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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 6,301 Documents
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Investigation of the satellite internet of things and reinforcement learning via complex software defined network modeling Kumar, Arun; Chakravarty, Sumit; Nanthaamornphong, Aziz
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.pp3506-3518

Abstract

The satellite internet of things (SIoT) has emerged as a transformative technology, enabling global connectivity and extending IoT infrastructure to remote and underserved regions. This paper explores the integration of SIoT with advanced reinforcement learning (RL) techniques through sophisticated software-defined networking (SDN) modeling. The study emphasizes SDN’s capability to offer flexible, dynamic, and efficient management of satellite-based IoT networks, addressing unique challenges such as high latency, limited bandwidth, and frequent mobility. To address these challenges, we propose an RL based approach for optimizing network resource allocation, routing, and communication strategies. The RL algorithm enables autonomous adaptation to real-time network conditions, tackling critical concerns such as spectrum management, energy efficiency, and load balancing, ensuring reliable connectivity while minimizing congestion and power consumption. Furthermore, SDN facilitates network programmability, enabling centralized control and streamlined management of SIoT systems. The proposed RL-driven SDN model is validated through simulation experiments, demonstrating significant improvements in throughput, network efficiency, and quality of service (QoS) metrics compared to traditional network models. This work advances the development of satellite IoT networks by providing a robust, scalable framework that integrates RL and SDN technologies, offering intelligent and efficient connectivity solutions to meet the growing demands of next-generation SIoT systems.
Arabic offensive text classification using emojis: Including emoji data in Arabic natural language processing Albalawi, Amal; Yafooz, Wael M. 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.pp3332-3345

Abstract

In the digital social media ecosystem, controlling offensive language requires advanced algorithmic tools. This study examines the influence of including emojis translation in the text preprocessing stage of the classification of offensive Arabic text. A novel dataset of 10,000 Arabic tweets was developed, with rigorous annotations to classify content as offensive or non-offensive. The dataset was meticulously annotated and validated using Cohen's kappa (CK) and Krippendorff's Alpha (α) to ensure consistency and accuracy. Several experiments evaluated the dataset with the most common text classification models: seven machine learning (ML) classifiers and three deep learning (DL) models. Two experimental sets were conducted: one with emoji translation in preprocessing to enrich text input and another without emoji translation to directly assess the impact of emojis on classification accuracy. The findings indicate that emojis significantly affect text classification models, with advanced DL models showing higher sensitivity to contextual nuances conveyed by emojis compared to traditional ML classifiers. This research highlights the dual role of emojis, which are often linked to positive emotions and offensive contexts, adding complexity to digital communication. It contributes to the development of more accurate and context-sensitive natural language processing (NLP) tools.
Fault tolerant design for 8-bit Dadda multiplier for neural network applications Chandrasekharan, Raji; Prasad, Sarappadi Narasimha
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.pp2697-2705

Abstract

As digital electronic systems continue to shrink in size, they face increased susceptibility to transient errors, especially in critical applications like neural networks, which are not inherently error-resilient. Multipliers, fundamental components of neural networks, must be both fault tolerant and efficient. However, traditional fault free designs consume excessive power and require substantial silicon real estate. Among existing multiplier architectures, the Dadda multiplier stands out for its speed and efficiency, but it lacks fault tolerance needed for robust neural network applications. Therefore, there is need to design a power efficient and fault free Dadda multiplier that can address these challenges without significantly increasing power consumption or hardware complexity. In this paper a solution involving a fault tolerant Dadda multiplier optimized for neural network applications is proposed. Because of its speed and efficiency when compared to other multipliers Dadda multiplier is used as the base architecture which is designed using carry select adder (CSA) in conjunction with binary to excess one converter to reduce power and complexity. To enhance fault tolerance, self-repairing full adder is used to implement the CSA. This allows the system to detect and correct errors, ensuring robust operation in the presence of transient faults. This combination achieves a power efficient, fault tolerant multiplier with a power consumption of 52.3 mW, reflecting a 3% reduction in power compared to existing designs.
Personalized learning recommendations based on graph neural networks Chetoui, Ismail; Bachari, Essaid El
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.pp3246-3256

