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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.
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Articles 75 Documents
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Search results for , issue "Vol 14, No 3: June 2025" : 75 Documents clear
Detecting spam using Harris Hawks optimizer as a feature selection algorithm Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Nabil Alkhatib, Sumaya; Shambour, Qusai Y.; Alsaaidah, Adeeb M.
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.v14i3.9198

Abstract

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.
Improved imperceptible engagement-based 2D sigmoid logistic maps, Hill cipher, and Kronecker XOR product Lestiawan, Heru; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Purwanto, Purwanto; Sari, Christy Atika; Doheir, Mohamed
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.v14i3.8331

Abstract

Image encryption is a crucial facet of secure data transmission and storage, and this study explores the efficacy of combining sigmoid logistic maps (SLM), Hill cipher, and Kronecker's product method in enhancing image encryption processes. The evaluation, conducted on diverse images such as Lena, Rice, Peppers, Cameraman, and Baboon, unveils noteworthy findings. The Lena image emerges as the most successfully encrypted, as evidenced by the lowest mean squared error (MSE) at 92.81 and the highest peak signal-to-noise ratio (PSNR) at 19.43, reflecting superior fidelity and quality preservation. Additionally, the encryption of 64×64 pixels images consistently demonstrate robustness, with a high number of pixels change rate (NPCR) and unified average change intensity (UACI) values, particularly notable for the Cameraman image. Even for 128×128 pixels images, commendable encryption performance persists across the tested images. The amalgamation of SLM, Hill cipher, and Kronecker's product emerges as an effective strategy for balancing security and perceptual quality in image encryption, with the Lena image consistently outperforming others based on comprehensive metrics. This research provides valuable insights for future studies in the dynamic domain of image encryption, emphasizing the potential of advanced cryptographic techniques in ensuring secure multimedia communication.
Development of a robust and sustainable regional demography-based demand management technique Ganguly, Ayandeep; Kumar Sil, Arindam
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.v14i3.9133

Abstract

This paper presents a robust and sustainable energy management system driven by regional demographic patterns developed using fuzzy logic and mixed integer linear programming (MILP). This method detects and integrates variations in the energy use patterns of urban and rural communities attaining improved efficiency in the management of regional power demand. The detection and integration of the urban and rural energy use patterns were done by combining period partitioning based regional time of use tariff and fuzzy based appliance level renewable resource allocation to develop a function to be optimized using an improved MILP which provides users with the optimum schedule of appliance usage based on their demographic classification. The effectiveness of the proposed method was tested by running MATLAB simulations of different scenarios emulating continuous regional renewable integration planning with urban and rural power consumption profiles generated using LoadProGen. The proposed method’s effectiveness is confirmed by the achievement of a reduction upto 31% in the community energy cost as well as significant reduction in the energy costs of each participant over different scenarios compared to the unoptimized base case. The proposed method can be effectively utilized in energy management applications catering to multiregional and mixed demographic communities.
Automatic urinary bladder detection from medical computed tomography scans using convolutional neural network Mahdy Omran, Lamia Nabil; Ali Ezzat, Kadry; El Fadaly, Hossam Ahmed; I. Hussein, Aziza; Gameil Shehata, Emad; Mansour Salama, Gerges
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.v14i3.8975

Abstract

This paper introduces a system for detecting and evaluating an algorithm that segments the urinary bladder in medical images obtained from contrast-less computed tomography (CT) scans of patients with bladder tumors. Multiple segmentation methods are needed in situations where tumors in the bladder cause structural changes that appear as irregularities in images, complicating the slicing process. The segmentation process begins with viewing the urinary bladder DICOM in three different perspectives, and then enhancing the image to expand the dataset. Next, the areas of the urinary bladder are pinpointed, with the urinary bladder dataset being split into 70% for training and 30% for testing to distinguish it from the nearby tissues, organs, and bones. The suggested system was evaluated on eight 3D CT images obtained from the cancer imaging archive (TCIA). Results from the experiment show that the designed system is effective in identifying and delineating the urinary bladder.
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.

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