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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 4: August 2024" : 111 Documents clear
A novel configuration of a microstrip metamaterial reconfigurable bandstop filter Aghanim, Amina; Oulhaj, Otman; Zbitou, Jamal; Oukaira, Aziz; Lakhssassi, Ahmed; Lasri, Rafik
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4128-4137

Abstract

This paper presents the design, simulation, and test measurements of a microstrip bandstop filter operating at 1.5 GHz, incorporating six split ring resonator (SRR) unit cells. The substrate employed is an FR-4 with a thickness of 1.6 mm and tangent losses of 0.025. In the initial phase, the design is conceptualized, simulated using computer simulation technology (CST) studio and advanced design system (ADS) Agilent simulators, and validated through test measurements. Building upon this foundation, the filter is transformed into a reconfigurable variant by integrating four SMV2019 varactor diodes. These varactors are modeled to ensure the reconfigurability of the bandwidth. The integration of varactors introduces dynamic tuning capabilities to the considered bandstop filter.
Exploring the research trends and development of augmented reality and virtual reality in ASEAN countries: a bibliometric study Hariyanto, Didik; Rafiq, Arif Ainur; Gunawan, Teddy Surya; Quynh, Nguyen Vu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4430-4444

Abstract

This review of Association of Southeast Asian Nations (ASEAN) augmented reality (AR) and virtual reality (VR) studies uses bibliometric analysis and VOSviewer mapping. This study looks at an extensive set of Scopus articles from reliable sources to determine who contributes to ASEAN AR and VR research, the themes, how people work together, and how people cite each other. A study of bibliographies shows that the number of ASEAN AR and VR research articles has grown significantly since 2010. It also talks about important ASEAN study institutions, authors, and countries. The study themes are shown visually on VOSviewer mapping, showing how AR and VR can be used in healthcare, travel, gaming, and business. Co-authorship and reference networks shed light on how people work together on research projects and how ideas move within and outside of ASEAN. This organized review of ASEAN AR and VR research helps researchers, policymakers, and business stakeholders understand the current situation, find research gaps, and work together. The results can change research, resource use, and policy changes to encourage the growth and use of AR and VR technologies in ASEAN. It can lead to more innovation, economic development, and positive social effects.
Enhancing 5G network performance through effective resource management with network slicing Suganthi, Nagarajan; Narasimhan Ganesh, Enthrakandi; Guruva Reddy, Elangovan; Balakumar, Vijayaraman; Ilakkiya, Thangam; Varadarajan, Mageshkumar Naarayanasamy; Ramesh Babu, Venkatachalam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4721-4731

Abstract

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.
Method for design and implementation of telecommunication devices for aircraft Yerzhan, Assel; Kozhabayeva, Indira; Manbetova, Zhanat; Boykachev, Pavel; Nauryz, Kanysh; Zhazykbaeva, Zhazira; Seitova, Zhadra; Aitzhanova, Nursulu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4183-4194

Abstract

This article highlights the importance of electrical filters in radio engineering devices, emphasizing their role in transmitting signals in the transparency band and suppressing signals in the stop band. We examined methods for designing frequency-selective circuits with lumped parameters, which, in general, are a complete field of radio engineering and allow the synthesis of devices of varying complexity. The focus of the article is the frequency region, where the distributed properties of the synthesized structures appear. The article also provides an overview of various methods for synthesizing ultra-high frequency (UHF) filters. It is emphasized that for low-pass filters a transition from a low-frequency prototype to a high-frequency representation is applied, which, despite the crudeness of the approach, provides satisfactory results that can be improved at the production stage. The article also discusses various methods for implementing bandpass filters on distributed elements, including the use of short-circuited and open-circuited stubs, as well as weak-coupled lines. In conclusion, the paper highlights the need to improve these methods to improve process accuracy and make filter designers more efficient in radio engineering.
Novel hybrid of marine predator algorithm-Aquila optimizer for droop control in DC microgrid Aribowo, Widi; Suryoatmojo, Heri; Pamuji, Feby Agung
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3703-3715

Abstract

This study presents a hybrid method, namely the marine predator algorithm (MPA) and Aquila optimizer (AO). The proposed algorithm is named MAO. AO duplicated the existence of the Aquila bird in nature while hunting for prey while MPA was inspired by predators in marine animal life. Although AO is widely accepted, it has several disadvantages. This causes various weaknesses such as a weak exploitation phase and slow growth of the convergence curve. Thus, certain exploitation and exploration in conventional AO can be studied to achieve the best balance. The MPA demonstrates the capacity to deliver optimal design and statistically efficient outcomes. The proposed method used AO as the main algorithm. To measure the performance of the proposed method, this study depicted a comparison using the AO, MPA, and whale optimization algorithm (WOA) methods. This paper was evaluated the performance of MAO on twenty-one CEC2017 benchmark functions test and droop control performance on direct current (DC) microgrid. From the simulation, MAO shows superior convergence ability. The proposed method and its application to droop control was successfully implemented and implied a promising performance.
Optimizing breast cancer diagnosis: combining hybrid architectures through Apache Spark Taib, Chaymae; Abdoun, Otman; Haimoudi, El Khatir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4261-4272

