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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 2,901 Documents
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Effective crop categorization using wavelet transform based optimized long short-term memory technique Pompapathi, Manasani; Khaleelahmed, Shaik; Jawarneh, Malik; Naved, Mohd; Awasthy, Mohan; Srinivas Kumar, Seepuram; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7748

Abstract

Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop categorization. Image denoising is performed by WT. Denoising algorithms for images attempt to find a middle ground between totally removing all of the image’s noise and preserving essential, signal-free components of the picture in their original state. After denoising of images, crop image classification is achieved by LSTM and support vector machine (SVM) algorithm. LSTM has achieved 99.5% accuracy.
Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing J., Shreyas; S. Kharat, Reena; N. Phursule, Rajesh; Bhujanga Rao Madamanchi, Venkata; S. Rakshe, Dhananjay; Gupta, Gaurav; Jawarneh, Malik; F., Sammy; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8098

Abstract

Task scheduling is an essential component of any cloud computing architecture that seeks to cater to the requirements of its users in the most effective manner possible. It is essential in the process of assigning resources to new jobs while simultaneously optimising performance. Effective job scheduling is the only method by which it is possible to achieve the essential goals of any cloud computing architecture, including high performance, high profit, high utilisation, scalability, provision efficiency, and economy. This article gives a framework based on chaotic grey wolf optimization (CGWO) for efficiently scheduling tasks in cloud fog computing. Task scheduling is done with CGWO, ant colony optimization (ACO), and min-max algorithms. CloudSim is used to implement task scheduling algorithms. Makespan time required by CGWO algorithm for 500 tasks is 73.27 seconds. CGWO is taking minimum resources to accomplish the tasks in comparison to ACO and min-max methods. Response time of CGWO is also 3745.2 seconds. CGWO is performing better in terms of Makespan time, response time and resource utilization among the methods used in the experimental work.
Optimization of parabolic photovoltaic and solar thermal tracker model systems using heat transfer fluid Satria, Habib; Maizana, Dina; Dayana, Indri
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8988

Abstract

Renewable energy sources such as photovoltaic (PV) and solar thermal (T) used in tropical Indonesia are still not optimal in converting electrical energy. This is caused by the movement of the sun which is not centered on the surface of the photovoltaic/thermal (PV/T). Based on this, PV/T technology was developed with a parabolic tracker system combined with T control with the aim of optimizing electrical energy conversion. The testing technique was carried out using an experimental method by comparing the installation of PV/T technology with a parabolic tracker system and a flat PV position. In the PV/T parabolic tracker, the supporting components use aluminum foil technology and heat transfer fluid (HTF) to reduce excess heat on the panel surface. The parabolic tracker system is implemented through a centralized system following the sun and aluminum foil and HTF technology function to keep the surface temperature stable. The results of PV performance testing at peak loads carried out at 10:00 am-3:00 pm, the installation of flat PV produced an average DC power of 71.68 Wp and the parabolic tracker system produced an increase in average DC power of 84.57 Wp, while with an increase in power of 17.98% during fluctuating weather conditions.
No binding machine learning architecture for SDN controllers Hosny Fouad Aly, Wael; Kanj, Hassan; Mostafa, Nour; Al-Arnaout, Zakwan; Harb, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8483

Abstract

Although software-defined networking (SDN) has improved the network management process, but challenges persist in achieving efficient load balancing among distributed controllers. Present architectures often suffer from uneven load distribution, leading to significant performance deterioration. While dynamic binding mechanisms have been explored to address this issue, these mechanisms are complex and introduce a significant latency. This paper proposes SDNCTRLML , a novel approach that applies machine learning mechanisms to improve load balancing. SDNCTRLML introduces a scheduling layer that dynamically assigns flow requests to controllers using machine learning scheduling algorithms. Unlike previous approaches, SDNCTRLML integrates with the standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays. Experimental results show that SDNCTRLML has outperformed static-binding controllers models without adding complexities of dynamic-binding systems.
Technical-economic performance of fuel cell integration in autonomous hybrid systems Bayoud, Mebarka; Ghoudelbourk, Sihem; Mohamed Nassim Bouzidi, Belgacem
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8683

Abstract

Increasing energy demand and greenhouse gas emissions reinforce the importance of renewable resources in energy systems. This study evaluates the technical and economic viability of integrating a fuel cell (FC) in autonomous hybrid systems to supply a community in Algeria, with an average power of 6.91 kW and a daily energy requirement of 165.6 kWh. Four hybrid system configurations were compared using HOMER software: i) photovoltaic (PV) and batteries (BAT), ii) PV, BAT, and diesel generator (DG), iii) PV, BAT, and FC, and iv) PV, BAT, DG, and FC. The PV/BAT/DG/FC system was identified as the optimal configuration, balancing energy efficiency, reducing energy surpluses, reducing reliance on DG, and reducing CO2 emissions while maintaining competitive energy costs. These results demonstrate that the integration of FC can improve the sustainability and stability of autonomous hybrid energy systems.
QEMF for spatial domain pre-processing in iris biometrics: advancing accuracy and efficiency in recognition systems Sindugatta Nagaraja, Prajwalasimha; Kulkarani, Naveen; M. Ichangi, Raghavendra; Varanamkudath, Vinitha; Tadkal, Sharanabasappa; Parakkal, Ranjima; Karuppusamy, Deepthika
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9036

