
    (IEEE, 2025-09-04) Rajasubha, J.; Sathish, A.; Jackson, Beulah; Hemavathi, U.; Ajay, A. P.; Ali, Guma
    The system proposed optimizes dynamically the Li-Fi transmission parameters using a Deep Reinforcement Learning (DRL) adaptive modulation algorithm, resulting in a bit error rate of 0.00034 and a throughput of 986.45 Mbps. An intrusion detection system can enhance cybersecurity by utilizing Convolutional Neural Networks, which yield a detection rate of 99.81%. The future paradigm is more secure, reliable, and programmable than conventional Wi-Fi and fixed-modulation Li-Fi systems. The Li-Fi architecture with AI support possesses a unique edge in intelligent home applications because of its scalability and reliability compared to previous, less dependable wireless networks. This work promises a safe and effective form of data transmission by creating a platform for upcoming advancements in optical wireless communication based on AI. The Li-Fi system proposed that is supported by AI works far better on smart home wireless networking problems of low speeds, insufficient customization, and security. The optimized transmission parameters were used in real-time by the system through a DRL adaptive modulation approach, and this resulted in reducing the bit error rate to 0.00034 and boosting the throughput to 986.45 Mbps.