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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 3,126 Documents
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Artificial intelligence and machine learning in healthcare: a comprehensive review Rosepreet Kaur Bhogal; Ajmer Singh
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9815

Abstract

Artificial intelligence (AI) and machine learning (ML) are reshaping healthcare by supporting faster diagnosis, predictive modeling, and efficient clinical workflows. This review examines 52 recent studies to assess how these technologies are applied across diagnostics, predictive analytics, patient monitoring, operations, treatment, and ethical considerations. Results show substantial progress in imaging, genomics, drug discovery, and hospital management, where systems often match or surpass human performance. At the same time, challenges such as limited generalizability, data bias, privacy concerns, and lack of interpretability remain significant barriers to adoption. This review identifies common strengths and gaps by grouping existing work into six themes, offering a structured view of current developments. The findings suggest that the future of AI in medical care lies in transparent, fair, and clinically validated systems that can scale across diverse populations and settings.
A systematic study on advanced intelligent techniques in automated guided vehicles control for Industry 5.0 perspective Prema C. Thalaivar; Madhusudhan K. N.; Bhagya R.
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10753

Abstract

Automated guided vehicles (AGVs) play a crucial role in streamlining operations within manufacturing plants, warehouses, and distribution centers. As the industrial landscape transitions from Industry 4.0 to Industry 5.0, there is an increasing demand for more advanced, intelligent control systems to support the evolving complexity of these environments. This paper presents a systematic study of the advanced intelligent techniques driving the autonomous behavior of AGVs, with a focus on their application in Industry 5.0. The review categorizes intelligent techniques—such as machine learning, soft computing, game theory, and other intelligent algorithms—used for enhancing AGV functionalities including path planning, task scheduling, and energy-efficient operation. Emphasis is placed on how these approaches enable adaptability and smarter decision-making in dynamic industrial settings. The study concludes with key insights and outlines future research directions to further enhance AGV performance in the context of Industry 5.0.
Data-driven modelling of Aquilaria essential oils via dual GC profiling and multicollinearity diagnostics Nur Athirah Syafiqah Noramli; Noor Aida Syakira Ahmad Sabri; Muhammad Ikhsan Roslan; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10870

Abstract

Aquilaria-derived essential oils are chemically diverse and hold significant value in pharmaceuticals, fragrances, and traditional medicine. However, the complexity of their chemical composition presents challenges in statistical modelling, particularly due to multicollinearity among biosynthetically related compounds. This study investigates the extent of multicollinearity in Aquilaria essential oil data using multiple linear regression (MLR) and variance inflation factor (VIF) analysis. A regression model was constructed using three compounds, ? -guaiene, 10-epi ? -eudesmol, and ? -eudesmol, across 360 samples, with VIF and collinearity diagnostics applied to assess model validity. The model explained 93% of the variance in species classification, which is substantially higher than values typically reported in earlier chemometric studies of Aquilaria oils. This demonstrates that even a limited number of carefully selected compounds, when supported by diagnostic safeguards, can achieve strong classification accuracy. These findings emphasize the importance of applying multicollinearity diagnostics to improve the interpretability and reliability of chemometric analyses. The study contributes a robust analytical framework for future research and practical applications in species authentication, essential oil quality control, and conservation of Aquilaria resources.
PID controller tuning performance evaluation based on integral time square error for coupled tank system Nur Asmiza Selamat; Atikah Razi; Syahar Azalia Ab Shukor; Nur Hazahsha Shamsudin; Fardila M. Zaihideee
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9334

