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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
Energy consumption prediction methods in a cyber-physical system Nurgaliyev, Kenzhegali; Tokhmetov, Akylbek
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.pp3054-3063

Abstract

In recent decades, cyber-physical systems (CPS) have become an essential part of modern industry and daily life. These systems integrate physical processes with computer and network components, allowing them to interact with their environment and manage their components autonomously. One of the most significant aspects of CPS efficiency is managing energy consumption, which significantly affects their reliability, efficiency, and economic performance. CPS devices generate vast amounts of diverse data, which is crucial to accurately model. Researchers use predictive analysis to develop models that forecast trends and simulate real-world conditions, enabling them to make better-informed decisions. This article presents a comparative analysis of different predictive models for CPS data analytics, focusing on energy consumption in smart buildings. Short-term models include gradient-boosted regressor (XGBoost), random forest (RF) and long short-term memory (LSTM). The comparative results have been studied in terms of prediction errors to determine accuracy.
EvalBERT: a novel framework for assisted descriptive answers and C programming evaluation Thippeswamy, Prakruthi Sondekere; Eraiah, Manjunathswamy Byranahalli; Jabeen, Salma
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.pp3346-3361

Abstract

Manual assessment of descriptive answers is often time-consuming, error-prone, and subject to bias. While artificial intelligence (AI) has made significant strides, current automated evaluation methods typically rely on simplistic metrics like word counts or predefined terms, which lack a deeper understanding of the content and are highly dependent on curated datasets. As demand for automated grading systems increases, there is a growing need to evaluate not only descriptive answers but also code-based responses. This study addresses these challenges by applying natural language processing (NLP) and deep learning (DL) techniques, testing three baseline models: multinomial Naïve bayes (MNB), bidirectional long short-term memory (Bi-LSTM), and bidirectional encoder representations from transformers (BERT). We propose EvalBERT, a BERT-based model fine-tuned with domain-specific academic corpora using computer processing unit (CPU) acceleration. EvalBERT automates grading for both descriptive and C programming exams, offering features like readability statistics and error detection. Experimental results show that EvalBERT achieves 94.86% accuracy, outperforming other models by 1.22 percentage points, with training time reduced by half. Additionally, EvalBERT is the first model pre-trained with academic corpora for this purpose. An interactive user interface, E-Pariksha, was also developed for administering and taking exams online. EvalBERT provides precise assessments, enabling educators to better evaluate student performance and offer more detailed feedback.
SIGAN: a generative adversarial network architecture for sketch to photo synthesis Latha, Buddannagari; Velmurugan, Athiyoor Kannan
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.pp3118-3126

Abstract

Of late, with the rise of artificial intelligence (AI) and deep learning (DL) models, image translation has become a very important phenomena which could produce realistic photographic results. Synthesizing new images is widely used in different applications including the ones used by investigation agencies. Image generation from hand-drawn sketch to realistic photos and vice versa is required in different computer vision applications. Generative adversarial network (GAN) architecture is extensively employed for generating images. However, there is need for investigating further on improvising GAN architecture and the underlying loss functions towards leveraging performance. In this paper, we put forth a GAN architecture known as sketch-image GAN (SIGAN) for synthesizing realistic photos from hand-drawn sketches. Both generator (G) and discriminator (D) components are designed based on DL models following a non-cooperative game theory towards improving image generation performance. SIGAN exploits improvised image representation and learning of data distribution. The algorithm we have proposed is known as learning-based sketch-image generation (LbSIG). This algorithm exploits SIGAN architecture for efficiently generating realistic photo from given hand-drawn sketch. SIGAN is assessed using a benchmark dataset called CUHK face sketch database (CUFS). From the empirical study, it is observed that the proposed SIGAN architecture with underlying deep learning models could outperform existing GAN models in terms of Fréchet inception distance (FID) with 38.2346%.
A novel hybrid generation technique of facial expressions using fine-tuning and auxiliary condition generative adversarial network Elbadwey, Mai; Attar, Hani; Amer, Ayman; Ibrahim, Khaled H.; Kayed, Somaya; Barakat, Tamer
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.pp3418-3428

Abstract

The facial expression generation continues to be interesting for researchers and scientists worldwide. Facial expressions are an excellent way of transmitting one's emotions or intentions to others, and they have been extensively studied in areas such as driver safety, human-computer interaction (HCI), deception detection, health care, and monitoring. The facial expression generation starts with a single neutral image and generates sequences of facial expression images, which are combined to create a video. Previous methods generated facial expression images of the same person. However, they still suffer from low accuracy and image quality. This article overcomes this problem using a novel hybrid model for facial expression video generation using fine-tuning and condition-generative model architectures to optimize the model's parameters. Results indicate that the proposed novel approach significantly improves the expression generation of the same person. The proposed method can reliably and accurately generate facial expressions, with a testing accuracy of 98.7% and a training accuracy of 99.9%.
Improved YOLOv10 model for detecting surface defects on solar photovoltaic panels Nguyen, Phat T.; Ho, Loc D.; Huynh, Duy C.
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.pp3319-3331

Abstract

Surface defects greatly affect the performance and service life of photovoltaic (PV) modules. Detecting these defects is important to improve the management, repair and maintenance of PV panels. With the development of artificial intelligence, computer vision brings higher accuracy and lower labor costs than traditional inspection methods. This paper introduces an improved PV you only look once v10 (YOLOv10) model for detecting surface defects of PV modules. The improvement includes adding an exponential moving average (EMA) attention mechanism to the neck, using a cycle generative adversarial network (GAN) to enhance the data, and replacing the YOLOv10 head with a YOLOv9 head to retain non-maximum suppression (NMS). Experiments show that the proposed model outperforms state-of-the-art methods such as YOLOv10s, n, x, b, l, and e, achieving superior detection accuracy. Despite the increased computational cost, the proposed method improved mAP@0.5 and mAP@0.5:0.95 by 5.1% and 6.5% over the original YOLOv10s.
Deep learning for the identification of autism traits in children through facial expressions: a systematic review Palomino, Daniella Romani; Ovalle, Christian; Cordova-Berona, Heli
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.pp3279-3290

