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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 73 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 73 Documents clear
Automated real-time cervical cancer diagnosis using NVIDIA Jetson Nano Mulmule, Pallavi; Shilaskar, Swati; Bhatlawande, Shripad; Mulmule, Vedant; H Kamble, Vaishali; Madake, Jyoti
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.v14i5.10169

Abstract

Cervical cancer is a global health concern, making early detection critical for ensuring effective treatment outcomes. Screening technique, the Papanicolaou test (Pap test), has been adopted globally for timely detection. Nevertheless, the process of screening is subjective. The current study aims to advance the development of an automated real time framework for cervical cell analysis for early-stage diagnosis using supervised classification on NVIDIA Jetson Nano platform. Our approach, leveraging adaptive fuzzy k-means (AFKM) clustering and k-means clustering, extracts distinctive features from cervical cell images for accurate classification. Utilizing multilayer perceptron (MLP) and support vector machine (SVM) classifiers, we achieved a classification accuracy of 97%, highlighting the potential of our system for real-time applications in cervical cancer investigation. Validation by two expert pathologists further supports the system’s practical utility.
The use of fiber bragg grating coated with polyimide for CO2 gas sensor Irawan, Dedi; Saktioto, Saktioto; Azhar, Azhar; Sutoyo, Sutoyo; Sahal, Muhammad; Hanto, Dwi; Widiyatmoko, Bambang
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.v14i5.9283

Abstract

This study presents the application of fiber bragg grating (FBG) sensors coated with polyimide for detecting carbon dioxide (CO₂) gas, employing both theoretical and experimental approaches. The basic FBG components were coated with polyimide layers of varying thicknesses. Subsequently, the fabricated FBG sensors were characterized using an optical interrogator system with four channels. Furthermore, the sensor was tested for CO₂ detection at a working temperature of 47 °C. Experimental data showed that the FBG sensor coated with polyimide layers of 10 nm, 15 nm, and 20 nm demonstrated sensitivities of 1.9 ppm, 1.84 ppm, and 1.8 ppm, respectively. In contrast, the uncoated FBG sensor exhibited a higher sensitivity of 3 ppm. Increasing the coating thickness beyond 20 nm leads to a decrease in sensor sensitivity. The findings suggest that an optimal polyimide coating thickness for CO₂ detection using FBG sensors is around 20 nm. Achieving high sensitivity in CO₂ gas sensors is crucial for their effective use across a broad range of applications.
A hybrid extreme machine learning model for predicting heart disease M. Ahmed, Abdelmoty; Bataineh, Bilal; Shakah, Ghazi; O. Al Enany, Marwa; M. Aboghazalah, Maie; M. Khattab, Mahmoud
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.v14i5.10028

Abstract

Heart disease (HD), the leading cause of death for adults over 65, can affect anyone at any time. Additionally, modern lifestyles, poor diets, and other factors have led to an increased risk of HD among teenagers. One significant challenge is managing and analysing vast amounts of data, often surpassing terabytes, which is crucial for researching, diagnosing, and predicting cardiovascular diseases quickly. To enhance primary health care, especially in early and rapid diagnosis of heart attacks and to assist less experienced doctors in understanding clinical HD data, we propose a hybrid method called the "hybrid extreme machine learning model (HEMLM)". This technique combines the strengths of multi-layer perceptron (MLP), random layers, and logistic regression (LR). The model offers various feature patterns and multiple classification techniques. Compared to support vector machine (SVM), LR, and Naive Bayes (NB), the HEMLM algorithm demonstrates superior performance and efficiency. Testing results show identification accuracies of 94.91%, 94.77%, 92.42%, and 87.14% for data splitting ratios of 85:15, 80:20, 70:30, and 60:40, respectively.

Page 8 of 8 | Total Record : 73

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