Items where Author is "Sreejith, Reshma"

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Sreejith, Reshma and Kanesaraj Ramasamy, R. and Rajendran, Venushini and Haqe, Nurul Azwaani Salehuddin and Ejaz, Muhammad Abdullah and Hamzah, Faizal Amri and Md Jamal, Shamsuriani and Thanjappan, Sivasutha (2026) A Comparative Study of Random Forest and Convolutional Neural Network for Lung Sound Classification. Lecture Notes in Networks and Systems, 1525. pp. 467-479. ISSN 2367-3370

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi (2026) Enhancing Respiratory Disease Diagnosis: Evaluating the Efficiency and Generalizability of AI-Driven Lung Sound Analysis Models. Lecture Notes in Networks and Systems, 1525. pp. 449-465. ISSN 2367-3370

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi and Md. Jamal, Shamsuriani and Amri Hamzah, Faizal and Thanjappan, Sivasutha (2026) RespireNet: Enhancing Lung Sound Classification using CNN-TCN Hybrid Approach. In: AIAT 2025: 2025 5th International Conference on Artificial Intelligence and Application Technologies, Kyoto, Japan, 4-6 December 2025.

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi (2026) Review of advancements in AI-assisted lung sound analysis for respiratory illness diagnosis in noisy environments. IAES International Journal of Artificial Intelligence (IJ-AI), 15 (3). p. 2863. ISSN 2089-4872

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi and Thanjappan, Sivasutha and Hamzah, Faizal Amri and Jamal, Shamsuriani Bt Md (2025) Energy-Efficient COPD Detection Using an Optimized Deep Learning Model for Medical Systems. Studies in Systems, Decision and Control, 629. pp. 39-55. ISSN 2198-4182

Sreejith, Reshma and Ramasamy, R. Kanesaraj and Mohd Isa, Wan Noorshahida and Abdullah, Junaidi (2025) Enhanced Lung Disease Classification Using CALMNet: A Hybrid CNN-LSTM-TimeDistributed Model for Respiratory Sound Analysis. IEEE Access, 13. pp. 135053-135073. ISSN 2169-3536

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