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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 3,049 Documents
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MPPT controller for a solar PV array with temperature-compensated P&O algorithm H. Yusuf, Abdulaziz; Tirmikci, Ceyda Aksoy; Eltahir Mohamed, Eltahir Idris
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11686

Abstract

Photovoltaic (PV) systems must operate at the maximum power point (MPP) to maintain efficiency under varying environmental conditions. This study develops a temperature-compensated perturb-and-observe (PO), maximum power point tracking (MPPT) controller integrated with a boost converter in MATLAB. A 100 kW PV array was modeled using realistic module parameters; baseline experiments without MPPT revealed power losses up to 70% and incorrect power–temperature correlations due to fixed-duty cycle operation. With MPPT enabled, the controller dynamically adjusted the duty ratio, achieving proportional scaling with irradiance and an inversely proportional response to temperature. Results confirm a peak summer output of 48.78 kW compared to 15 kW without MPPT, improving annual energy yield by a factor of three under realistic Sakarya operating conditions.
Risk-aware dynamic spectrum allocation using learning-augmented RIS control for cognitive radio networks Dheshmuk, Mallikarjun; Kumbalavati, Santosh B.
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11909

Abstract

The proposed RADIANT-CRN framework introduces a risk-aware dynamic spectrum allocation approach for reconfigurable intelligent surface (RIS)-enabled cognitive radio networks while maintaining reliable protection for primary users (PUs). It incorporates our previously developed bidirectional long short-term memory and adaptive manta-ray foraging optimization (BiLSTM-AMRFO) spectrum prediction and deep channel estimation models into a bilevel optimization framework, where semidefinite relaxation (SDR) adjusts RIS phase shifts, geometric programming (GP) allocates transmit power, and entropy-regularized assignment performs channel selection. A primal-dual actor–critic framework coordinates these modules. On a 120 MHz testbed with 256-element RIS, RADIANT-CRN gets a sum rate of 846±18 Mbps with a 0.7±0.2% PU-violation and 99.3% chance-constraint coverage. This is about 25% higher than a greedy non-risk baseline and about 43% higher than a no-RIS optimizer. It also lowers interference CVaR_0.95 from 9.1 mW to 2.4 mW. These results demonstrate that RADIANT-CRN is the first framework to enforce both chance and CVaR guarantees in RIS-assisted CRNs, achieving high spectral efficiency with statistically certifiable PU protection that aligns with Federal Communications Commission interference requirements. The framework is validated on a prototype SDR testbed (Rician fading, K=6 dB, bursty PU activity); implementation uses Python 3.11/PyTorch 2.x with convex optimization python (CVXPy/MOSEK), and synthetic testbed traces are available upon request.
Smart city solutions: internet of things-enabled ambulances for enhanced post-earthquake resilience in Morocco Sara Tahiri; Mouad Choukhairi; Youssef Fakhri; Mohamed Amnai
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10662

Abstract

Disasters such as earthquakes pose severe threats to sustainable urban development, causing loss of life, infrastructure collapse, and long-term disruption. In the critical first hours after an earthquake, delays in emergency response significantly reduce survival chances for vulnerable populations. This paper proposes an integrated framework that leverages internet of things (IoT)-enabled ambulances, geographic information systems (GIS), and optimization algorithms to enhance post-earthquake emergency response. The framework addresses two key challenges: i) optimal allocation of ambulances under resource constraints and ii) dynamic routing in disrupted road networks. Using real data from the 2023 Al Haouz earthquake in Morocco, the study compares deterministic approaches (Dijkstra, A*) with metaheuristics (particle swarm optimization (PSO), ant colony optimization (ACO), and Tabu Search (TS)). Results show that PSO reduced ambulance requirements by 40% while rescuing more citizens, and ACO achieved the highest route reliability (0.96). These findings demonstrate the practical applicability of IoT-enabled smart ambulances in improving resilience, efficiency, and equity in urban disaster management.
Cybersecurity in property digitisation: a taxonomy and evaluation of current security methods Anuradha Uppar; Nagaveni Veerakyatharayappa; Parvathi Chikkanna
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10945

