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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
Structure of quaternion-type algebras and a post-quantum signature algorithm Duong, May Thu; Moldovyan, Alexander Andreevich; Moldovya, Dmitriy Nikolaevich; Nguyen, Minh Hieu; Do, Bac Thi
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.pp2965-2976

Abstract

Algebraic digital signature algorithms with a commutative hidden group, which are based on the computational difficulty of solving large systems of power equa- tions, are promising candidates for post-quantum cryptoschemes, especially in securing applications like the internet of things (IoT) and other information tech- nologies. Associative finite non-commutative algebras are used as an algebraic support of the said algorithms. Among such algebras, finite quaternion-type al- gebras have been identified as strong candidates for providing algebraic support. This paper investigates the decomposition of these algebras into commutative subrings and explores their multiplicative groups, which can serve as poten- tial hidden groups. The analysis reveals the existence of three distinct types of subrings, with derived formulas for the number of subrings and the orders of their multiplicative groups. These findings align with previous studies on four- dimensional algebras defined by sparse basis vector multiplication tables. Using the finite quaternion-type algebras as algebraic support, a novel post-quantum signature algorithm characterized in using two mutually non-commutative hid- den groups has been developed.
Name privacy on named data networking: a survey and future research Shah, Mohammad Shahrul Mohd; Leau, Yu-Beng; Anbar, Mohammed; Zhao, Liang
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.pp3039-3053

Abstract

Information-centric networking (ICN) has gained significant interest in recent years, attracting both academic and industry, it represents a paradigm shift and moving away from the host-based IP networks that dominate today landscape. As ICN technology matures and advances towards real-world deployment, the importance of addressing security and privacy concerns has grown exponentially. The ICN paradigm is deliberately designed to encompass numerous security and privacy features, including but not limited to provenance and privacy. These features, which are often lacking in the host-centric paradigm, inherently form a core aspect of ICN. Nevertheless, due to its relatively recent emergence, the ICN paradigm also presents a range of unresolved privacy challenges. This paper offers a comprehensive survey of the existing literature on privacy primarily focuses on major domains name privacy. We delve into the fundamental principles of existing research and evaluate the limitations of proposed methodologies. In name privacy, we also explore strategies to preserve name privacy. We have identified future research directions and highlighted ongoing challenges in the pursuit of enhancing ICN privacy.
Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks Choukhairi, Mouad; Tahiri, Sara; Choukhairi, Ouail; Fakhri, Youssef; Amnai, Mohamed
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.pp3494-3505

Abstract

The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Enhancing cyberbullying detection with advanced text preprocessing and machine learning Dhumale, Rakesh Bapu; Dass, Ajay Kumar; Umbrajkaar, Amit; Mane, Pradeep
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.pp3139-3148

Abstract

The use of social media and the internet has been increasing dramatically in recent years. Cyber-bullying is the term used to describe the misuse of social media by some people who make threatening comments. This has a devastating influence on people's lives, especially those of children and teenagers, and can lead to feelings of depression and suicidal thoughts. The methodology proposed in this paper includes four steps for identifying cyberbullying: preprocessing, feature extraction, classification, and evaluation. The first step is to create a labeled, varied dataset. Word2Vec and term frequency-inverse document frequency are used in feature extraction to transform text into high-dimensional vectors. Word2Vec creates word embeddings using the skip-gram and continuous bag-of-words models, while term frequency-inverse document frequency assesses the text's term relevancy. Support vector machine classifiers are used in the model, and their effectiveness is compared to that of other techniques like logistic regression and naïve Bayes. The classifiers support vector machine, naïve Bayes, and logistic regression were assessed. The maximum accuracy was 95% for the support vector classifier with skip-gram and 93% for continuous bag-of-words. For sentiment categories, F1-scores, recall, and precision were computed. The average precision and recall were 0.77 and 0.79, respectively.
Particle swarm optimization tuned controllers for capacitor voltage balancing and harmonic suppression in modular multilevel converters Outazkrit, Mbarek; aamri, Faicel El; Jaoide, Essaid; Radouane, Abdelhadi; Mouhsen, Azeddine
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.pp2616-2630

Abstract

The modular multilevel converter (MMC) has become a highly attractive power converter topology for various applications due to its modularity and scalability. However, it faces significant challenges, such as capacitor voltage balancing and circulating current, which can lead to instability and high-power losses. While the sorting algorithm is commonly used to balance capacitor voltages, this paper uses an individual balancing control method as an alternative. Additionally, a proportional resonant controller is employed to suppress the second and fourth harmonics in the circulating current. This paper presents a method for tuning the parameters of both the circulating current controller and the individual balancing control using the particle swarm optimization (PSO) algorithm, which represents the main contribution of this work. The MMC system, connected to a grid with a low number of submodules, is modeled and evaluated using the PLECS and MATLAB/Simulink environments. The results demonstrate the effectiveness of the proposed PSO-based tuning method in improving the performance and stability of the MMC.
Challenges of load balancing algorithms in cloud computing utilizing data mining tools Halima, Anouar Ben; Benaboud, Hafssa
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.pp3449-3457

Abstract

In the cloud computing environment, load balancing plays an important role in the efficient operation of cloud computing, where a multitude of resources serve diverse workloads and fluctuating demands. In the rapidly evolving cloud computing, efficient resource management, and optimization are critical for maximizing performance, scalability, and cost-effectiveness. Load balancing algorithms aim to distribute workloads across cloud resources to ensure optimal utilization and maintain high availability of services. This paper presents a comparative study of load balancing algorithms in cloud computing using data mining tools. It underscores the complexity of selecting algorithms for effective load balancing in scenarios with diverse criteria, emphasizing its critical importance for future research and practical implementations. The experimental results are presented, evaluating the performance of different load balancing algorithms using data-mining tools. The outcomes highlight the substantial difficulties when building a model with unacceptable errors to cover users’ needs while selecting the desired load balancing method.
Marginalized particle filtering for reliable land vehicle navigation in global navigation satellite system-denied environments Lahrech, Abdelkabir; Soulhi, 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.pp2735-2747

Abstract

Accurate localization in land vehicle navigation systems is highly dependent on the global navigation satellite system (GNSS). However, GNSS signal outages are common in urban areas due to obstacles such as tall buildings and tunnels. To mitigate these issues, digital road maps and dead reckoning sensors, like odometers, are often integrated to provide continuous vehicle localization. This paper presents a robust estimation method to solve the fusion problem of GNSS, odometer, and digital road map measurements in the presence of GNSS out- ages. The proposed solution utilizes a marginalized particle filter (MPF), which combines the robustness of particle filtering with the efficiency of a Kalman filter to handle the linear and non-linear parts of the state and/or measurement equations, respectively. When GNSS signals are unavailable, the MPF fuses all available pseudo-range data with odometric and map information to enhance vehicle positioning. The effectiveness of the proposed method is demonstrated using real-world data in an urban transportation scenario, highlighting signifi- cant performance improvements and real-time application potential.
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.

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