Abstract

This paper presents a novel graph neural network (GNN)-based model for personalized learning with advanced graph neural networks, incorporating both graph convolutional networks (GCN) and graph attention Networks (GAT). Our model leverages GCN, which consists of multiple layers embedding deep learning models, to aggregate data from neighboring nodes and capture the intricate relationships between students and courses. The GAT layers refine these embeddings by dynamically assigning importance weights to connections, prioritizing relationships critical for personalized course recommendations. This dual-layered approach enables the model to account for both global structural patterns and locally significant interactions within the student-course graph. We evaluated the performance of our model using the open university learning analytics dataset (OULAD), a rich dataset encompassing student demographic information, interaction data, and course performance metrics. Experimental results achieved 78.9% F1-score, 78.3% precision, and 76.2% recall in personalized recommendations, outperforming single-layer GCN implementations by approximately 15 percentage points. These results demonstrate the model's ability to handle complex, dynamic relationships in educational data, ensuring more relevant and effective recommendations. By addressing key challenges in recommendation systems, such as the need for dynamic weighting of relationships and the handling of sparsity in educational data, our study underscores the transformative potential of GNNs in advancing personalized education. This work sets the stage for further exploration of GNN applications in e-learning, paving the way for adaptive and intelligent course recommendation systems that align with individual learning needs and preferences.
Optimizing rice leaf disease classification through convolutional neural network architectural modification and augmentation techniques Firdaus, Mohamad; Kusrini, Kusrini; Agastya, I Made Artha; Martínez-Béjar, Rodrigo
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.pp3429-3438

Abstract

This research focuses on advancing the accuracy of rice leaf disease classification through the integration of convolutional neural network (CNN) and deep learning models. With Indonesia ranking third in global rice production, effective crop management is crucial for sustaining agricultural output. This study employs innovative data augmentation techniques, including random zoom and others, to enhance model training robustness. The experimentation involves eight scenarios with varied architectural configurations applied to a residual network-50 (ResNet50) layers model, aiming to optimize disease classification performance. Featuring random zoom without the multilayer perceptron (MLP) component, emerges as the most effective, demonstrating superior accuracy and performance metrics. A grid search is conducted to optimize MLP layers, revealing a three-layer configuration as most effective. We found that the data augmentation and MLP layer can increase the accuracy of the disease classification task. The method proposed in this study is likely to have a much higher proportion of correct disease classification by combining MLP and zoom augmentation. Specifically, the model with three MLP layers and zoom augmentation demonstrated significantly higher accuracy, achieving a test accuracy, precision, recall, and F1-score of 0.92, 0.94, 0.92, and 0.92, respectively.
Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8 Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Adriansyah, Andi; Muttaqin, Muhammad Husni
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.pp2796-2808

Abstract

Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
A ten-year retrospective (2014-2024): Bibliometric insights into the study of internet of things in engineering education Yusoff, Zakiah Mohd; Nordin, Siti Aminah; Othman, Norhalida; Bakar, Zahari Abu; Ismail, Nurlaila
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4213-4226

