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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 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
A novel semi-supervised consensus fuzzy clustering method for multi-view relational data Thi Canh, Hoang; Huy Thong, Pham; The Huan, Phung; Thuy Trang, Vu; Nhu Hieu, Nguyen; Tien Phuong, Nguyen; Nhu Son, Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6883-6893

Abstract

Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent research topic in recent years. In this paper, we propose a novel approach: the semi-supervised consensus fuzzy clustering method for multi-view relational data (SSCFMC). This method combines the advantages of fuzzy clustering and consensus clustering to address the challenges posed by multi-view data. By leveraging available labeled information and the relational structure among views, our method aims to enhance clustering performance. Extensive experiments on benchmark datasets demonstrate that our method surpasses existing single-view and multi-view relational clustering algorithms in terms of accuracy and stability. Specifically, the SSCFMC algorithm exhibits superior clustering performance across various datasets, achieving an adjusted rand index (ARI) of 0.68 on the multiple features dataset and an F-measure of 0.91 on the internet dataset, highlighting its robustness and efficiency. Overall, this study advances multi-view clustering techniques for relational data and provides valuable insights for researchers in this field.
Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things Metan, Jyoti; Mathapati, Mahantesh; Yogegowda, Prasad Adaguru; Ananda Kumar, Kurilinga Sannalingappa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7169-7177

Abstract

Cyber-physical system (CPS) is an integral part of an internet of things (IoT) with established wide spread applications. An increasing concern towards being highly vulnerable to various forms of dynamic cyber-attacks has been increasingly evolving. A review of existing research methodology showcases complex solutions that can offer sub-optimal security strength when exposed to dynamic cyber-attack forms while increasing the computational burden. Therefore, this manuscript presents a novel yet simplified computational framework capable of determining and resisting critical anomalies within internet-of-cyber physical systems (IoCPS). The presented scheme contributes towards preprocessing following a distinct oversampling method targeting balancing the data. An ensemble machine learning model using a discrete variant of AdaBoost and neural decision tree (NDT) has been implemented to optimize the learning process and improve the threat detection efficiency. The comparative outcome of the proposed study showcases that it offers approximately 7.2% increased threat detection accuracy and approximately 68% reduced response time compared to frequently adopted learning mechanisms towards threat detection over an IoT environment.
Evolution of the concept of device moving from internet of things to artificial intelligence of things Paolone, Gaetanino; Pilotti, Francesco; Piazza, Andrea
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7236-7243

Abstract

The internet of things (IoT) refers to a network of physical devices that are embedded with sensors, software, and network connectivity, allowing them to collect and share data. The devices are the core of any IoT ecosystem. Browsing the extant literature, it emerges that the meaning of the term device depends on the reference context. It follows that, it is an important topic to investigate the reasons behind such a degree of indeterminacy. This paper elaborates on the evolution of the concept of device moving from IoT to artificial intelligence of things (AIoT). The finding that comes from this study is that this evolution is a direct consequence of the evolution of the IoT computing paradigms.
Effective ethernet controller protocol architecture verification strategy using system Verilog Parameshwara, Shubha; Sagar, Ganapathi Vithoba; Dasappa, Hamsa Rekha Sorekunte; Nagaraj, Sheetal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6195-6203

Abstract

The pre-silicon verification is typically more significant than post-silicon verification, which produces an algorithm with the correct functionality and timing parameters. In this paper we propose innovative pre-silicon verification methodology focused on the Ethernet controller architecture as the design under test (DUT). The methodology employs a layered verification architecture implemented using the system Verilog language, aiming to streamline the testing process. A novel test pattern test generator, interfaces and blocks are used to perform the verification. The test patterns are generated based on the operational principles of the ethernet controller block, ensuring comprehensive verification coverage. Additionally, the paper combines different verification parameters with existing approaches to demonstrate the effectiveness of the proposed methodology. It is observed that the performance of the proposed method is better compared to existing methods.
Traffic signs detection and prohibitor signs recognition in Morocco road scene Taouqi, Imane; Klilou, Abdessamad; Chaji, Kebir; Arsalane, Assia
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6313-6321

Abstract

Traffic sign detection is a crucial aspect of advanced driver assistance systems (ADAS) for academic research and the automotive industry. seeing that accurate and timely detection of traffic signs (TS) is essential for ensuring the safety of driving. However, TS detection methods encounter challenges like slow detection speed and a lack of robustness in complex environments. This paper suggests addressing these limitations by proposing the use of the you only look one version 7 (YOLOv7) network to detect and recognize TS in road scenes. Furthermore, the k-means++ algorithm is used to acquire anchor boxes. Additionally, a tiny version of YOLOv7 is used to take advantage of its real-time and low model size, which are required for real-time hardware implementation. So, we conducted an experiment using our proprietary Morocco dataset. According to the experimental results, YOLOv7 achieves 85% in terms of mean average precision (mAP) at 0.5 for all classes. And YOLOv7-tiny obtains 90% in the same term. Afterward, a recognition system for the prohibitive class using the convolutional neural network (CNN) is trained and integrated inside the YOLOv7 algorithm; its model achieves an accuracy of 99%, which leads to a good specification of the prohibitive sign meaning.
Design and analysis of a Sub-6 GHz antenna array with high gain for 5G mobile phone applications Bellekhiri, Abderrahim; Chahboun, Noha; Zbitou, Jamal; Oukaira, Aziz; Laaziz, Yassin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6401-6410