Abstract

This study presents a conclusive performance evaluation based on integral time between the conventional and optimization based proportional integral derivatives (PID) tuning method. The tuning method of interest are including trial and error, auto-tuning, Ziegler-Nichols (ZN), Cohen-Coon (CC), and particle swarm optimization (PSO). The coupled tank system (CTS) is used for the system under consideration as it’s one of the popular technologies in industrial control application. Previous study had compared the tuning performance in terms of its transient response. The transient consists of several parameters such as rise time (Tr), settling time (Ts), peak time (Tp), steady state error, overshoot, and steady state error. Due to that, a conclusive performance comparison could not be achieved. Hence this study proposed integral time squared error evolution which is based on only one parameter which also reflects good overall transient response performance. The results show that of all tuning methods, PSO provide the smallest integral time square error (ITSE) value, while trial and error provide the highest with a value of 12.84 and 203.10, respectively. The ITSE also reflects the transient response performances.
A machine learning framework for dynamic and balanced computing resource allocation in 5G networks Shaik Abdul Hameed; Indurthi Ravindra Kumar; Chavali Amaresh; Kanchana Rajendran; Zarapala Sunitha Bai; Maganti Syamala
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11138

Abstract

The swift expansion of fifth-generation (5G) networks has heightened the difficulty of distributing computing and transmission resources amidst the demands for extensive connectivity, ultra-low latency, and high throughput. This paper presents an innovative hybrid framework that combines deep learning (DL) with bird swarm optimization (BSO) to achieve dynamic and balanced resource allocation in mobile edge–cloud environments. A DL model based on long short-term memory (LSTM) forecasts user demand and channel conditions, while BSO enhances offloading and power distribution to reduce latency, energy usage, and expenses. In a setup utilizing non-orthogonal multiple access (NOMA) and mobile edge computing (MEC), the proposed DL–BSO approach demonstrates an impressive improvement of up to 54% compared to heuristic methods in simulations that reflect realistic traffic and channel conditions. The framework demonstrates a strong ability to adjust to different loads, rendering it ideal for applications that require low latency, including autonomous driving and augmented reality. The constraints involve dependence on precise forecasts and scalability issues in extensive implementations, which will be tackled in forthcoming research focused on 6G advancements.
Artificial intelligence-based solar radiation forecasting for energy optimization in sustainable buildings Imad Laabab; Said Ziani; Abdellah Benami
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11103

Abstract

The work presented in this article aligns with our university's commitment to advancing renewable energy sources. We can better plan and optimize energy use if we are aware of the factors that affect solar energy generation. This study examines the application of artificial neural networks (ANNs) in forecasting global horizontal irradiance (GHI) within the context of sustainable energy. The primary objective is to enhance the accuracy and reliability of solar irradiance forecasts, thereby improving the performance of renewable energy systems, such as concentrated solar power (CSP). This article provides an overview of solar radiation, the physical factors that influence its distribution, and the impact of panel tilt angle on energy production. It presents a case study in Morocco, which uses a hybrid approach to predict solar radiation. The results demonstrate that ANN, employing advanced machine learning (ML) methods, provides more accurate and reliable forecasts than traditional models. This advance could improve energy planning, reduce uncertainty, and enable better management of solar energy production and storage systems. Our results suggest that this approach has increased forecast accuracy.
Design and fabrication of a novel millimeter-wave bandstop filter using transversal signal interference technique Salaheddine Barou; Jamal Zbitou; Stéphane Ginestar; Mohammed El Gibari
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10777

Abstract

This paper presents the design, simulation, and fabrication of a miniaturized millimeter-wave band-stop filter (BSF) on microstrip technology. The proposed arrangement is based on the concept of transversal interference of signals, and is realized as a pair of parallel transmission lines with the appropriate characteristic impedance and electrical length. Such scheme provides sharpening signal discrimination without resonator structures additional to the filter. The filter is fabricated on a ROGERS RT/Duroid 5880 substrate, selected for its favorable properties at high frequencies, including a thickness of 0.13 mm, a relative dielectric constant of 2.2, and a loss tangent of 0.0009. The optimized design was validated through electromagnetic simulations by two types of electromagnetic solvers, and then fabricated and measured by coplanar waveguide (CPW) probe to confirm its practical performance.
Advancements in UAV-based traffic monitoring: a systematic review of deep learning and edge computing Mohamed S. Sawah; Mohammed Tawfik; Issa Alsmadi
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9596

