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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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Improved feature-based hybrid deep learning for multiclassification of ultrasound thyroid nodules Mayuresh Gulame; Deepthi D. Kulkarni; Priya Khune; Nilesh N. Thorat; Ashwini G. Shahapurkar; Vijaya S. Patil; Sumit Arun Hirve; Aarti Pimpalkar
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.v15i1.10447

Abstract

Ultrasonography is frequently used to identify thyroid nodules. Because of their internal features, variable appearances, and ill-defined borders, it might be difficult for a hospitalist to distinguish amongst benign and malignant forms of the nodule based solely on visual inspection. Although deep learning, a subset of artificial intelligence, has significantly advanced medical image recognition, challenges remain in achieving accurate and efficient diagnosis of thyroid nodules. To identify and classify thyroid nodules, this study uses an innovative hybrid DL-assisted multi-classification technique. A median blur eliminates salt-and-pepper noise, and this is followed by segmentation using a method based on enhanced pooling integrated U-Net (EPIU-Net). To produce a single histogram series, features are recovered from the segmented image, including multi-texton, and local ternary pattern (LTP) based patterns. Following feature extraction, the data is expanded and input into a fusion classification model utilizing Deep Maxout and convolutional neural network (CNN) to categorize nodules. This work uses 2 types of datasets and for both datasets, we achieved great results with our hybrid technique across all performance criteria. 0.976, 0.008, 0.992, and 0.017 are the corresponding values for accuracy, false discovery rate (FDR), sensitivity, false negative rate (FNR). Moreover proposed work is verified by k-fold method.
Gift recommendation with multilabel clustering Violitta Yesmaya; Rini Wongso
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.v15i1.10161

Abstract

In the rapidly evolving e-commerce landscape, personalized gift recommendation systems play a crucial role in enhancing customer satisfaction and driving sales. This study introduces a gift recommendation system using a multilabel clustering approach with the using four algorithms, aiming to provide personalized and accurate product suggestions. The proposed system compares the performance of four algorithms: K-nearest neighbors (K-NN), decision trees, random forest, and eXtreme gradient boosting (XGBoost). Through extensive model training and hyperparameter tuning, XGBoost demonstrated superior performance with a label ranking average precision score of 95% and minimal overfitting, outperforming other algorithms in accuracy and runtime. The results highlight the effectiveness of XGBoost in managing complex data and delivering precise recommendations, making it a valuable tool for improving user experience and increasing revenue in e-commerce platforms.
Fault-tolerant trajectory tracking of mobile robots via model-free intelligent PID and fault observer Sy Phuong Ho; Manh Tien Ngo; Duy Tan Ngo; Dinh Tu Duong; Van Du Phan; Kim Duyen Ha Thi; Thanh Tri Nguyen; Minh Dang Dau; Thu Uyen Le Thi
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.v15i1.9884

Abstract

This paper addresses the problem of unexpected errors, such as model uncertainties and actuator faults, that degrade the performance of wheeled mobile robots (WMR). To overcome these challenges, a fault-tolerant control (FTC) approach is developed in which model-free control (MFC) is merged with an intelligent-proportional integral derivative (i-PID) controller and complemented by a fault observer (FO). Unlike existing approaches, the proposed controller does not rely on accurate system modeling; instead, MFC ensures robustness to time-varying parameters, and while i-PID enhances trajectory tracking through adaptive gain adjustment. The FO estimates actuator faults in real time and compensates for their effects, ensuring reliable operation even under severe conditions. The closed-loop stability is rigorously analyzed via Lyapunov theory. MATLAB/Simulink results show reduced tracking errors, improved stability, and strong robustness under both model uncertainties and actuator faults, including time-varying mass and inertia, validating the effectiveness and practical potential of the proposed FTC.
An integrated hybrid U-Net and EfficientNetV2-S approach for brain tumor segmentation and classification Marwa Abbas; Ashraf A. M. Khalaf; Hussein Mogahed; Aziza I. Hussein; Lamya Gaber; Mohamed Mourad Mabrook
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.v15i1.9818