Abstract

In this paper, we designed, analyzed, and simulated a 32-element antenna array for the sub-6 GHz band. Each radiating element is a square patch on a Rogers RT5880 substrate, featuring a relative dielectric permittivity of 2.2, a low-loss tangent of 0.0009, and a thickness of 0.508 mm, measuring 28.1×28.1 mm². Simulations were conducted using two electromagnetic solvers, advanced design system (ADS) and CST Microwave Studio, providing a comprehensive comparison of the results. To achieve a high balance between performance and antenna size, two 4×8 array antenna structures were designed. The simulations demonstrated excellent input impedance matching around 3.5 GHz for both configurations, with high gains of 20.5 dBi for the first and 18 dBi for the second configuration, along with directional radiation patterns. The dimensions were 315×576×0.578 mm³ for the first configuration and 170×961×0.578 mm³ for the second. These performance metrics make the proposed antenna arrays highly suitable for wireless communication technologies operating below 6 GHz, particularly for 5G mobile communications.
Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco Stitou, Hicham; Atillah, Mohamed amine; Boudaoud, Abdelghani; Aqil, Mounaim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7067-7079

Abstract

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region’s weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%.
Time and wavelength diversity schemes for transdermal optical wireless links Almajdoubah, Rawan; Hasan, Omar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6423-6432

Abstract

Transdermal optical wireless communication systems have gained significant research and commercial attention in recent years. While using the skin as a transmission medium, the performance of such a system is influenced by the transdermal channel conditions between the transmitter and receiver. Indeed, for transdermal optical channel links, the performance of a linked communication system can be significantly affected by skin- induced attenuation and pointing errors. Extra attention has been given to diversity techniques to anticipate this. This research examines a conventional transdermal optical wireless system by analyzing the outage probability, an important performance parameter that has not been previously evaluated for such systems using the same method. In an examination of outage probability, both skin-induced attenuation and stochastic spatial jitter, also referred to as pointing error effects, are included. The usefulness of this topic is demonstrated using analytical formulas and results that determine the probability of system outage under various skin channel conditions and varying levels of stochastic pointing faults. Simulation model using Monte- Carlo simulation method was conducted using MATLAB to validate our suggestions. The simulation results showed a good agreement with numerical results, which proved the effectiveness of using wavelength and time diversity schemes to enhance transdermal optical wireless based systems.
Performance analysis of hybrid bio-inspired algorithms for classifying brain tumors in imbalanced magnetic resonance imaging datasets Chakre, Rahul Ramesh; Vaidya, Archana S.; Patil, Dipak V.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6339-6350

Abstract

Magnetic resonance imaging (MRI) is a substantial imaging procedure for diagnosing brain tumors. However, brain tumor classification continues challenging due to the unequal distribution of classes within datasets, complicating precise diagnosis and classification. This research focuses on the class imbalance in medical image datasets by proposing a hybrid bio-inspired algorithm for brain tumor classification. A rider optimization and particle rider mutual information-based dendritic-squirrel search algorithm combined with an artificial immune classifier is developed and tested on imbalanced datasets generated from BRATS and SimBRATS. Experimental outcomes are encouraging, For the imbalanced BRATS dataset, the rider optimization- based classifier achieved an accuracy of 94.84%, sensitivity of 92.96%, and specificity of 94.95%. The particle rider mutual information-based classifier outperformed others with 96.25% accuracy, 94.33% sensitivity, and 94.85% specificity. For the imbalanced SimBRATS dataset, the rider optimization-based classifier achieved 94.95% accuracy, 92.05% sensitivity, and 94.04% specificity. The particle rider mutual information-based classifier excelled with 96.35% accuracy, 94.42% sensitivity, and 95.44% specificity. These findings suggest that the proposed algorithm effectively addresses class imbalance in medical image datasets, offering a robust solution for brain tumor classification. The particle rider mutual information-based classifier shows promise for enhancing diagnostic accuracy in clinical settings, demonstrating the efficacy of hybridized bio-inspired algorithms in managing imbalanced datasets.
Enhanced Vigenere encryption technique for color images acting at the pixel level Chemlal, Abdelhakim; Tabti, Hassan; El Bourakkadi, Hamid; Rrghout, Hicham; Jarjar, Abdellatif; Benazzi, Abdelhamid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6675-6688

Abstract

The pixel unit is an essential component of many encryption schemes. In the beginning two substitution tables, separately constructed from chaotic maps namely, the logistic map, slanted tent map, and the AJ map, which has a very high Lyapunov exponent and is very sensitive to start factors, are used to make modifications at the pixel level. These S-Boxes have a maximum period and are produced from several linear congruential generators. This approach uses newly developed confusion and diffusion functions connected to the recently built substitution tables to perform a refined Vigenere strategy. The purpose of this chaining is to defend the system from differential assaults. Extensive simulations on a variety of image formats and sizes confirm our process’s robustness against identified dangers.

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