Abstract

The proliferating methods of digitization for documents related to real estate, land, property, and tax, encountered with critical security challenges. Such documents, which have an inclusion of high-value information, such as transaction history, geographic boundaries, tax assessment, and ownership details, are quite prime targets for various types of cyber threats. It is observed that with security domains advancing in faster pace, their implications towards securing such legal documents are questionable, especially in the presence of emerging lethal threats. This manuscript presents a comprehensive and highly compact exhibit of curated information about the effectiveness of all identified security solutions. The presented study has discussed the taxonomy of six mechanisms of securing property-related digitized document. It was found that although the impersonation attacks and forgery can be addressed with multifactor authentication, digital signatures and blockchain, they are not reportedly found to be mitigating modern-day threats like social engineering, and insider collusion. The outcome of this study infers that hybrid approaches fusing with zero-trust models, anomaly detection, and artificial intelligence (AI) demands more resilient security system. The paper contributes towards some novel findings of widely deployed security solutions, research trends, and gaps which will offer definitive guidelines towards designing a novel solution.
Cross-lingual deep learning model for gender detection Jhajharia, Kavita; Mahajan, Ginika; Samrudh, Dhondi; Patel, Koustubh
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10985

Abstract

Speech recognition is transforming the way humans interact with technology and automatic gender recognition is an essential part of this evolution. This study develops a multilingual deep learning (DL) model for gender detection using three audio datasets: RAVDESS (English), Berlin EmoDB (German), and IITKGP-SEHSC (Hindi). These datasets provide linguistic diversity, enabling the development of a multi-lingual gender identification model. The mel-frequency cepstral coefficients (MFCC) and VGGish embeddings and other audio features were used to process raw audio data into something meaningful. The findings show the machine learning (ML) models (random forest (RF) and extreme gradient boosting) achieved good results in the monolingual (98.26% using Hindi and 96.90% using cross-lingual) setup. In DL models, convolutional neural network (CNN) outperformed other models in both monolingual and cross-lingual scenarios, with 99.33% accuracy for Hindi and 98.11% accuracy in cross-lingual setup. These findings show how well DL works for gender detection in multilingual and emotionally complex settings. It outperforms traditional models. The experiment describes the potential of DL in speech-based artificial intelligence (AI) applications, which enhances the performance in real-life scenarios.
Enhanced face recognition with nuclear norm-based angle 2D-PCA using QR decomposition Elalji, Jamal; Gretete, Driss; Chougdali, Khalid
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11042

Abstract

Several approaches based on two-dimensional principal component analysis (2DPCA) have shown limitations in terms of classification performance. To enhance its robustness, an angular variant of 2DPCA has been proposed, establishing a relationship between reconstruction error and data variance through the Frobenius norm. However, this technique still encounters certain challenges. To overcome these shortcomings and further strengthen resilience to data variations, we propose a novel framework: nuclear norm-based angular 2DPCA using QR-decomposition (AN2DPCA-QR). This new formulation leverages the nuclear norm to optimize a variance-related criterion by maximizing the ratio of projected to original variance, aiming to improve the discriminative capacity of the projection space. The method employs a non-greedy iterative algorithm to solve the optimization problem, incorporating adaptive mean centralization for bias reduction, and QR decomposition instead of singular value decomposition (SVD) for numerical stability and reduced complexity. Compared to its predecessor, AN2DPCA-QR offers enhanced robustness, and interpretability. Results obtained on various public benchmark datasets clearly demonstrate the practical relevance and resilience of the proposed method.
Electrocardiogram signal denoising and heart disease classification Venkata Siva Reddy, K.; Balaji, M
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11276

Abstract

Electrocardiogram (ECG) recordings are often contaminated by baseline wander (BLW), power-line interference, and motion or muscular noise, reducing the reliability of both manual and automated diagnosis. The paper presents a light and reproducible MATLAB pipeline applying finite-impulse-response (FIR) filters designed using Kaiser and Hamming windows for ECG denoising, which after R-peak detection follows an RR-interval analysis for classification of heart rate as tachycardia, bradycardia, or normal. In the experiments, 15 MIT-BIH records with added Gaussian noise at several SNR levels were used for benchmarking the performance of denoising. FIR band-pass and low-pass windowed filters improved the clarity of the waveform and supported robust R-peak detection; RR-interval-based classification reached a mean accuracy of ~98.7% on the study set. The approach is computationally lightweight and quite suitable for embedded real-time deployment but is restricted to the small sample of records and synthetic noise modeling. Future work will compare the efficacy of windowed FIR filtering against modern deep-learning denoisers (CNN/RNN/GAN architectures) and assess the pipeline in larger clinical datasets.
Enhancing wind speed forecasting accuracy: comparative insights into recurrent neural networks for short-term prediction Fennane, Sara; Kacimi, Houda; Mabchour, Hamza; Altalqi, Fatehi; El Hazmir, Aziz; Echchelh, Adil
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11222