Abstract

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.
Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification Huddar, Suma; Prabhushetty, Kopparagaon; Jakati, Jagadish; Havaldar, Raviraj; Sirdeshpande, Nandakishor
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3971-3985

Abstract

Plant leaf diseases pose significant threats to global agriculture, leading to reduced crop yields and economic losses. Rapid and accurate disease detection is essential for timely interventions and sustainable farming practices. This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification. Wavelet analysis is employed to extract multi-resolution features, capturing intricate details at different scales. Furthermore, color and texture characteristics are extracted to encompass a broad spectrum of visual information crucial for distinguishing diseases. The Autoencoder model helps to enhance the feature representation that mitigates the impact of noise and irrelevant data. The SVM classifier is utilized to learn complex patterns and accurately classify different disease classes. The combined model of wavelet, color, and texture attributes, in combination with autoencoder denoising and SVM classification, markedly enhances the precision and efficiency of disease detection in contrast to conventional methods. The system's performance is evaluated using a PlantVillage dataset, showcasing its adaptability to different plant species and disease types. The overall performance is obtained as 98.60%, 97.25%, 96.89%, and 97.20% in terms of accuracy, precision, recall, and F-Score, respectively.
Comparing Mask R-CNN backbone architectures for human detection using thermal imaging Trinh, Tan Dat; Cung Le Thien Vu, Pham; The Bao, Pham
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3962-3970

Abstract

We introduce a method for detecting humans in thermal imaging using an end-to-end deep learning model. Our objective is to optimize the human detection process in thermal imaging by investigating the mask region-based convolutional neural network (Mask R-CNN). The model, an advancement of the faster region-based convolutional neural network (Faster R-CNN), not only captures bounding boxes encompassing human subjects but also delineates segmentation masks around them. Our investigation extends to the evaluation and comparison of various convolutional neural networks for feature learning, like residual network (ResNet) and Inception ResNet, all integrated into the Mask R-CNN framework. Furthermore, the experimental results show that our proposed technique achieves high accuracy. Specifically, the Mask R-CNN model using ResNet50-V1 achieved the best results, with an F-value of 87.85%, a recall of 79.33%, and a precision of 98.41%.
Email subjects generation with large language models: GPT-3.5, PaLM 2, and BERT Loukili, Soumaya; Fennan, Abdelhadi; Elaachak, Lotfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4655-4663

Abstract

In order to enhance marketing efforts and improve the performance of marketing campaigns, the effectiveness of language generation models needs to be evaluated. This study examines the performance of large language models (LLMs), namely GPT-3.5, PaLM 2, and bidirectional encoder representations from transformers (BERT), in generating email subjects for advertising campaigns. By comparing their results, the authors evaluate the efficacy of these models in enhancing marketing efforts. The objective is to explore how LLMs contribute to creating compelling email subject lines and improving opening rates and campaign performance, which gives us an insight into the impact of these models in digital marketing. In this paper, the authors first go over the different types of language models and the differences between them, before giving an overview of the most popular ones that will be used in the study, such as GPT-3.5, PaLM 2, and BERT. This study assesses the relevance, engagement, and uniqueness of GPT-3.5, PaLM 2, and BERT by training and fine-tuning them on marketing texts. The findings provide insights into the major positive impact of artificial intelligence (AI) on digital marketing, enabling informed decision-making for AI-driven email marketing strategies.
Modeling and simulation for flashover location determination on 150 kV insulator string Sitti Amalia; Sitti Amalia; Warmi, Yusreni; Amalia, Sitti; Zulkarnaini, Zulkarnaini; Dasman, Dasman; Bachtiar, Antonov; Anthony, Zuriman; Azhar, Hamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3716-3728

Abstract

The 150 kV Payakumbuh-Koto Panjang transmission line in West Sumatra is located in an area with high lightning activity. Based on Meteorological, Climatological, and Geophysical Agency (BMKG) data (2017-2023), the average number of lightning days per year (IKL: isokeraunic level) reaches 165-173 days/year, and 79% of the transmission towers are located in hilly and rocky areas. This causes contamination of the insulator, which can reduce its performance and cause flashovers in the insulator circuit. However, in the field, finding flash points in insulators is still a challenge. Therefore, simulation must be used as a tool to determine the location of flashover in an insulator circuit that is affected by temperature and humidity. Simulation by modeling flashover provides an effective solution for determining the location of flashover in insulator circuits, which is the novelty of this research. This research compares laboratory test results with manual calculations modeled using Visual Basic 6. The research results show that temperature and humidity have a significant influence on determining the flashover voltage value on the insulator. The flashover locations during the test are the same as the calculated flashover locations, as shown by these simulations and modeling.

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