Abstract

Rapid urbanization necessitates innovative traffic monitoring solutions. Traditional methods (fixed sensors/CCTV) face limitations in coverage, adaptability, and real-time processing. This review examines advancements (2015–2024) in vision-based unmanned aerial vehicle (UAV) traffic monitoring systems, evaluating their effectiveness in vehicle detection, traffic analysis, and congestion management. A systematic preferred reporting items for systematic reviews and meta-analyses (PRISMA)-guided analysis of 2,895 articles from IEEE Xplore, Scopus, Web of Science, and ACM Digital Library identified 49 eligible studies. Quantitative performance metrics (detection accuracy and latency) were standardized for cross-study comparison. Modern systems achieve 94% detection accuracy and 40 ms latency through edge computing and deep learning (e.g., you look only once (YOLO) and Faster region-based convolutional neural network (Faster R-CNN)). Multi-sensor fusion improves robustness by 35% in challenging conditions. However, battery life (reduced by 40% under processing load) and regulatory barriers remain critical constraints. Artificial intelligence (AI)-driven UAV systems enable real-time, high-accuracy traffic monitoring but require solutions for power efficiency and scalability. Future integration of 5G/6G and swarm intelligence holds promise for next-generation smart traffic management.
Improving genomic classification via Pearson-based SNP selection: a comparison of k-NN, SVM, and random forest Prihanto Ngesti Basuki; Sri Yulianto Joko Prasetyo; Adi Setiawan
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9087

Abstract

Accurate genomic classification is vital for precision health and population studies, yet high-dimensional single-nucleotide polymorphism (SNP) data (pn) amplify noise, redundancy, and overfitting. This study evaluates a simple, model-independent Pearson-based selection that ranks SNPs by feature–label correlation, and assesses k-nearest neighbors (k-NN), linear support vector machine (SVM), and random forest (RF) under leakage-free stratified Monte Carlo cross-validation (MCCV). Performance increases monotonically with |r|: the strongest tiers reach ?99–100% accuracy; SVM leads in mid tiers (RF second), while k-NN is competitive mainly at the extremes. A matched-dimensionality PCA-120 baseline (TRAIN-only) attains parity for SVM/RF and trails slightly for k-NN at the 10% test size. With 120-SNP panels, prediction medians are ?0.30 ms (SVM), 1.81–1.83 ms (k-NN), and 34–35 ms (RF), supporting CPU-only deployment. A consensus panel combining correlation evidence with principal component analysis (PCA) selection frequency yields interpretable Top-20/Top-120 subsets and |r|-based operating thresholds. Overall, Pearson-based selection provides a transparent, reproducible baseline for small-sample SNP classification, offering accuracy competitive with PCA at lower computational complexity and straightforward extensions to broader cohorts and multi-omics integration.
PULMO-NET: blockchain-integrated lung cancer classification using Golden Jackal Optimization and GoogleNet Angel Mary Azhakesan; Thanammal Kakkumperumal Krishnammal
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10747

Abstract

Lung cancer (LC) is a malignant disease caused by uncontrolled cell growth in the lungs, often associated with smoking and environmental factors. However, accurate LC classification is particularly challenging due to poor image quality, variability in imaging conditions, and noise artifacts in medical scans. In this work, a novel PULMO-NET is proposed for classifying LC using dual-modality imaging (CXR and CT). The dual-modality images are preprocessed using an adaptive trilateral (ADT) filter and segmented using the Golden Jackal Optimization (GJO). The segmented lung regions are refined using the dragonfly algorithm (DA) which enables accurate extraction of diamond-shaped tumor patterns. Additionally, a blockchain-based system with local nodes is integrated to collect real-time patient data. GoogleNet uses inception modules to capture multi-scale features, enabling accurate classification of lung images into normal, non-small cell lung cancer (NSCLC), and small cell lung cancers (SCLC). The proposed PULMO-NET achieves the classification accuracy (AC) of 98.91% and F1 score of 96.51%. The PULMO-NET model improves the overall AC by 1.91%, 7.78%, and 4.33% better than Inception-v3, TPOT_SVM, and LeNet–DenseNet respectively.

Page 306 of 313 | Total Record : 3126

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