Abstract

Brain tumors involve the uncontrolled proliferation of cells either inside or adjacent to brain tissue, which frequently results in poor patient outcomes. Accurate diagnosis and early identification are vital for effective treatment planning. In this study, we develop a sequential deep learning pipeline for automatic brain tumor segmentation and classification using the publicly accessible Figshare dataset, comprising 3,064 images from 233 patients. We present a double hybrid encoder model based on the U-Net architecture, which combines complementary feature extractors to improve segmentation performance. The model achieves a loss of 0.047, an intersection over union (IoU) of 86.89%, and a Dice score of 95.27%, surpassing the performance of conventional U-Net and U-Net++ architectures. For classification, we utilize a modified EfficientNetV2-S, which is lightweight and achieves a 99% F1-score and 99% accuracy, while being less computationally intensive and faster to train than deeper frameworks such as ResNet50V2. Model performance was evaluated using cross-validation, which included fault detection to improve reliability. We propose that our framework can reliably and efficiently analyze brain tumors and serve as an important component in clinical decision-making in neuro-oncology.
Optimizing feature selection in multilayer ensemble models for improved HAR accuracy Dhiraj Prasad Jaiswal; Ashok Kumar Shrivastava
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.v15i1.10769

Abstract

Human activity recognition (HAR), is an increasingly significant research area as it can be used in many fields of application such as; health care, elderly monitoring, sports training, and smart homes. In this research we developed a novel multi-layer ensemble model based on a combination of a genetic algorithm (GA) to optimize feature selection and hierarchical learning to solve the issues of high dimensional data, feature redundancy and over fitting in HAR. Our model systematically reduces the number of features required to recognize activities while maintaining the most important features; thus, allowing the base learner to learn patterns across multiple layers. We demonstrated through experiments using three standard benchmark datasets-UCI HAR, WISDM, and PAMAP2, that our method significantly outperformed standard methods achieving 96.8% accuracy, and reduced the amount of feature sets by more than 70%. Evaluation metrics including; precision, recall, F1-score, and ROC-AUC, further validated the robustness of our model; while statistical tests confirmed the improvement in performance. Additionally, our framework improved the efficiency and interpretability of our model, which will enable it to be practically implemented in real time environments. These results demonstrate the potential of combining feature selection optimized by a GA and hierarchical ensembles in HAR, and provide avenues for future work in cross domain adaptability and multimodal HAR systems.
Deep learning architectures for medical image segmentation: an organized analysis of CNN-based models and uses Cherifa Abdellaoui; Samia Belkacem; Noureddine Messaoudi
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.v15i1.10839

Abstract

Numerous techniques, especially those based on deep learning (DL), have been developed and applied to a wide range of tasks, including image recognition, classification, object detection, and image segmentation, as a result of extensive research in the field of image processing. Image processing has become crucial in the medical field, with segmentation emerging as a crucial method for organ identification, disease detection, and abnormality analysis in medical images. Convolutional neural networks (CNNs), one of the many approaches, have recently demonstrated great promise in resolving intricate problems associated with medical image analysis because of their capacity to automatically learn hierarchical features. In this review, we discuss recent developments in deep CNNs for medical image segmentation. The architectures and features of the most popular CNN-based models are examined, along with the different publicly accessible medical imaging datasets that are used in studies and the evaluation metrics that are frequently used to gauge segmentation performance and accuracy, also the advantages and disadvantages of each one. In addition, we look at comparative research and the shortcomings of existing approaches, offering suggestions for future lines of clinical relevance.
Gated recurrent unit model for assessment of food quality based on E-nose sensors supported with one-way analysis of variance Mohammad A. Alsharaiah; Yousef K. Sanjalawe; Sharif Naser Makhadmeh; Rizik M. Al-Sayyed; Bashar Awad Al-Shboul
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.v15i1.9835

Abstract

Ensuring the quality of global food supplies has emerged as a significant challenge in recent times. Overseeing perishable items' excellence, freshness, and longevity poses considerable intricacy. A special kind of system established on electronic scent detection systems has been engaged for quality assessment. Recent advancements have concentrated on integrating electronic scent detection systems with machine learning (ML) and deep learning (DL), which comprise encouraging remedies to meet these hurdles. Mainly, this investigation aims to present a pioneering strategy for addressing this issue by binding DL with electronic olfaction technology. Gated recurrent units (GRU) were used for classification actions. The research entails examining from the literature a benchmark dataset acquired from electronic noses (E-noses) across beef cuts. These cuts are allocated into four classes: i) outstanding, ii) satisfactory, iii) passable, and iv) spoiled, depending on their quality. The proposed model, exploiting a GRU for classification tasks, was developed with active dataset attributes identified over the analysis of variance (ANOVA) feature selection method. As a consequence, three key features were selected and employed for the classification process, such as MQ5, MQ137, and total volatile content (TVC). Experimental outcomes demonstrate an impressive classification accuracy of 99.77%, accomplished by the proposed model across further literature models.
Robust Arabic tweet NER via label-aware data augmentation and AraBERTv2 Brahim Ghazoui; Ismail El Bazi; Ibtissam Essadik; Brahim Ait Benali; Hicham Moussa
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.v15i1.10462