Abstract

This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.
Tri-level lung cancer classification via deep learning based GoogleNet with computed tomography images Rathinam, Vinoth; Arunagiri, Ramathilagam; Krishnasamy, Valarmathi; Rajendran, Sasireka
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9258

Abstract

Lung cancer (LC) is one of the most prevalent causes of cancer-related death worldwide. World Health Organization (WHO) classifies LC into two broad histological subtypes: non-small cell lung cancer (NSCLC) which is the cause of about 85% of cases and small cell lung cancer (SCLC) which makes up the remaining 15%. Several issues can influence LC detection including poor image quality, insufficient training data, low-quality image characteristics, and poor tumor localization. To overcome these challenges a novel TRI-level LC classification via deep learning-based GoogleNet with computed tomography (CT) images (TRI-LCNet) approach has been proposed for early-stage LC detection using CT images. Initially, the LC-input images CT are collected from openly accessible datasets. The lung CT images have been preprocessed using a Gaussian star filter (GaSF) to decrease noise, followed by feature extraction using GoogleNet. The extracted LC features are then given into a support vector machine (SVM) which is utilized as a classification tool to distinguish between different classes of LC cases. The TRI-LCNet approach performance was assessed by several metrics: specificity, accuracy, F1 score, and recall. The outcomes show that the suggested method obtains a higher accuracy range of 96.93% for the early identification of LC.
An efficient inductor-capacitor-inductor-capacitor compensation topology for wireless power transfer system Irshad, Talha; Nauman, Malik; Emeroylariffion Abas, Pg
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8728

Abstract

Wireless power transfer (WPT) systems provide a promising alternative for charging various applications, including electric vehicles (EVs), biomedical implants, smartphones, and network sensors. However, these systems often struggle to maintain high efficiency under varying loading and coupling conditions. This paper addresses these challenges by proposing a novel hybrid inductor-capacitor-inductor-capacitor (LC-LC) compensation topology. The proposed LC-LC topology is specifically designed to outperform conventional single-element compensation topologies, such as series-series (SS) and series-parallel (SP) configurations, by effectively reducing leakage inductance between coils. An analytical model of the LC-LC topology is developed and validated through simulations using Keysight advanced design system (ADS) software. The results demonstrate that the LC-LC topology not only achieves a peak efficiency of 99.6% under optimal conditions but also maintains superior performance compared to SS and SP topologies, with only a slight decrease to 93% efficiency observed at low load resistances. These findings highlight the potential of the LC-LC topology to significantly enhance WPT system efficiency across a range of operating conditions.
Predicting the intention to adopt e-zakat payment services: a machine learning approach Abdul Samad, Nor Hafiza; Abdul Rahman, Rahayu; Masrom, Suraya; Omar, Norliana; Che Hasan, Haslinawati
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8512

Abstract

The technology evolution in the zakat collection and payment services has brought about a profound transformation in the global processes of gathering and distributing charitable contributions. Despite witnessing a positive trend in annual zakat collection in Malaysia, it has yet to reach its optimal level. Therefore, predictions regarding performance and comparisons across multiple models for online zakat collection hold crucial significance in improving the overall collection rate. This paper, utilizing data from 230 zakat payers, presents an empirical assessment of various machine learning algorithms aimed at predicting zakat payer intentions when utilizing online platforms for zakat payments. Additionally, this paper presents the analysis of machine learning features importance to justify the effect of technology acceptance model (TAM) and theory of technology readiness (TR) attributes in the machine learning algorithms for predicting e-zakat payment service adoption intention. The findings show that many of the machine learning models are able to perform for highly accurate results, with most achieving over 80% accuracy. The most crucial attribute influencing these predictions was found to be the TAM. This study's methodology is designed to be easily replicable, allowing for further detailed exploration of both the influencing factors and the machine learning algorithms used.
Unmasking effects of feature selection and SMOTE-Tomek in tree-based random forest for scorch occurrence detection Dumebi Okpor, Margaret; Eluemnor Anazia, Kizito; Adigwe, Wilfred; Abugor Okpako, Ejaita; Moses Setiadi, De Rosal Ignatius; Adimabua Ojugo, Arnold; Omoruwou, Felix; Erhovwo Ako, Rita; Ochuko Geteloma, Victor; Valentine Ugbotu, Eferhire; Chukwudi Aghaunor, Tabitha; Enadona Oweimeito, Amanda
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8901

Abstract

Scorch occurrence during the production of flexible polyurethane foam has been a menace that consistently, jeopardize a foam’s integrity and resilience. It leads to foam suppression and compactness integrity failure due to scorch. There is always the increased likelihood of scorching, and makes crucial the utilization of methods that seek to avert it. Studies predict that the formation of foam constituent processes via optimization using machine learning have adequately trained models to effectively identify scorch occurrence during the profiling in the polyurethane foam production. Our study utilizes the random forest (RF) ensemble with feature selection (FS) and data balancing technique to identify production predictors. Study yields accuracy of 0.9998 with F1-score of 0.9819. Model yields 2-distinct cases for (non)-occurrence of scorch respectively, and the ensemble demonstrates that it can effectively and efficiently predict the occurrence of scorch in the production of flexible polyurethane foam manufacturing process.

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