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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Markom, Arni Munira
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Digital technologies evolution in swiftlet farming: a systematic literature review Markom, Arni Munira; Yusof, Yusrina; Markom, Marni Azira; Haris, Hazlihan; Muhammad, Ahmad Razif
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4456-4470

Abstract

The integration of cutting-edge technologies into swiftlet farming has greatly enhanced efficiency, productivity, and sustainability. The internet of things (IoT) provides farmers with up-to-date environmental data, enabling them to create and sustain ideal circumstances for swiftlets. Artificial intelligence (AI) enhances this process by analysing vast databases and providing farmers with well-informed choices to optimize yield. Biotechnology, by combining genetic selection and breeding programs, effectively connects with the IoT, enabling constant monitoring and control of the health and genetic traits of swiftlets. The integration of renewable energy technology seeks to diminish dependence on conventional energy sources, promoting sustainability. In this paper, a systematic review of the literature is examined the utilization of digital technology in the swiftlet farmhouse. The findings were classified into three main themes: smart monitoring and control systems, advanced bird detection techniques, and sustainable practices and innovative approaches, specifically in the manufacture of edible bird nest. This systematic literature review emphasizes the multidisciplinary nature of swiftlet farming's technological evolution, technology developers, challenges and recommendations that farmers and the industry face in their pursuit of sustainable growth.
Integrating time-frequency features with deep learning for lung sound classification Chang, Su Yuan; Markom, Marni Azira; Choong, Zhi Sheng; Markom, Arni Munira; Kamaruddin, Latifah Munirah; Tan, Erdy Sulino Mohd Muslim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3737-3747

Abstract

Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Co-Authors Ahmad Jafri, Nur Aqilah Awalin, Lilik Jamilatul Burham, Norhafizah Chang, Su Yuan Choong, Zhi Sheng Haris, Hazlihan Harun, Sulaiman Wadi Hasnan, Megat Muhammad Ikhsan Megat Kamaruddin, Latifah Munirah Markom, Marni Azira Muhammad, Ahmad Razif Mustaffa, Siti Nasuha Saad, Ismail Tan, Erdy Sulino Mohd Muslim Tan, Sin Jin Yusof, Yusrina