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All Journal ComEngApp : Computer Engineering and Applications Journal
Nitit WangNo
roi et rajabhat university
Computer Science & IT Engineering

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A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients Nitit WangNo
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.460

Abstract

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.
A real-time intruder detection and notification system using the LBPH facial recognition method via the LINE application Nitit WangNo
Computer Engineering and Applications Journal Vol. 15 No. 2 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i2.1360

Abstract

Face detection and recognition in images and videos is a widely studied topic in the field of biometrics, with increasing importance in security and surveillance applications. This paper presents the development of a real-time face recognition system designed to enhance security and automation. The system utilizes a Haar-cascade classifier for initial face detection and the Local Binary Pattern Histogram (LBPH) algorithm for face recognition, based on a locally generated training dataset. It operates in two main stages: detecting human faces and identifying individuals. In cases where an unrecognized face is detected, the system sends an immediate alert via the LINE application. Key components of the system include real-time processing, identity verification, and access control. The proposed system shows strong potential for practical deployment in areas such as crowd monitoring and personal security in sensitive environments like airports. Experimental results demonstrate a recognition accuracy ranging from 90% to 93.45%, validating the effectiveness of the approach.
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