Abstract

This article presents a comprehensive ten-year retrospective analysis (2014-2024) of the evolving landscape of internet of things (IoT) studies within engineering education, employing bibliometric insights. The pervasive influence of IoT technologies across diverse domains, including education, underscores the significance of examining its trajectory in engineering education research over the past decade. Recognizing the dynamic nature of this intersection is crucial for educators, researchers, and policymakers to adapt educational strategies to IoT-induced technological shifts. Addressing this imperative, the study conducts a detailed bibliometric review to identify gaps, trends, and areas necessitating further exploration. Methodologically, the study follows a framework involving a comprehensive search of Scopus and Web of Science databases to identify relevant articles. Selected articles undergo bibliometric analysis using the Biblioshiny tool, supplemented by manual verification and additional analysis in Excel. This approach facilitates robust evaluation of citation patterns, co-authorship networks, keyword trends, and publication patterns over the specified timeframe. Anticipated outcomes include the identification of seminal works, key contributors, influential journals, and science mapping. The study aims to unveil emerging themes, track research trends, and provide insights into collaborative networks shaping IoT discourse in engineering education. This analysis offers a roadmap for future research directions, guiding educators and researchers toward fruitful avenues of exploration.
Efficient high-gain low-noise amplifier topologies using GaAs FET at 3.5 GHz for 5G systems Zarrik, Samia; Bendali, Abdelhak; Fadlaoui, Elmahdi; Benkhadda, Karima; Habibi, Sanae; Kobbi, Mouad El; Sahel, Zahra; Habibi, Mohamed; Hadjoudja, Abdelkader
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3833-3842

Abstract

Achieving a gain greater than 18 dB with a noise figure (NF) below 2 dB at 3.5 GHz remains a formidable challenge for low-noise amplifiers (LNAs) in sub-6 GHz 5G systems. This study explores and evaluates various LNA topologies, including single-stage designs with inductive source degeneration and cascade configurations, to optimize performance. The single-stage topology with inductive source degeneration achieves a gain of 18.141 dB and an NF of 1.448 dB, while the cascade-stage common-source low-noise amplifier with inductive degeneration achieves a gain of 32.714 dB and a noise figure of 1.563 dB. These results underscore the importance of GaAs FET technology in meeting the demanding requirements of 5G systems, specifically in the 3.5 GHz frequency band. The advancements demonstrated in gain, noise figure, and linearity affirm the viability of optimized LNA topologies for high-performance 5G applications, supporting improved signal quality and reliability essential for modern telecommunication infrastructure.
Load frequency control for integrated hydro and thermal power plant power system Nguyen, Vu Tan; Tran, Thinh Lam-The; Tuan, Dao Huy; Hien, Dinh Cong; Nguyen, Vinh Phuc; Huynh, Van Van
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3583-3592

Abstract

Persistent electrical supply requires the power systems to be stable and reliable. Against varying load conditions, control strategies such as load frequency control (LFC) is a key mechanism to protect its stability. Traditional control strategies for LFC often face challenges due to system uncertainties, external disturbances, and nonlinearities. This paper presents an advanced approach to control load frequency and enhancing LFC in power systems by using sliding mode control (SMC). SMC offers powerful stability and robustness versus nonlinearities and perturbation, making it a promising approach for addressing the limitations of conventional control methods. We contemporary a comprehensive analysis of the SMC approach tailored for LFC, including the strategy and employment of the control algorithm. The proposed method makes use of a sliding/gliding surface to enable the system trajectories to be continuous on this surface despite parameter variations and external disturbances. Simulation results demonstrate significant improvements in frequency stability and system performance compared to conventional proportional-integral-derivative (PID) controllers. The paper also includes a comparative analysis of SMC with other modern control techniques, highlighting its advantages in terms of robustness and adaptability.
Blockchain and internet of things synergy: transforming smart grids for the future Bensalah, Mouad; Hair, Abdellatif; Rabie, Reda
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4239-4248

Abstract

Conventional smart grid systems face challenges in security, transparency, and efficiency. This study addresses these limitations by integrating blockchain and internet of things (IoT) technologies, presenting proof-of-concept implemented on an Orange Pi 4 single-board computer. The realized prototype demonstrated secure and transparent energy transaction management with consistent throughput between 7.45 and 7.81 transactions per second, and efficient resource utilization across varying transaction volumes. However, scalability challenges, including a linear increase in processing time with larger block sizes, emphasize the need for optimized consensus mechanisms. The findings underscore the feasibility of blockchain-based smart grids in resource-constrained settings, paving the way for advancements in peer-to-peer energy trading, decentralized energy storage, and integration with artificial intelligence for dynamic energy optimization. This work contributes to developing secure, efficient, and sustainable energy systems.

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