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All Journal JOIV : International Journal on Informatics Visualization
Goh, Kah Ong Michael
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Predicting Different Classes of Alzheimer's Disease using Transfer Learning and Ensemble Classifier Tamim, Mubasshar-Ul-Ishraq; Malik, Sumaiya; Sneha, Soily Ghosh; Mahmud, S M Hasan; Goh, Kah Ong Michael; Nandi, Dip
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3038

Abstract

Alzheimer's disease (AD), the most prevalent cause of dementia, affects over 55 million individuals globally. With aging populations, AD cases are expected to increase substantially, presenting a pressing public health challenge. Early diagnosis is crucial but remains challenging, particularly in the mild cognitive impairment stage before extensive neurodegeneration. Existing diagnostic methods often fall short due to the subtle nature of early AD symptoms, highlighting the need for more accurate and efficient approaches. In response to this challenge, we introduce a hybrid framework to enhance the diagnosis of Alzheimer's Disease (AD) across four classes by integrating various deep learning (DL) and machine learning (ML) techniques on an MRI image dataset. We applied multiple preprocessing techniques to the MRI images. Then, the methodology employs three pre-trained convolutional neural networks (CNNs): VGG-16, VGG-19, and MobileNet - each undergoing training under diverse parameter settings through transfer learning to facilitate the extraction of meaningful features from images, utilizing convolution and pooling layers. Subsequently, for feature selection, a decision tree-based RFE method was employed to iteratively select the most significant features and enable more accurate AD classification. Finally, an XGBoost classifier was used to classify the multiclass types of AD under 5-fold cross-validation to assess the performance of our proposed model. The proposed model achieved the highest accuracy of 93% for multiclass classification, indicating that our approach significantly outperforms state-of-the-art methods. This model could apply to clinical applications, marking a significant advancement in AD diagnostics.
Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions Tan, Shao Xian; Ong, Jia You; Goh, Kah Ong Michael; Tee, Connie
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2313

Abstract

In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. This study proposes techniques to enhance vehicle object detection and classification using augmentation methods based on the YOLO (You Only Look Once) network. The primary objective of the trained model is to generate a local vehicle detection system for Malaysia which have the capacity to detect vehicles manufactured in Malaysia, adapt to the specific environmental factors in Malaysia, and accommodate varying lighting conditions prevalent in Malaysia. The dataset used for this paper to develop and evaluate the proposed system was provided by a highway company, which captured a comprehensive top-down view of the highway using a surveillance camera. Rigorous manual annotation was employed to ensure accurate annotations within the dataset. Various image augmentation techniques were also applied to enhance the dataset's diversity and improve the system's robustness. Experiments were conducted using different versions of the YOLO network, such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8, each with varying hyperparameter settings. These experiments aimed to identify the optimal configuration for the given dataset. The experimental results demonstrated the superiority of YOLOv8 over other YOLO versions, achieving an impressive mean average precision of 97.9% for vehicle detection. Moreover, data augmentation effectively solves the issues of overfitting and data imbalance while providing diverse perspectives in the dataset. Future research can focus on optimizing computational efficiency for real-time applications and large-scale deployments.
A Robust License Plate Detection System Using Smart Device Bin Mohamad Azhar, Muhammad Darwish; Goh, Kah Ong Michael; Check Yee, Law; Connie, Tee
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2287

Abstract

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.
Visual Analytic for Traffic Impact Assessment Chan, Jia Chun; Fahad, Nafiz; Goh, Kah Ong Michael; Tee, Connie
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2314

Abstract

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.
Co-Authors Bin Mohamad Azhar, Muhammad Darwish Chan, Jia Chun Check Yee, Law Connie, Tee Fahad, Nafiz Mahmud, S M Hasan Malik, Sumaiya Nandi, Dip Ong, Jia You Sneha, Soily Ghosh Tamim, Mubasshar-Ul-Ishraq Tan, Shao Xian Tee, Connie