Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization
Su-Cheng, Haw
Unknown Affiliation
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques Tong-Ern, Tong; Su-Cheng, Haw; Kok-Why, Ng; Al-Tarawneh, Mutaz; Gee-Kok, Tong
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.8.2.2305

Abstract

The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF). 
A Comprehensive Review on Cancer Detection and Classification using Medical Images by Machine Learning and Deep Learning Models J, Jayapradha; Su-Cheng, Haw; Naveen, Palanichamy; Anaam, Elham Abdulwahab
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.8.3-2.3061

Abstract

In day-to-day life, machine learning and deep learning plays a vital role in healthcare applications to predict various diseases such as cancer, heart attack, mental problem, Parkinson, etc. Among these diseases, cancer is the life-threatening disease that leads a human being to death. The primary aim of this study is to provide a quick overview of various cancers and provides a comprehensive overview of machine learning and deep learning techniques in the detection and classification of several types of cancers. The significance of machine learning and deep learning in detecting various cancers using medical images were concentrated in this study. It also discusses various machine learning and deep learning algorithms that lead to accurate classification of medical images, early diagnosis, and immediate treatment for the patients and explores the methodologies which has been used to predict the cancer with the help of low dose computer tomography to reduce cancer related deaths. As the study narrows down the research into lung cancer, it combats the findings limitations in lung cancer detection models and highlights the need for a deep study of novel cancer detection algorithms. In addition, the review also finds the role of setting up data in lung cancer and the potential of genetic markers in stabilizing the accuracy of machine learning models. Overall, this study gives valuable suggestions to achieve more accuracy in cancer detection and classification using machine learning and deep learning techniques. 
Domain-Independent True Fact Identification from Knowledge Graph Govindapillai, Sini; Lay-Ki, Soon; Su-Cheng, Haw
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.9.3.3690

Abstract

The trustworthiness of information in the Knowledge Graph (KG) is determined by the trustworthiness of information at the fact level. KGs are incomplete and noisy. Yet, most existing error detection approaches were applied to specific KGs. A large percentage of error detection approaches work well on DBpedia, particularly. However, we do not have a single KG containing all the information regarding the entity relations of a specific entity from any random class. The main objective of this research is to increase the trustworthiness of entity relations from KGs. In this paper, we propose a framework for identifying fact entity information that combines two independent approaches from knowledge graphs, ensuring the accuracy of triples. The first approach detects true facts of entity information from various KGs by integrating Linked Open Data (LOD), string similarity measures, and semantic similarity measures. Next, we propose an error detection and correction approach using RDF Reification on the integrated environment, independent of any particular KG. The research was conducted on related and diverse knowledge graphs, DBpedia, YAGO and Wikidata. In addition, the effectiveness of RDF reification for identifying true facts is evaluated on Wikidata on selected entities. The proposed framework provides a flexible framework for improving data quality across multiple KGs, enabling broader applicability in data integration and semantic search domains. Future work will explore extending this approach to deep learning models with additional features like entity type and path for error detection and correction in real-time KG applications.
Co-Authors Al-Tarawneh, Mutaz Anaam, Elham Abdulwahab Gee-Kok, Tong Govindapillai, Sini J, Jayapradha Kok-Why, Ng Lay-Ki, Soon Naveen, Palanichamy Tong-Ern, Tong