Work place: School of Computing and Creative Media, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia
E-mail: marcella@uts.edu.my
Website: https://orcid.org/0009-0005-7349-8625
Research Interests:
Biography
Marcella Peter holds a Master’s Degree in Computer Science. Her research lies in the field of face image synthesis, where she leverages statistical methods to create realistic and synthetic facial images. Passionate about the intersection of computer vision and mobile technology, her research also explores the potential of extended reality in mobile applications.
By Michael Chi Seng Tang Siew Ping Yiiong Kee Chuong Ting Sing Ling Ong Marcella Peter Khairunnisa Ibrahim
DOI: https://doi.org/10.5815/ijem.2025.05.05, Pub. Date: 8 Oct. 2025
This study looks at how well a Support Vector Machine (SVM) with a quadratic polynomial kernel works for detecting Monkeypox. The SVM method is compared to other machine learning models like Neural Networks, KNN, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes. By using features from medical images called Local Binary Patterns (LBP), the SVM model showed the best results, with 93.33% accuracy, 95.24% recall, 91.67% true negative rate, and 90.91% precision. The LBP features are used because they exhibit unique textural patterns that can distinguish Monkeypox and normal cases. The results show that the SVM with this kernel is good at telling the difference between Monkeypox and normal cases, making it a helpful tool for early detection in healthcare.
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