Conference Proceedings

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Browsing Conference Proceedings by Subject "Artificial intelligence"

Now showing 1 - 4 of 4
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    AI-driven pathogen detection systems for rapid and accurate diagnosis
    (IEEE, 2025-09-24) Deepika, M.; Murugan, Sundara Bala; Subasini, V.; Rufus, N. Herald Anantha; Atkinswestley, A.; Ali, Guma
    Pathogen detection at speed along with precision serves as a basis for disease diagnosis and overall control effectiveness. Several current detection methods based on culture-based techniques and PCR face drawbacks of extended processing times, high expenses, and the need for expert operators. It adopts deep learning Convolutional Neural Networks with an attention mechanism in a system designed to overcome existing limitations of pathogen detection. The model was trained using a multi-modal dataset that combined spectral and biological signals through feature extraction optimization and privacy-protected training methods based on federated learning. The proposed system reaches a 96.8% detection accuracy together with 15% reduced false alarm rates and 35% faster operation speeds than conventional models. Experimental data measurements showed that this system achieved 92.3% F1-score together with 97.1% AUC for real-time identification of pathogens. AI has proven its effectiveness through these findings in changing the way pathogen diagnostics operate. Further work will concentrate on extending datasets while creating real-time deployment protocols for clinical use.
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    AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture
    (IEEE, 2025-12-29) Sundaram, N. Kalyana; Rajendran, Megala; Ehssan, Muhamed; Soy, Aakansha; Anandhi, K.; Begum, T Ummal Sariba; Ali, Guma; Dhananjaya, B
    By advising and teaching farmers on how to apply modern farm practices that embrace Artificial Intelligence (AI) and the Internet of Things (IoT), precision agriculture is revolutionising sustainable farming by optimising for usages that are as much as possible and waste as little as can be afforded. In this research, we propose an AIoT-driven Smart Agri Grid (ASAG) framework that integrates real-time nanosensor networks, an AI-operational control microclimate, an autonomous decision-support system, and secure data sharing via a blockchain using encrypted statistical data. To achieve real-time analytics, edge computing is used in the framework for real-time data analytics, predictive algorithms for dynamic irrigation & nutrient management, and federated learning for distributed AI training, which maintains privacy and scalability. In addition, the system uses AI-based waste-minimisation techniques, such as predictive harvest timing and the conversion of bio-waste into organic fertilisers, thereby reducing post-harvest losses. Experimental results show that ASAG can improve crop yield by 20 to 30%, reduce water waste by up to 50%, and reduce chemical overuse by up to 30%, with its economic and environmental benefits. The feasibility of such deployment on a large scale in precision agriculture is further confirmed by a cost-benefit analysis. The results reinforce the power of AI and IoT in transforming contemporary farming into a self-optimising, climate-resilient system. For long-term sustainability in global agriculture, quantum AI will be used to predict soil health, monitor AI-assisted carbon sequestration, and enable genomic AI for climate-resistant crops.
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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.