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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 113 Documents
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Search results for , issue "Vol 13, No 6: December 2023" : 113 Documents clear
Skin cancer classifier based on convolution residual neural network Ajel, Ahmed R.; Al-Dujaili, Ayad Qasim; Hadi, Zaid G.; Humaidi, Amjad Jaleel
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6240-6248

Abstract

Accurate automatic classification of skin lesion images is a great challenge as the image features are very close in these images. Convolution neural networks (CNN) promise to provide a potential classifier for skin lesions. This work will present dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. It presents a classifier with a single CNN to automatically recognize benign and malignant skin images. The network inputs are only disease labels and image pixels. About 320 clinical images of the different diseases have been used to train CNN. The model performance has been tested with untrained images from the two labels. This model identifies the most common skin cancers and can be updated with a new unlimited number of images. The DLCNN trained by the ResNet-50 model showed good classification of the benign and malignant skin categories. The ResNet-50 as a DLCNN has verified a significant recognition rate of more than 97% on the testing images, which proves that the benign and malignant lesion skin images are properly classified.
Two-link lower limb exoskeleton model control enhancement using computed torque Parikesit, Elang; Maneetham, Dechrit
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6204-6215

Abstract

Robotic technology has recently been used to help stroke patients with gait and balance rehabilitation. Rehabilitation robots such as gait trainers are designed to assist patients in systematic, repetitive training sessions to speed up their recovery from injuries. Several control algorithms are commonly used on exoskeletons, such as proportional, integral and derivative (PID) as linear control. However, linear control has several disadvantages when applied to the exoskeleton, which has the problem of uncertainties such as load and stiffness variations of the patient’s lower limb. To improve the lower limb exoskeleton for the gait trainer, the computed torque controller (CTC) is introduced as a control approach in this study. When the dynamic properties of the system are only partially known, the computed torque controller is an essential nonlinear controller. A mathematical model forms the foundation of this controller. The suggested control approach’s effectiveness is evaluated using a model or scaled-down variation of the method. The performance of the suggested calculated torque control technique is then evaluated and contrasted with that of the PID controller. Because of this, the PID controller’s steady-state error in the downward direction can reach 5.6%, but the CTC can lower it to 2.125%.
An efficient reconfigurable optimal source detection and beam allocation algorithm for signal subspace factorization Thazeen, Sadiya; Srikantaswamy, Mallikarjunaswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6452-6465

Abstract

Now a days, huge amount of data is communicated through channels in wireless network. It requires an efficient parallel operation for the optimal utilization of frequency, time allocation and coding model for signal subspace factorization in smart antenna. In view of this requirement, an efficient reconfigurable optimal source detection and beam allocation algorithm (RoSDBA) is proposed. The proposed algorithm is able to allocate desired signal to the user space to reduce the noise and also for efficient allocation of subspace to remove disturbance in all directions. The proposed method efficiently utilizes the antenna array elements by accurate identification and allocation of antenna array elements such as individual radiators, radiation beam, signal strength, and disturbance factor. With respect to simulation analysis, the proposed method shows better performance for the resolution, radiation beam allocations, identification bias, distribution factor and time taken for the detection of various array arrangements and source numbers.
Assessment of the main features of the model of dissemination of information in social networks Imanberdi, Assel; Lira, La; Aitolkyn, Kulmuratova; Leila, Rzayeva; Abitova, Gulnara; Aigerim, Bakiyeva; Ainur, Orynbayeva; Assem, Baimakhanbetova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6729-6736

Abstract

Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter Malathi, Venkatesan; Manikandan, Arumugam
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6361-6368

Abstract

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.
Image noise reduction by deep learning methods Uzakkyzy, Nurgul; Ismailova, Aisulu; Ayazbaev, Talgatbek; Beldeubayeva, Zhanar; Kodanova, Shynar; Utenova, Balbupe; Satybaldiyeva, Aizhan; Kaldarova, Mira
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6855-6861

Abstract

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
Improved autocorrelation method for time synchronization in filtered orthogonal frequency division multiplexing Suyoto, Suyoto; Subekti, Agus; Satyawan, Arief Suryadi; Marta Dinata, Mochamad Mardi; Mitayani, Arumjeni; Adhi, Purwoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6538-6546

Abstract

Time synchronization is essential in multicarrier systems such as filtered orthogonal frequency division multiplexing (F-OFDM) because it determines the whole system’s performance. Differ with OFDM, where subcarrier allocation is not flexible. In F-OFDM, the subcarrier allocation is more flexible, and the whole subcarrier in one symbol can be grouped into several subbands. The use of subcarriers that are limited to only one subband can reduce the performance of time synchronization based on autocorrelation (AC) methods. In this study, we first compare the performance of the AC-based time synchronization algorithms used in F-OFDM when training symbols are limited to one subband. Secondly, we made improvements to the AC-based time synchronization with the averaging technique of its timing metric, thus increasing the accuracy of time estimates in the F-OFDM system. The averaging technique of the timing metric improved the performance of the AC method in cases where the training symbol is limited to one subband, as shown in the simulation results.
Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework Salih, Ahmad Mahdi; Syed-Mohamad, Sharifah Mashita; Keikhosrokiani, Pantea; Samsudin, Nur Hana
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6701-6717

Abstract

DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT.
A large-scale sentiment analysis using political tweets Tun, Yin Min; Khaing, Myo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6913-6925

Abstract

Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.
Influence analysis of director’s elements on the circular Yagi disc antenna performance at 1.8 GHz Shamsudin, Mustaqim Hakimi; Ibrahim, Imran Mohd; Al-Gburi, Ahmed Jamal Abdullah; Purnamirza, Teddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6426-6434

Abstract

This paper aims to investigate and design a Yagi disc antenna with a variable number of director elements for Band 3 in fourth-generation long term evolution (4G LTE) mobile applications. The array technique was introduced by increasing the number of director elements to achieve superior results and better performance, such as higher gain and lower return loss. Initially, the simulated results of return loss and gain with one director element were -19.02 dB and 8.51 dBi, respectively. Then, by increasing the number of directors to three and five elements, the antenna’s performance improved significantly from -32.44 to -42.68 dB for return loss and from 8.51 to 11.17 dBi for gain, respectively. The simulated circular Yagi disc antenna provided a response in the range of 1.78 to 1.82 GHz. Therefore, a model was fabricated and tested to validate the antenna design. The measured results matched well with the simulated ones. By increasing the number of director elements, the measurement results of gain and return loss at a frequency of 1.8 GHz also showed improvement from 7.70 to 11.09 dBi and from -27.31 to -32.91 dB, respectively. Meanwhile, the measured antenna provided a wider bandwidth in the range of 1.72-1.82 GHz.

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