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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 83 Documents
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Search results for , issue "Vol 15, No 3: June 2025" : 83 Documents clear
A comprehensive survey on automatic image captioning-deep learning techniques, datasets and evaluation parameters Chauhan, Harshil; Thacker, Chintan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3257-3266

Abstract

Automatic image captioning is a pivotal intersection of computer vision and natural language processing, aiming to generate descriptive textual content from visual inputs. This comprehensive survey explores the evolution and state-of-the-art advancements in image caption generation, focusing on deep learning techniques, benchmark datasets, and evaluation parameters. We begin by tracing the progression from early approaches to contemporary deep learning methodologies, emphasizing encoder-decoder based models and transformer-based models. We then systematically review the datasets that have been instrumental in training and benchmarking image captioning models, including MSCOCO, Flickr30k, Flickr8k, and PASCAL 1k, discussing image count, types of scenes, and sources. Furthermore, we delve into the evaluation metrics employed to assess model performance, such as bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ordering (METEOR), recall-oriented understudy for gisting evaluation (ROUGE), and consensus-based image description evaluation (CIDEr), analyzing their domains, bases, and measurement criteria. Through this survey, we aim to provide a detailed understanding of the current landscape, identify challenges, and propose future research directions in automatic image captioning.
Deep learning for predicting drug-related problems in diabetes patients Smadi, Fatima M.; Al-Radaideh, Qasem A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2998-3009

Abstract

Machine learning and deep learning have made advances in the healthcare domain. In this study, we aim to apply deep learning models to predict the drug-related problems (DRPs) status for diabetes patients. Also, to determine the appropriate model to use for classification using deep learning algorithms or machine learning methods to investigate which one performed better results for tabular data by comparing the achieved deep learning results with the machine learning methods to figure out which one gives better results. To apply the deep learning models, the same criteria that were applied in the previous study have been implemented in this investigation, and the same dataset was used. The results show that the machine learning algorithms especially the random forests for predicting the DRPs status outperform the deep learning models. For classification tasks in healthcare for tabular data, the findings of this study show that machine learning methods are the appropriate model instead of using deep learning to apply classification.
Improving breast cancer classification with a novel VGG19-based ensemble learning approach Taib, Chaymae; Ahmadi, Adnan El; Abdoun, Otman; Haimoudi, El Khatir
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2809-2819

Abstract

Breast cancer is one of the most life-threatening diseases, particularly affecting women, highlighting the importance of early detection for improving survival rates. In this study, we propose a novel diagnostic framework that combines a modified VGG19 architecture with Bagging ensemble learning, using three base classifiers: decision tree (DT), logistic regression (LR), and support vector machine (SVM). We also compare this approach with twenty-four hybrid models, integrating various convolutional neural network (CNN) architectures (ResNet50, VGG19, ConvNextBase, DenseNet121, EfficientNetV2B0, EfficientNetB0, MobileNet, and NasNetMobile) with Bagging ensemble methods. Our results show that the proposed model outperforms all other architectures, especially when combined with SVM, achieving accuracy of 97% on the fine needle aspiration cytology (FNAC) dataset and 90% on the International Conference on Image Analysis and Recognition (ICIAR) dataset. This framework demonstrates strong potential for improving early breast cancer diagnosis.
Harnessing speed breakers potentials for electricity generation: a case study of Covenant University Orovwode, Hope Evwieroghene; Abubakar, John Amanesi; Josiah, Olutunde Oluwatimileyin; Abdullkareem, Ademola
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2669-2680

Abstract

The global imperative to transition towards sustainable energy sources has sparked innovative solutions for energy generation and environmental conservation challenges. As fossil fuel usage for power generation continues to raise environmental concerns, converting kinetic energy from vehicular motion via speed breakers presents a unique avenue for renewable power production. This study explores the concept of utilizing speed breakers as a means of electricity generation to power little power-consuming but critical load, with Covenant University serving as a pertinent case study. This research investigates the technical, economic, and environmental implications of implementing speed breaker-based electricity generation within Covenant University. Analyzing the university's energy consumption patterns showed that some loads do not require much power but are critical. Street lighting is one of such loads. This study discerns the potential contribution of speed breaker-generated electricity to address energy demands by simulation and constructing a prototype. Advanced engineering tools, such as simulation software Fusion 360 and Proteus 8.0, were employed to model and integrate the roller speed breaker mechanism with the electrical infrastructure. The findings offer valuable insights into the viability of speed breaker-generated electricity as an alternative energy source, paving the way for sustainable energy practices in educational institutions and beyond.
Investigating power quality issues in electric buggy battery charger systems: analysis and mitigation strategies Bunyamin, Wan Muhamad Hakimi Wan; Muhamad, Samshul Munir; Saidon, Wan Salha; Baharom, Rahimi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2534-2544

Abstract

This paper investigates power quality issues in the battery charger system of an electric buggy. Key power quality parameters such as total harmonic distortion (THD), power factor (PF), input voltage, and input current, were measured and analyzed during the charging process. The findings reveal significant power quality challenges, with THD levels exceeding IEEE 519 standards, indicating inefficiencies and potential risks such as increased heating and stress on charger components. Power factor readings reveal a substantial reactive power component, further contributing to inefficiency. To address these issues, the study recommends implementing harmonic mitigation techniques, such as passive and active filters, to reduce THD levels, using power factor correction methods, and optimizing charging algorithms to manage power demand more effectively. Continuous monitoring of charging parameters is essential for maintaining optimal performance and reliability. Adhering to standards is crucial for the efficient and reliable operation of electric vehicle (EV) charging systems, with regular compliance testing and benchmarking necessary to identify improvement areas and maintain a high-quality charging infrastructure. The proposed solutions aim to develop a sustainable and efficient charging system for electric buggies, providing valuable insights and recommendations for future research and development in power electronics and drive systems for EV applications.
Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies Mutovina, Natalya; Nurtay, Margulan; Kalinin, Alexey; Tomilov, Aleksandr; Tomilova, Nadezhda
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3291-3308

