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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT)
Abdul Hamid, Nurzeatul Hamimah
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Computer Science & IT Engineering

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Empowering anomaly detection algorithm: a review Iqbal Basheer, Muhammad Yunus; Mohd Ali, Azliza; Osman, Rozianawaty; Abdul Hamid, Nurzeatul Hamimah; Nordin, Sharifalillah; Mohd Ariffin, Muhammad Azizi; Iglesias Martínez, José Antonio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp9-22

Abstract

Detecting anomalies in a data stream relevant to domains like intrusion detection, fraud detection, security in sensor networks, or event detection in internet of things (IoT) environments is a growing field of research. For instance, the use of surveillance cameras installed everywhere that is usually governed by human experts. However, when many cameras are involved, more human expertise is needed, thus making it expensive. Hence, researchers worldwide are trying to invent the best-automated algorithm to detect abnormal behavior using real-time data. The designed algorithm for this purpose may contain gaps that could differentiate the qualities in specific domains. Therefore, this study presents a review of anomaly detection algorithms, introducing the gap that presents the advantages and disadvantages of these algorithms. Since many works of literature were reviewed in this review, it is expected to aid researchers in closing this gap in the future.
Review of NLP in EMR: abbreviation, diagnosis, and ICD classification Iqbal Basheer, Nurul Anis Balqis; Nordin, Sharifalillah; Kasim, Sazzli Shahlan; Ali, Azliza Mohd; Abdul Hamid, Nurzeatul Hamimah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp881-891

Abstract

This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.
Co-Authors Ali, Azliza Mohd Iglesias Martínez, José Antonio Iqbal Basheer, Muhammad Yunus Iqbal Basheer, Nurul Anis Balqis Kasim, Sazzli Shahlan Mohd Ali, Azliza Mohd Ariffin, Muhammad Azizi Nordin, Sharifalillah Osman, Rozianawaty