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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Armoogum, Sandhya
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Computer Science & IT Engineering Mechanical Engineering Transportation

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Enhancing biodegradable waste management in Mauritius through real-time computer vision-based sorting Suddul, Geerish; Babajee, Avitah; Rambarun, Nundjeet; Armoogum, Sandhya
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1119-1125

Abstract

Mauritius faces a significant waste management challenge due to the indiscriminate mixing of biodegradable and non-biodegradable waste. This practice hinders proper recycling and composting efforts, contributing to environmental pollution and resource depletion. This research proposes a computer vision-based system for real-time classification of waste into biodegradable and non-biodegradable categories. Transfer learning approach based on deep learning models, specifically DenseNet121, MobileNet, InceptionV3, VGG16 and VGG19 were evaluated with two different classifiers, the K-nearest neighbors (KNN) and support vector machine (SVM). Our experiments demonstrate that the MobileNet paired with SVM achieves a classification accuracy of 97% for detection in realtime. Compared to other studies, our results demonstrate better performance and realtime classification capabilities through the implementation of a prototype, facilitating automatic sorting of waste.
A Deep Learning Approach to Fake News Classification Using LSTM Andrianarisoa, Sitraka Herinambinina; Ravelonjara, Henri Michaël; Suddul, Geerish; Foogooa, Ravi; Armoogum, Sandhya; Sookarah, Doorgesh
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39360

Abstract

The rapid spread of misinformation on digital platforms poses a major challenge today. The ability to detect false information is essential to mitigate the associated harmful consequences. This research presents a deep learning approach for detecting fake news using Long Short-Term Memory (LSTM) model, which captures linguistic patterns and long-term dependencies in text. Our approach consists of optimizing the model through different experiments based on hyperparameter tuning, on a pre-processed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves high accuracy of 0.9974, with embedding dimension of 128 using 100 LSTM units, batch size of 64 and drop-out rate of 0.48. It is a substantial improvement over previous studies. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a fine-tuned LSTM network with robust data preprocessing can provide a powerful tool to combat online misinformation.
Co-Authors Andrianarisoa, Sitraka Herinambinina Babajee, Avitah Foogooa, Ravi Rambarun, Nundjeet Ravelonjara, Henri Michaël Sookarah, Doorgesh Suddul, Geerish