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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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Developing a trust model using graph and ranking trust of social messaging system Mostafa Heidarzadeh Kalahroudy; Kheirollah Rahsepar Fard; Yaghoob Farjami
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp997-1007

Abstract

Trust is an important issue in social interactions, especially in using cyberspace services. In this paper, a trust and evaluation model are proposed based on which the government can provide reliable services to users. The model is a distributed and hierarchical model. First, the number 12 trust criteria and the weight of these criteria were extracted using the analytical hierarchy process (AHP) and analytic network process (ANP) techniques. Second, to obtain the trust in the service examined, for each criterion, a graph of trusted entities is proposed. Then, a weighted graph with the number of trusted entities called trust pathways measure will be obtained. To test the model, the effect of the 12 criteria on three important evaluation factors over seven widely used social services was rated by three experts. The trust of each service was obtained, which was satisfactory as compared to a valid organizational evaluation. Finally, the correlation coefficient of this comparison was 70.37%, indicating that the results from this model were appropriate.
Comparison analysis of different classification methods of power quality disturbances Nur Adrinna Shafiqa Zakaria; Dalila Mat Said; Norzanah Rosmin; Nasarudin Ahmad; Mohamad Shazwan Shah Jamil; Sohrab Mirsaeidi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5754-5764

Abstract

Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.
Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure Nurshazwani Muhamad Mahfuz; Marina Yusoff; Zainura Idrus
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1048-1059

Abstract

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.
Advanced approach for encryption using advanced encryption standard with chaotic map Yahia Alemami; Mohamad Afendee Mohamed; Saleh Atiewi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1708-1723

Abstract

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text.
Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter Hendro Nurhadi; Mayga Kiki; Dieky Adzkiya; Teguh Herlambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5988-5997

Abstract

Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.
Involving machine learning techniques in heart disease diagnosis: a performance analysis Ban Salman Shukur; Maad M. Mijwil
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2177-2185

Abstract

Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.
Internet of things applications using Raspberry-Pi: a survey Khalid M. Hosny; Amal Magdi; Ahmad Salah; Osama El-Komy; Nabil A. Lashin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp902-910

Abstract

The internet of things (IoT) is the communication of everything with anything else, with the primary goal of data transfer over a network. Raspberry Pi, a low-cost computer device with minimal energy consumption is employed in IoT applications designed to accomplish many of the same tasks as a normal desktop computer. Raspberry Pi is a quad-core computer with parallel processing capabilities that may be used to speed up computations and processes. The Raspberry Pi is an extremely useful and promising technology that offers portability, parallelism, low cost, and low power consumption, making it ideal for IoT applications. In this article, the authors provide an overview of IoT and Raspberry Pi and research on IoT applications using Raspberry Pi in various fields, including transportation, agriculture, and medicine. This article will outline the details of several research publications on Raspberry Pi-based IoT applications.
3D printing part orientation optimization: discrete approximation of support volume Juan C. Guacheta Alba; Sebastian Gonzalez Garzon; Diego A. Nunez; Mauricio Mauledoux; Oscar F. Aviles
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5958-5966

Abstract

In three-dimensional (3D) printing, due to the geometry of most parts, it is necessary to use extra material to support the manufacturing process. This material must be discarded after printing, so its reduction is essential to minimize manufacturing time and cost. An important parameter that must be defined before starting the printing process is the part orientation, which has repercussions on the quality, deposition path, and post-processing among others. Usually, the user sets up this parameter arbitrarily, so this paper takes advantage of it on optimization techniques and proposes an approximation of the volume be covered by the support material, which depends directly on the angle of the part to be printed and its geometry. Among mono-objectives optimization strategies, this work focuses on five of them. Their performance is compared by two metrics: support volume and execution time. Then, the best result is compared with commercial software.
Optimizing Alzheimer's disease prediction using the nomadic people algorithm Shaymaa Taha Ahmed; Suhad Malallah Kadhem
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2052-2067

Abstract

The problem with using microarray technology to detect diseases is that not each is analytically necessary. The presence of non-essential gene data adds a computing load to the detection method. Therefore, the purpose of this study is to reduce the high-dimensional data size by determining the most critical genes involved in Alzheimer's disease progression. A study also aims to predict patients with a subset of genes that cause Alzheimer's disease. This paper uses feature selection techniques like information gain (IG) and a novel metaheuristic optimization technique based on a swarm’s algorithm derived from nomadic people’s behavior (NPO). This suggested method matches the structure of these individuals' lives movements and the search for new food sources. The method is mostly based on a multi-swarm method; there are several clans, each seeking the best foraging opportunities. Prediction is carried out after selecting the informative genes of the support vector machine (SVM), frequently used in a variety of prediction tasks. The accuracy of the prediction was used to evaluate the suggested system's performance. Its results indicate that the NPO algorithm with the SVM model returns high accuracy based on the gene subset from IG and NPO methods.
Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning Intisar AL-Shaeli; lsmail Sharhan Hburi; Ammar A. Majeed
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp493-501

Abstract

Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity.

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