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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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k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market Erny Haslina Abd Latib; Nurlaila Ismail; Saiful Nizam Tajuddin; Jasmin Jamil; Zakiah Mohd Yusoff
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3158-3165

Abstract

Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil’s resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area.
Video captioning in Vietnamese using deep learning Dang Thi Phuc; Tran Quang Trieu; Nguyen Van Tinh; Dau Sy Hieu
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3092-3103

Abstract

With the development of today's society, demand for applications using digital cameras jumps over year by year. However, analyzing large amounts of video data causes one of the most challenging issues. In addition to storing the data captured by the camera, intelligent systems are required to quickly analyze the data to correct important situations. In this paper, we use deep learning techniques to build automatic models that describe movements on video. To solve the problem, we use three deep learning models: sequence-to-sequence model based on recurrent neural network, sequence-to-sequence model with attention and transformer model. We evaluate the effectiveness of the approaches based on the results of three models. To train these models, we use microsoft research video description corpus (MSVD) dataset including 1970 videos and 85,550 captions translated into Vietnamese. In order to ensure the description of the content in Vietnamese, we also combine it with the natural language processing (NLP) model for Vietnamese.
English speaking proficiency assessment using speech and electroencephalography signals Abualsoud Hanani; Yanal Abusara; Bisan Maher; Inas Musleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2501-2508

Abstract

In this paper, the English speaking proficiency level of non-native English speakerswas automatically estimated as high, medium, or low performance. For this purpose, the speech of 142 non-native English speakers was recorded and electroencephalography (EEG) signals of 58 of them were recorded while speaking in English. Two systems were proposed for estimating the English proficiency level of the speaker; one used 72 audio features, extracted from speech signals, and the other used 112 features extracted from EEG signals. Multi-class support vector machines (SVM) was used for training and testing both systems using a cross-validation strategy. The speech-based system outperformed the EEG system with 68% accuracy on 60 testing audio recordings, compared with 56% accuracy on 30 testing EEG recordings.
Efficient lane marking detection using deep learning technique with differential and cross-entropy loss Al Mamun, Abdullah; Em, Poh Ping; Hossen, Md. Jakir; Tahabilder, Anik; Jahan, Busrat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4206-4216

Abstract

Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.
Optimizing the placement of cloud data center in virtualized environment Al-Karawi, Yassir; S. Alhumaima, Raad; Hussein Khudair, Khalid; Ahmed, Abdulmunem
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3276-3286

Abstract

In cloud mobile networks, precise assessment for the position of the virtualization powered cloud center would improve the capacity limit, latency and energy efficiency (EEf). This paper utilized the Monte Carlo oriented particle swarm optimization (PSO) and genetic algorithm (GA) to first, obtain the optimal number of virtual machines (VMs) that maximize the EEf of the mobile cloud center, second, optimize the position of the mobile data center. To fulfil such examination, a power evaluation framework is proposed to shape the power utilization of a virtualized server while hosting an amount of VMs. In addition, the total power consumption of the network is examined, including data center and radio units (RUs). This evaluation is based on linear modelling of the network parameters, such as resource blocks, number of VMs, transmitted and received powers, and overhead power consumption. Finally, the EEf is constrained to many quality of service (QoS) metrics, including number of resource blocks, total latency and minimum user's data rate.
Rectangular microstrip antenna design with multi-slotted patch and partial grounding for performance enhancement Firoz Ahmed, Md; Muhammad Touhidul Islam, Abu Zafor; Hasnat Kabir, Md
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3859-3868

Abstract

This paper presents design of a rectangular microstrip patch antenna by using multi-slotted patch and partial grounding plane techniques for both the gain and bandwidth enhancement at the same time. The antenna is designed and simulated for ultra-wideband (UWB) applications using a high frequency structure simulator (HFSS) on FR4_epoxy substrate having a size of 30×20 mm with a dielectric permittivity of 4.4, a tangent loss of 0.02, and a thickness of 0.8 mm and excited by a simple 50 Ω microstrip feed line. The simulation results show that the antenna attains an improved gain of 8.06 dB with a wider impedance bandwidth of 19.7 GHz ranges from 3.15 to 22.85 GHz. The antenna also achieves an efficiency of 96.83% with a return loss of -28.35 dB, and a directivity of 9.39 dB within the entire frequency range. These results imply that the deployment of multi-slotted patch and partial grounding techniques in designing a rectangular microstrip patch antenna is effective in improving its performance.
Robot for plastic garbage recognition Janusz Bobulski; Mariusz Kubanek
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2425-2431

Abstract

Waste and related threats are becoming more and more severe problems in environmental security. There is growing attention in waste management globally, both in developing techniques to decrease their quantity and those correlated to their neutralization and commercial use. The basic segregation process of waste due to the type of material is insufficient, as we can reuse only some kinds of plastic. There are difficulties with the effective separation of the different kinds of plastic; therefore, we should develop modern techniques for sorting the plastic fraction. One option is to use deep learning and a convolutional neural network (CNN). The main problem that we considered in this article is creating a method for automatically segregating plastic waste into seven specific subcategories based on the camera image. The technique can be applied to the mobile robot for gathering waste. It would be helpful at the terrain and the sorting plants. The paper presents a 15-layer convolutional neural network capable of recognizing seven plastic materials with good efficiency.
Comparison of two deep learning methods for detecting fire hotspots Dewi Putrie Lestari; Rifki Kosasih
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3118-3128

Abstract

Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (YOLO) method and faster region-based convolutional neural network (faster R-CNN) method. The first stage, we collected 100 image data (70 training data and 30 test data). The next stage is model training which aims to make the model can recognize fire. Later, we calculate precision, recall, accuracy, and F1 score to measure performance of model. If the F1 score is close to 1, then the balance is optimal. In our experiment results, we found that YOLO has a precision is 100%, recall is 54.54%, accuracy is 66.67%, and F1 score is 0.70583667. While faster R-CNN has a precision is 87.5%, recall is 95.45%, accuracy is 86.67%, and F1 score is 0.913022.
Notice of Retraction Combining 3D run-length encoding coding and searching techniques for medical image compression Arif Sameh Arif; Muntaha Abood Jassim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2601-2613

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijece@iaesjournal.com.-----------------------------------------------------------------------The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average.
A current control method for bidirectional multiphase DC-DC boost-buck converter Gifari Iswandi Hasyim; Sulistyo Wijanarko; Jihad Furqani; Arwindra Rizqiawan; Pekik Argo Dahono
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2363-2377

Abstract

In the future, more and more electric vehicle (EV) batteries are connected to the direct current (DC) microgrid. Depending on the battery state of charge, the battery voltage can be higher or lower than the DC microgrid voltage. A converter that is aimed to fulfil such function must be capable of working in both charging and discharging regardless the voltage level of the battery and DC microgrid. Battery performance degradation due to ripple current entering the battery is also a concern. In this paper, a converter that can minimize ripple current that entering battery and operate in two power-flow directions regardless of battery and DC microgrid voltage level is presented. A current control method for this kind of converter was proposed. Experiment on a prototype was conducted to prove the proposed converter current control method.

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