Abstract

Named entity recognition (NER) is vital for turning unstructured social media text into structured information. However, Arabic tweets pose distinct challenges; informality, brevity, dialectal variation, and inconsistent orthography. This study targets those challenges by coupling targeted data augmentation with a transformer model, bert-base-arabertv2. We design a lightweight augmentation pipeline—synonym replacement, name and location replacement, and deletion of third-person Arabic names—to expand linguistic variety and reduce overfitting under limited annotation. The approach is simple, but deliberate: preserve labels when substituting entities with type-consistent alternatives; remove corresponding tags when deleting names; and keep tweet semantics intact where possible. We then fine-tune bert-base-arabertv2 on the combined original and augmented data and evaluate on a held-out set of tweets. The result is a substantial gain in overall performance: F1=0.93 with augmentation versus 0.72 without. These findings indicate that controlled, label-aware augmentation can improve robustness and generalization for Arabic tweet NER, where data scarcity and linguistic variability otherwise degrade accuracy. Beyond empirical gains, our work offers a practical recipe—clear augmentation heuristics and a standard transformer backbone—that can be replicated and adapted to similar low-resource, noisy domains. This contributes to more reliable Arabic social media analysis and downstream information extraction.
Optimized control approach for bidirectional wireless power transfer systems with vehicle-to-grid integration Mareedu Hari Venkatesh; Malligunta Kiran Kumar; Chandra Babu Guttikonda; Thalanki Venkata Sai Kalyani; Kambhampati Venkata Govardhan Rao
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.v15i1.10740

Abstract

The transition to electric vehicles (EVs) has intensified the need for efficient vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. Bidirectional wireless power transfer (BWPT) presents a seamless and intelligent approach to energy exchange, particularly under dynamic tariff and grid demand conditions. This study aims to model and simulate a Python-based rule-driven BWPT system to evaluate energy efficiency and economic performance in V2G/G2V applications. A synthetic dataset representing grid demand and time-of-use (TOU) pricing over seven days was used to simulate real-world operating conditions. The model incorporates state-of-charge (SoC) dynamics, bidirectional power control logic, and profit calculation using a 15-minute resolution over 672 time steps. The simulation achieved a total energy exchange of 122.8 kWh and a cumulative net profit of ?536.67, with daily profits averaging ?76.6. SoC levels were effectively maintained between 20% and 90%, and power flows adapted accurately to tariff variations. The study confirms the feasibility of a lightweight, reproducible BWPT model capable of delivering optimized energy management and economic returns. The simulation approach offers strong potential for academic, research, and pre-deployment evaluation of intelligent charging systems.
Automated detection of rice plant diseases using dual stage thresholding and twin support vector machine Snehaprava Acharya; Prasant Kumar Patra; Umesh Chandra Samal; Prabodh Kumar Sahoo; Ankur Goyal; Prince Jain
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.v15i1.9470

Abstract

Rice plants are susceptible to various diseases such as brown spot, BLB, and blast, caused by viral, bacterial, or fungal infections, which significantly affect both the quantity and quality of rice production. This study introduces an automated method for detecting these diseases using dual thresholding (DT) in segmentation combined with twin support vector machine (TW-SVM) classification. Early detection and accurate identification of rice leaf diseases are crucial for effective management and optimization of production. The proposed method leverages the strengths of TW-SVM, including its ability to handle high-dimensional data efficiently. The approach is compared with three SVM-based techniques: basic SVM, least-square SVM, and proximal SVM. Simulations are performed using images from both a public dataset and a real-time drone image dataset. Thirteen features, including color, texture, and shape, are extracted for classification. Results show that the proposed dual stage thresholding (DST) TW-SVM achieves superior performance in terms of time complexity and accuracy, with 95% accuracy on the public dataset and 99.3% accuracy on the drone image dataset.

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