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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 2: April 2024" : 111 Documents clear
Interoperability for intelligent traffic management systems in smart cities Alanazi, Fayez; Alenezi, Mamdouh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1864-1874

Abstract

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.
A 3D reconstruction-based method using unmanned aerial vehicles for the representation and analysis of road sections Benmhahe, Brahim; Alami Chentoufi, Jihane; Basmassi, Mohamed Amine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1552-1564

Abstract

Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.
Design and analysis of wideband four-port multiple input multiple output antenna using defective ground structure for 5G communication Tamminaina, Govindarao; Manikonda, Ramesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1646-1653

Abstract

This research work describes a compact four-port multiple input multiple output (MIMO) antenna using defective ground structure (DGS), and it perfectly supports the n77, n78, and n79 frequencies in 5G new radios (NR) bands. It can cover a wideband from 3.4 to 5.4 GHz with good impedance matching. The pair of antenna elements are orientated opposite to another pair with DGS. Due to this technique, it has minimal complexity, is less expensive, and improves isolation. It has also improved the frequency band's reflection coefficient and range using MIMO antenna with different stub lengths. Because it is less expensive, FR-4 substrate is used in the implementation of all antennas. Each antenna element has two identical stubs linked to the primary radiator. On the primary radiating element, a ''HI'' slot is created. The partial ground enhances impedance matching and radiation properties throughout the targeted band. The total dimensions of the four-port MIMO antenna are 46×30×1.6 mm3. The array elements' mutual coupling in the simulation is -14 dB. The ECC value is below 0.01, and the diversity gain (DG) is less than 10 dB. The suggested designs' measured gain ranges from 10 to 11.0 dB, and the radiation efficiency is nearly 91%.
Performance analysis of 2D optical code division multiple access through underwater wireless optical medium Islam, Md. Rabiul; Islam, Md. Jahedul; Mitra, Bithi; Hossain, Md. Amzad; Islam, Jahedul; Dev, Shuvo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1665-1673

Abstract

The performance of a two-dimensional optical code division multiple access (2D-OCDMA) system using an underwater wireless optical (UWO) medium is assessed in this work. The optical source is an LED with a working wavelength of 532 nm, and the optical detector is a p–i–n photodiode. When calculating the bit error rate (BER), the phase-induced intensity noise (PIIN), thermal noises, and shot sounds are taken into account. The user code address is set using 2D perfect difference (2D-PD) codes. Link distance, inclination angle, beam divergence angle, transmitter power, and the number of concurrent users are all taken into account when determining the BER performance. For various water media, such as pure sea water (PSW), clear ocean water (CLOW), and coastal ocean water (CSOW), the performance of the suggested system is examined.
Agarwood oil quality identification using artificial neural network modelling for five grades Mohd Huzir, Siti Mariatul Hazwa; Tajuddin, Saiful Nizam; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Almisreb, Ali Abd; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2254-2261

Abstract

Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.
Developing a restaurant recommended system via the Vietnamese food image classification Pham, Viet Hoang; Nguyen, Anh Thai; Phung, Bao The; Phan, Truong Ho-Viet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1711-1719

Abstract

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.
Facial emotion recognition using enhanced multi-verse optimizer method Gummula, Ravi; Arumugam, Vinothkumar; Aranganathan, Abilasha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1519-1529

Abstract

In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2145-2155

Abstract

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Developing a smart system for infant incubators using the internet of things and artificial intelligence Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2293-2312

Abstract

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
Insights of effectivity analysis of learning-based approaches towards software defect prediction Rai, Deepti; Arcot Prashant, Jyothi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1916-1927

Abstract

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

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