Items where Author is "Sreejith, Reshma"
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Article
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 (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
Conference or Workshop Item
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.