Abstract

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.
Enhancing malware detection with genetic algorithms and generative adversarial networks Eddine, Abid Dhiya; Abdelkader, Ghazli; Mourad, Bouache
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3064-3074

Abstract

Malware detection is a critical task in cybersecurity, necessitating the creation of robust and accurate detection models. Our proposal employs a holistic methodology for identifying and mitigating malware using deep learning techniques. Initially, a customized genetic algorithm is employed for feature selection, reducing dimensionality and enhancing the discriminatory power of the dataset. Subsequently, a deep neural network is trained on the selected features, achieving high accuracy and robust performance in distinguishing between malware and benign data. Generative adversarial networks are also utilized to evaluate model effectiveness on unseen data and ensure the model's robustness and generalization capabilities. Evaluation of the proposed model demonstrates accurate malware detection with high generalization capabilities. Furthermore, future research should focus on developing and deploying practical tools or systems that implement the proposed model for real-time malware detection in operational environments. This research makes a significant contribution to the field of malware detection and provides excellent opportunities for practical implementation in the field of cybersecurity.
A hybrid convolutional neural network-recurrent neural network approach for breast cancer detection through Mask R-CNN and ARI-TFMOA optimization Sreekala, Keshetti; Yalamati, Srilatha; Lakshmanarao, Annemneedi; Kumari, Gubbala; Kumari, Tanapaneni Muni; Desanamukula, Venkata Subbaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3084-3094

Abstract

This paper presents a novel hybrid deep learning-based approach for breast cancer detection, addressing critical challenges such as overfitting and performance degradation in varying data conditions. Unlike traditional methods that struggle with detection accuracy, this work integrates a unique combination of advanced segmentation and classification techniques. The segmentation phase leverages Mask region-based convolutional neural network (R-CNN), enhanced by the adaptive random increment-based tomtit flock metaheuristic optimization algorithm (ARI-TFMOA), a novel algorithm inspired by natural flocking behavior. ARI-TFMOA fine-tunes Mask R-CNN parameters, achieving improved feature extraction and segmentation precision while ensuring adaptability to diverse datasets. For classification, a hybrid convolutional neural network-recurrent neural network (CNN-RNN) model is introduced, combining spatial feature extraction by CNNs with temporal pattern recognition by RNNs, resulting in a more nuanced and comprehensive analysis of breast cancer images. The proposed framework achieved significant advancements over existing methods, demonstrating improved performance. This hybrid integration of ARI-TFMOA and Hybrid CNN-RNN models represents a unique contribution, enabling robust, accurate, and efficient breast cancer detection.
Genetic algorithm-adapted activation function optimization of deep learning framework for breast mass cancer classification in mammogram images Razali, Noor Fadzilah; Isa, Iza Sazanita; Sulaiman, Siti Noraini; Osman, Muhammad Khusairi; Karim, Noor Khairiah A.; Damit, Dayang Suhaida Awang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2820-2833

Abstract

The convolutional neural network (CNN) has been explored for mammogram cancer classification to aid radiologists. CNNs require multiple convolution and non-linearity repetitions to learn data sparsity, but deeper networks often face the vanishing gradient effect, which hinders effective learning. The rectified linear unit (ReLU) activation function activates neurons only when the output exceeds zero, limiting activation and potentially lowering performance. This study proposes an adaptive ReLU based on a genetic algorithm (GA) to determine the optimal threshold for neuron activation, thus improving the restrictive nature of the original ReLU. We compared performances on the INbreast and IPPT-mammo mammogram datasets using ReLU and leakyReLU activation functions. Results show accuracy improvements from 95.0% to 97.01% for INbreast and 84.9% to 87.4% for IPPT-mammo with ReLU and from 93.03% to 99.0% for INbreast and 84.03% to 91.06% for IPPT-mammo with leakyReLU. Significant accuracy improvements were observed for breast cancer classification in mammograms, demonstrating its potential to aid radiologists with more robust and reliable diagnostic tools.
Monkey detection using deep learning for monkey-repellent Azyze, Nur Latif Azyze Mohd Shaari; Quan, Teow Khimi; Isa, Ida Syafiza Md; Husman, Muhammad Afif
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3238-3245

Abstract

Animal intrusion has caused many issues for human beings, especially monkeys. Monkeys have caused many problems such as yield crop damage, damage to human facilities and assets and stealing food. This study aims to investigate the use of deep learning to detect monkey presence accurately and use a proper repellent system to scare them away. A deep learning algorithm is constructed with supervised learning, which includes the monkey dataset with appropriate labelling of the object of interest. The detection of the monkey comes with a bounding box with respective confidence of detection. The result is then used to evaluate the accuracy of monkey detection. The accuracy of the trained model is assessed by evaluating its performance under varying conditions of camera quality and distances. The study focuses on proving the reliability of deep learning to detect monkeys and automatically perform corresponding actions like repelling monkeys. Hence this may reduce the reliance of manpower to secure a large space and improve safety issues.

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