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All Journal International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science Aptisi Transactions on Technopreneurship (ATT) Science in Information Technology Letters
Ismail, Amelia Ritahani
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Journal : knowledge engineering and data science

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Change Vulnerability Forecasting for Southeast Asiausing Deep Learning Algorithm Ismail, Amelia Ritahani; Ali, Nur 'Atikah Binti Mohd; Sulaiman, Junaida
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (NDGAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.
Adam Optimization Algorithmfor Wide and Deep Neural Network Jais, Imran Khan Mohd; Ismail, Amelia Ritahani
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.
Social Distancing Monitoring System using Deep Learning Ismail, Amelia Ritahani; Affendy, Nur Shairah Muhd; Puzi, Asmarani Ahmad
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to carefully observe the social distancing of every single person in a surrounding, especially in crowded places. Real-time object detection can be proposed to improve the efficiency in monitoring the social distance SOP inspection. Therefore, in this paper, object detection using a deep neural network is proposed to help the authorities monitor social distancing even in crowded places. The proposed system uses the You Only Look Once (YOLO) v4 object detection models for the detection. The proposed system is tested on the MS COCO image dataset with a total of 330,000 images. The performance of mean average precision (mAP) accuracy and frame per second (FPS) of the proposed object detection is compared with Faster Region-based Convolutional Neural Network (R-CNN) and Multibox Single Shot Detector (SSD) model. Finally, the result is analyzed among all the models.
Deep Learning Approach for Dental Anomalies X-ray Imaging using YOLOv8 Ismail, Amelia Ritahani; Taseen, Md Salim Sadman
Knowledge Engineering and Data Science
Publisher : citeus

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Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a dataset of annotated dental X-ray images and preprocessed with data augmentation techniques to improve generalization. Performance was evaluated using Precision, Recall, F1-score, and Mean Average Precision (mAP). Additional insights were obtained from confusion matrices, precision-recall curves, and training-validation loss curves. The model achieved high precision in detecting Implants (0.90), Crowns (0.89), and Root Canal Treatment (0.69), demonstrating strong potential for clinical applications. However, Caries (0.30) and Periapical lesions (0.15) were detected with lower accuracy, indicating the need for further optimization. Analysis of training loss curves and label distributions suggested that class imbalance and anomaly co-occurrence influenced detection performance. YOLOv8 presents a promising AI-based solution for dental anomaly detection, capable of improving diagnostic efficiency and accuracy in clinical practice. The model’s integration into dental healthcare systems can reduce radiologists' workload and enhance early disease detection, particularly in resource-limited settings.
Co-Authors Affendy, Nur Shairah Muhd Afiqa, Nurul Aji Prasetya Wibawa Ali, Nur 'Atikah Binti Mohd Andri Pranolo Ara, Muhammad Afiq Awang, Suryanti Azlan, Faris Farhan Ghazali, Ahmad Badaruddin Ismail, Ahsiah Jais, Imran Khan Mohd Nisa, Syed Qamrun Noormaizan, Khairul Akmal Puzi, Asmarani Ahmad Ramli, Roziana Saifullah, Shoffan Shaharuddin, Nur Azri Simbolon, Triyanti Sulaiman, Junaida Taseen, Md Salim Sadman Zaeni, Ilham Ari Elbaith