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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Chai, Ian
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Computer Science & IT Electrical & Electronics Engineering

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Cross-cultural prediction of marital satisfaction using machine learning algorithms and generic needs Sponge, Khye; Ng, Kok-Why; Ting, Choo-Yee; Chai, Ian
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9055

Abstract

Marital satisfaction is crucial for individual well-being and family stability. Prior research has predominantly focused on Western contexts using traditional statistical models, limiting the generalizability of findings across cultures. This study addresses a significant gap by employing machine learning algorithms Naive Bayes, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) on a diverse dataset comprising responses from 7,178 participants across 33 countries. Our methodology includes a robust data preprocessing pipeline, feature selection, and algorithm evaluation, emphasizing their practical application in relationship interventions. Using predictors derived from Maslow's generic needs, including love, respect, and pride in one's spouse, we demonstrate that these factors are significant cross-cultural predictors of marital satisfaction. Our results show that pride in spouse, love, and respect for spouse are the most significant predictors of marital satisfaction across cultures. This demonstrates the effectiveness of machine learning in capturing complex relationships, offering more accurate predictions than traditional methods. These findings suggest that fostering love, respect, and sacrifice in early relationships can significantly enhance marital satisfaction across diverse cultural contexts.
Applying Data Mining on Personal Computer for Document Classification Chai, Ian; Salleh, Ahmad Zarif
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3473

Abstract

The typical user creates documents over many years of computer usage. As people move from computer to computer, they tend to copy the files to the new computer, because "you never know when we might need to refer to something from the past." Hence, the collection grows larger and larger, expanding to hundreds and thousands. This collection soon exceeds the ability of most people to remember what each document was, even if they have been keeping them in some order in folders – and many people fail to anticipate how the folders and subfolders should be arranged as time passes – and by the time they realize it, most find it too daunting a task to reclassify them all manually. Therefore, we sought to solve this problem using a data mining-based solution, specifically multinomial naive Bayes. We developed a document classification program to automatically categorize all documents stored on a person's personal computer hard drive, eliminating the need for manual classification. The proposed algorithm achieved a score of 0.853 for accuracy, 9,833 for precision, 0.661 for recall, and 0.767 for the F1 metric. It should be possible, with further refinement and improvement, for example by balancing the dataset and increasing its size, for this technique to be applied in practical applications that enable automatic document classifications on the computers of most computer users.
Integrating Spatial Computing with Clinical Pathology for Enhanced Diagnosis and Treatment Informatics in Healthcare Chituru, Chinwe Miracle; Ho, Sin-Ban; Chai, Ian
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2951

Abstract

This paper investigates spatial computing, which is a pathological transformational modern technology that integrates the physical and digital realms and has the potential to revolutionize pathology healthcare. Pathology as a medical specialist plays a crucial role in patient care by providing essential information for diagnosis, treatment planning, and disease monitoring. It studies and diagnoses diseases by examining tissues, organs, bodily fluids, and cells. Pathology is a broad field with three main branches: Anatomic pathology, Clinical pathology, and Molecular pathology. This study investigates the possibilities of spatial computing in radiography and clinical pathology with emphasis on diagnosis accuracy, medical education, workflow efficiency, and the outcomes in the patients. Augmented Reality (AR) medical devices guide pathologists in real-time during diagnostics procedures. The digital reproduction of tissue samples to allow pathologists to examine specimens in three dimensions is a significant utilization of spatial computing in virtual microscopy. This process allows remote collaboration between pathologists and laboratories, provides health informatics as seen in electronic health records (EHRs), improves diagnosis, and presents a platform with learning experiences in the medical field. Patients can interact with three-dimensional simulations of their anatomy, which helps them make more educated treatment decisions provided via the pathology findings and treatment alternatives in an immersive format. As this technology advances, its potential to transform pathology practice and improve patient care remains high. This review describes technological perspectives and discusses the statistical methods, clinical applications, potential obstacles, and directions of spatial computing in clinical pathology.
Co-Authors Chituru, Chinwe Miracle Ho, Sin-Ban Ng, Kok-Why Salleh, Ahmad Zarif Sponge, Khye Ting, Choo-Yee