Abstract

This study employs a bibliometric analysis to examine research on the application of artificial intelligence, specifically deep learning, in the detection of autism traits through facial expressions. Using quantitative methodologies. The analysis revealed a notable growth in scientific output from 2019, with an emphasis on techniques such as convolutional neural networks and systems based on the facial action coding system (FACS- CNN). The results highlight improvements in diagnostic accuracy thanks to the use of deep learning, although challenges related to data quality and availability remain. This study underscores the importance of international collaboration and technological innovation to advance the diagnosis and treatment of autism, offering a comprehensive perspective on current and future trends in this interdisciplinary field.
A comparison of approaches for modeling software security requirements using unified modeling language extensions Hassan, Syed Muhammad Junaid; Shahab, Aamir; Tabba, Fatima Ali; Alrammal, Muath; Abu-Amara, Fadi; Nadeem, Muhammad
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.pp2911-2927

Abstract

The unified modeling language (UML) supports extension mechanisms called stereo-types, tagged values, and constraints to extend its modeling capabilities. These extension mechanisms are utilized to create new and customized profiles. Their applications in modeling emerging security requirements are discussed. To model authentication, availability, integrity, access control, confidentiality, data integrity, non-repudiation, authorization, encryption, hashing, and session mechanisms, a set of novel stereotypes is proposed in this paper. The proposed stereotypes inherit from baseline security requirements. Further, security concepts within the UML diagram are represented using these stereotypes. In addition, the proposed stereotypes were evaluated with the help of human subject evaluation using real-world scenarios to illustrate the usefulness of these stereotypes in modelling security requirements. The contribution of this paper is a stereotyped model security requirements and library of existing security notations with high quality symbols which can be incorporated in existing and new stereotypes and diagrams to facilitate the process of security requirement modelling. Results indicate that the proposed stereotyped model improves the modeling process of security requirements. It also provides a better representation of emerging security mechanisms in software design. Finally, during the software development process, stakeholders enjoy improved communication and understanding of security requirements.
Highly sensitive microwave sensor for metallic mine detection Aldhaeebi, Maged A.; Almoneef, Thamer S.
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.pp2631-2641

Abstract

This study introduces an innovative microwave system for detecting buried metallic landmines, providing an alternative to conventional imaging approaches. The system consists of two highly sensitive sensors, each configured with identical antennas arranged in a triangular formation to enhance sensitivity. The proposed microwave sensors exhibit exceptional sensitivity in detecting metallic landmines buried at various depths within sand and at different distances. Simulation and experimental studies were conducted using a foam box filled with sand and a metallic cube to simulate a landmine. The sensor’s sensitivity is evidenced by shifts in both the magnitude and phase of insertion loss (????21) between scenarios with and without a metallic mine, attributed to differences in dielectric properties between the sand and the mine in the microwave spectrum. The results from both simulations and experiments confirm the sensor’s capability to detect metallic mines at varying depths within the sand medium. The proposed system offers significant advantages over imaging technologies for mine detection, including cost-effectiveness, simplicity, and ease of data processing without the need for complex imaging algorithms.
Exploring the effectiveness of multiclass decision jungle for internet of things security Rajagopal, Smitha; Sarkar, Abhik; Manjunath, Venkat Narayanan
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.pp3095-3106

Abstract

Network intrusion detection systems (NIDS) are vital in protecting computer networks against cyber security incidents. The relationship between NIDS and internet of things (IoT) security is pivotal and NIDS plays a significant role in ensuring the security and reliability of IoT ecosystems. Ensuring the security of IoT devices is critical for several reasons. It helps safeguard sensitive information, guarantees the dependability of crucial infrastructure, meets regulatory obligations, and fosters user confidence. As the IoT ecosystem expands, prioritizing security is essential to minimize risks and maximize the benefits of connected devices. Given the ever-expanding cyber threat landscape, the multiclass classification task is essential to empower the NIDS with an ability to distinguish between various attack patterns in less computational time. The multiclass decision jungle algorithm is investigated to optimize the performance of NIDS. The research has considered permutation feature importance to include only the relevant features from the data. Using a contemporary dataset such as CICIOT 2023, the study has demonstrated an impressive attack detection rate of over 90% for 20 modern attack types. This research has investigated the effectiveness of IoT security measures and its prospective contributions to the field of cyber security.
A prediction of coconut and coconut leaf disease using MobileNetV2 based classification Gopalakrishna, Kavitha Magadi; Lingaraju, Raviprakash Madenur
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.pp2834-2844

Abstract

This research is aimed at effectively predicting coconut and coconut leaf disease using enhanced MobileNetV2 and ResNet50 methods. The stages involved in this implemented method are data collection, pre-processing, feature extraction, and classification. At first, data is collected from coconut and coconut leaf datasets. Gaussian filter and data augmentation techniques are applied on these images to eliminate noise during the pre-processing phase. Then, features are extracted using ResNet50 technique, while the diseases are classified using MobileNetV2 approach. In comparison to the existing methods namely, EfficientDet-D2, DL-assisted whitefly detection model (DL-WDM), and modified inception net-based hyper tuning support vector machine (MIN-SVM), the proposed method achieves superior classification values with 99.99% and 99.2% accuracy for coconut leaf and for coconuts, respectively.

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