Abstract

Short-term wind speed forecasting is essential for maintaining grid stability and supporting the integration of renewable energy, yet the strong variability of wind makes accurate prediction difficult. Sudden fluctuations and nonlinear atmospheric behavior often reduce the performance of conventional artificial intelligent models. To address this challenge, this study evaluates three forecasting methods which include a gated recurrent unit (GRU) model, a temporal convolutional networks (TCNs) model, and a hybrid GRU–TCN design that enables prolonged term forecasting while enabling quick identification of localized weather changes across various meteorological parameters. The researchers used Laayoune, Morocco data to build their model training process. The hybrid method exceeded all other models because it achieved an R² value of 0.99 and a root mean square error (RMSE) of 0.16 m/s and a mean absolute error (MAE) of 0.03 m/s. The system successfully manages sudden shifts in wind patterns while maintaining accurate site-specific physical behavior. The hybrid GRU–TCN design functions as a dependable and expandable system, which delivers real-time wind forecasting capabilities that enable effective smart grid operations and facilitate the growth of wind energy systems.
Optimization of cyber attack detection model using deep learning algorithm based on convolutional neural network Saragih, Hondor; Manurung, Jonson
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10579

Abstract

The increasing intensity and complexity of cyber threats demand more adaptive intrusion detection mechanisms. Conventional approaches are often limited in capturing complex and non-linear attack patterns in network traffic data. This study develops and evaluates a convolutional neural network (CNN)-based model for multi-class cyberattack detection. The proposed architecture integrates convolutional, pooling, and fully connected layers with rectified linear unit (ReLU) and SoftMax activation functions to improve classification performance. The network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset is used for training and evaluation. Experimental results show that the CNN model achieves 96.34% accuracy and an F1-score of 0.99, outperforming several traditional machine learning methods, including Naïve Bayes (NB), decision tree (DT), support vector machine (SVM), and random forest (RF). The superior performance is attributed to the model’s capability to automatically learn and extract meaningful spatial representations from network data without manual feature engineering. These findings demonstrate the effectiveness of deep learning techniques in improving cyberattack detection and contribute to the development of reliable AI-driven network security systems with strong potential for real-world cybersecurity applications and evolving threat mitigation strategies.
Hybrid dual-stream deep learning for breast cancer ultrasound detection Musab Mahmoud Iqtait; Marwan Harb Alqaryouti; Ala Eddin Sadeq; Jafar Ababneh; Suhaila Abuowaida; Nawaf Alshdaifat; Muath Alali
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10102

Abstract

The heterogeneity of breast tissue and subtle morphological variations in ultrasound images make breast cancer detection a challenging task. This study proposes a hybrid deep learning framework that integrates EfficientNetB4 and ConvNeXt within a dual-stream architecture enhanced by advanced attention mechanisms. The model combines multi-scale texture representation with spatial feature extraction to improve classification performance. A two-stage preprocessing pipeline, consisting of adaptive median filtering and bilateral filtering, is applied to reduce speckle noise while preserving important structural details. The proposed method is evaluated on BUSI and UDAIT datasets, achieving 87.82% accuracy, 87.33% precision, and 85.33% recall on BUSI, and 85.69% accuracy, 84.00% precision, and 78.00% recall on UDAIT. These results outperform several baseline models, including ResNet-50, DenseNet-121, and vision transformers. Error analysis shows limitations in detecting small lesions and cross-modal generalization, with reduced performance on mammography images. Attention visualization demonstrates strong agreement with radiologist annotations, supporting model interpretability. The findings highlight the effectiveness of hybrid architectures for ultrasound-based breast cancer detection while emphasizing the need for modality-specific optimization.

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