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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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Query expansion using novel use case scenario relationship for finding feature location Achmad Arwan; Siti Rochimah; Chastine Fatichah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5501-5516

Abstract

Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
Convolutional auto-encoded extreme learning machine for incremental learning of heterogeneous images Sathya Madhusudhanan; Suresh Jaganathan; Dattuluri Venkatavara Prasad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5853-5864

Abstract

In real-world scenarios, a system's continual updating of learning knowledge becomes more critical as the data grows faster, producing vast volumes of data. Moreover, the learning process becomes complex when the data features become varied due to the addition or deletion of classes. In such cases, the generated model should learn effectively. Incremental learning refers to the learning of data which constantly arrives over time. This learning requires continuous model adaptation but with limited memory resources without sacrificing model accuracy. In this paper, we proposed a straightforward knowledge transfer algorithm (convolutional auto-encoded extreme learning machine (CAE-ELM)) implemented through the incremental learning methodology for the task of supervised classification using an extreme learning machine (ELM). Incremental learning is achieved by creating an individual train model for each set of homogeneous data and incorporating the knowledge transfer among the models without sacrificing accuracy with minimal memory resources. In CAE-ELM, convolutional neural network (CNN) extracts the features, stacked autoencoder (SAE) reduces the size, and ELM learns and classifies the images. Our proposed algorithm is implemented and experimented on various standard datasets: MNIST, ORL, JAFFE, FERET and Caltech. The results show a positive sign of the correctness of the proposed algorithm.
Random forest model for forecasting vegetable prices: a case study in Nakhon Si Thammarat Province, Thailand Sopee Kaewchada; Somporn Ruang-On; Uthai Kuhapong; Kritaphat Songsri-in
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5265-5272

Abstract

The objectives of this research were developing a model for forecasting vegetable prices in Nakhon Si Thammarat Province using random forest and comparing the forecast results of different crops. The information used in this paper were monthly climate data and average monthly vegetable prices collected between 2011 – 2020 from Nakhon Si Thammarat meteorological station and Nakhon Si Thammarat Provincial Commercial Office, respectively. We evaluated model performance based on mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE). The experimental results showed that the random forest model was able to predict the prices of vegetables, including pumpkin, eggplant, and lentils with high accuracy with MAPE values of 0.09, 0.07, and 0.15, with RMSE values of 1.82, 1.46, and 2.33, and with MAE values of 3.32, 2.15, and 5.42, respectively. The forecast model derived from this research can be beneficial for vegetable planting planning in the Pak Phanang River Basin of Nakhon Si Thammarat Province, Thailand.
An ensemble model to detect packet length covert channels Muawia A. Elsadig; Ahmed Gafar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5296-5304

Abstract

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.
Performance analysis of smart optimization antenna for wireless networks Jacob Abraham; Kannadhasan Suriyan; Beulah Jackson; Mahendran Natarajan; Thanga Mariappan Lakshmanaperumal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5222-5231

Abstract

Antenna design has significantly advanced as a result of the widespread need for wireless communications and data substitution through wireless devices. The research article's goal is to provide a conceptual framework, difficulties, and opportunities for a source as well as a general overview of the antenna used in wireless communications applications. In this proposed research, we will go over a variety of topics related to mobile communication and fifth generation (5G) technologies, including its pros and benefits. A thorough comparison between the expected properties of the antennas and each generation, from 1st generation (1G) to 5G, is also included. This article also provides an overview of the investigated 5G technologies and various antenna designs.
Comprehensive study: machine learning approaches for COVID-19 diagnosis Amir Nasir Hussein; Seyed Vahab Al-Din Makki; Ali Al-Sabbagh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5681-5695

Abstract

Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.
Sensor fault reconstruction for wind turbine benchmark model using a modified sliding mode observer Mohammed Taouil; Abdelghani El Ougli; Belkassem Tidhaf; Hafida Zrouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5066-5075

Abstract

This paper proposes a fault diagnosis scheme applied to a wind turbine system. The technique used is based on a modified sliding mode observer (SMO), which permits the reconstruction of actuator and sensor faults. A wind turbine benchmark with a real sequence of wind speed is exploited to validate the proposed fault detection and diagnosis scheme. Rotor speed, generator speed, blade pitch angle, and generator torque have different orders of magnitude. As a result, the dedicated sensors are susceptible to faults of quite varying magnitudes, and estimating simultaneous sensor faults with accuracy using a classical SMO is difficult. To address this issue, some modifications are made to the classic SMO. In order to test the efficiency of the modified SMO, several sensor fault scenarios have been simulated, first in the case of separate faults and then in the case of simultaneous faults. The simulation results show that the sensor faults are isolated, detected, and reconstructed accurately in the case of separate faults. In the case of simultaneous faults, with the proposed modification of SMO, the faults are precisely isolated, detected, and reconstructed, even though they have quite different amplitudes; thus, the relative gap does not exceed 0.08% for the generator speed sensor fault.
New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system Ali Tarraq; Faissal El Mariami; Abdelaziz Belfqih
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4909-4918

Abstract

This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
IPv6 flood attack detection based on epsilon greedy optimized Q learning in single board computer April Firman Daru; Kristoko Dwi Hartomo; Hindriyanto Dwi Purnomo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5782-5791

Abstract

Internet of things is a technology that allows communication between devices within a network. Since this technology depends on a network to communicate, the vulnerability of the exposed devices increased significantly. Furthermore, the use of internet protocol version 6 (IPv6) as the successor to internet protocol version 4 (IPv4) as a communication protocol constituted a significant problem for the network. Hence, this protocol was exploitable for flooding attacks in the IPv6 network. As a countermeasure against the flood, this study designed an IPv6 flood attack detection by using epsilon greedy optimized Q learning algorithm. According to the evaluation, the agent with epsilon 0.1 could reach 98% of accuracy and 11,550 rewards compared to the other agents. When compared to control models, the agent is also the most accurate compared to other algorithms followed by neural network (NN), K-nearest neighbors (KNN), decision tree (DT), naive Bayes (NB), and support vector machine (SVM). Besides that, the agent used more than 99% of a single central processing unit (CPU). Hence, the agent will not hinder internet of things (IoT) devices with multiple processors. Thus, we concluded that the proposed agent has high accuracy and feasibility in a single board computer (SBC).
An approach of ontology and knowledge base for railway maintenance Zaynabe Ragala; Asmaâ Retbi; Samir Bennani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5282-5295

Abstract

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi.

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