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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Ting, Choo-Yee
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Computer Science & IT Electrical & Electronics Engineering

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Journal : joiv international journal on informatics visualization

 1 
Detecting Need-Attention Patients using Machine Learning Law, Theng Jia; Ting, Choo-Yee; Zakariah, Helmi
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.8.2.2277

Abstract

In healthcare, detecting patients who need immediate attention is difficult. Identifying the critical variables is challenging in patient detection because human intervention in variable selection is required. Consequently, patients who need immediate attention often experience prolonged waiting times. Researchers have investigated various approaches to identify those who require attention. One of the techniques is leveraging Artificial Intelligence (AI). However, identifying the optimal feature set and predictive model is complex. Therefore, this study has attempted to (i) identify the critical features and (ii) develop and evaluate predictive models in detecting those who need attention. The dataset is collected from one of the healthcare companies. The dataset collected contains 67 variables and 51102 records. It consists of patient information and questionnaires answered by each participant registered in the Selangor Saring Program. Important features were identified in detecting those who need attention on treated data. Multiple classifiers were developed due to their simplicity. The models were evaluated before and after hyperparameter tuning based on accuracy, precision, recall, F1-score, Geometric Mean, and Area Under the Curve. The findings showed that the Stacking Classifier produced the highest accuracy (69.9%) when using the blood dataset. In contrast, Extreme Gradient Boosting achieved the highest accuracy (81.7%) when the urine dataset was used. This work can be extended to explore the incorporation of Points of Interest and geographical data near patients’ residences and study other ensemble models to enhance the performance of detecting those who need attention.
Optimising iCadet Assignment through User Profiling Fei, Yap Peak; Ting, Choo-Yee; Abdul-Rashid, Hairul A.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3470

Abstract

Industry Cadetship programme is a programme that assigns penultimate year students to companies matching their profiles, bridging academic learning and industry skills.  Manual data analysis for assignments is time-intensive, prompting this study’s objectives: (i) propose an algorithm to optimize student-company assignment by using the student and company profiles, (ii) propose a method for the assignment of lecturers to company, and (iii) use similarity measure techniques to recommend companies with similar characteristics. Data was collected from a university's student, company, and lecturer datasets. To assign students to companies, the Haversine, OpenStreetMap, and NetworkX were used to calculate the shortest geographical distance between the students and the companies; evaluated based on mean, variance, standard deviation, and utilization rate. For the lecturer assignment, cosine similarity was applied to measure the similarity between domain descriptions and company or lecturer information after performing Voyage AI embeddings. Lecturers are assigned to companies based on the highest domain similarity scores. The performance was evaluated using accuracy, precision, recall, and F1- score.  Findings showed embedding techniques significantly enhanced the matching process, with accuracy improved from 0.464 to 0.6071, precision increased from 0.417 to 0.5058, recall saw an equal rise from 0.464 to 0.6071, and the F1-score advanced from 0.417 to 0.5264. Longer descriptive inputs further improved performance, with accuracy rising from 0.6154 to 0.7692, precision from 0.5744 to 0.7751, recall remaining steady at 0.7692, and F1-score increasing from 0.5807 to 0.7484. This work can be extended to explore job portal dataset by aligning profiles with geography and specialization.
Identifying Fraud Sellers in E-Commerce Platform Anand, Lovesh; Goh, Hui-Ngo; Ting, Choo-Yee; Quek, Albert
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3479

Abstract

The identification of fake reviews in e-commerce is crucial as they might impact the purchasing decisions and overall satisfaction of buyers. This work investigates the effectiveness of machine learning and transformer-based models for detecting fake reviews on the Amazon Fake Review Labelled Dataset. The dataset contains 20,000 computer-generated and 20,000 original reviews across various product categories with no missing values. In this study, machine learning and transformer-based models were compared, revealing that transformer-based models outperformed in terms of accuracy in detecting fake reviews, achieving an accuracy of 98% with the DistilBERT model. Additionally, this work too examines the impact of word embedding on machine learning models in enhancing fake review detection accuracy. The results show that the word embedding model Word2Vec displays notable improvements, achieving accuracies of 92% with SVM and 90% with Random Forest and Logistic Regression. Furthermore, a comparison study being carried out on comparing transformer models from previous work, which utilized the same full dataset, it was found that the DistilBERT model produced comparable accuracy despite its lighter architecture. In summary, this study underscores the effectiveness of transformer-based models and machine learning models in detecting fake reviews while at the same time highlighting the importance of word embedding techniques in enhancing the performance of machine learning models. With this work, it is hope that it would contribute to combating fake reviews and fostering trust in e-commerce platforms.
No-Show Passenger Prediction for Flights Chin, Wei-Song; Ting, Choo-Yee; Cham, Chin-Leei
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2328

Abstract

In aviation, “no-show” refers to a customer who booked a reservation but failed to show up. No-shows can result in various resource wastes, such as vacant seats, leading to income loss and flight delays. As a result, no-show passengers can cause considerable problems for airlines, ultimately affecting their bottom line. Recent research has shown the use of machine learning algorithms to reduce the rate of no-shows. For example, a researcher in healthcare is using a predictive model to identify no-shows’ patients to increase efficiency. Therefore, this study aimed to develop prediction models to predict passenger no-shows. In this work, we used a dataset supplied by a local airline company consisting of 1,046,486 rows and 8 columns. Additional datasets like weather data, public holiday data of different countries, aircraft details, and foot traffic data are used to carry out the dataset's feature enrichment task to complement the original dataset. As a result, feature selection has become an important stage in this research to identify and pick the most relevant and useful features from the enormous number of columns. The findings showed that the model built using Random Forest has the highest accuracy of 90.4%, while Decision Tree performed at 90.2%, Gradient Boosting at 86.5%, and Neural Networks at 67.6%. To enhance the accuracy of the models, further research efforts are essential to integrate supplementary passenger information.
Co-Authors Abdul-Rashid, Hairul A. Anand, Lovesh Chai, Ian Cham, Chin-Leei Chin, Wei-Song Fei, Yap Peak Goh, Hui-Ngo Law, Theng Jia Ng, Kok-Why Quek, Albert Sponge, Khye Zakariah, Helmi