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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand Supanee Sengsri; Kheamparit Khunratchasana
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp299-304

Abstract

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period.
A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images Usman Haruna; Rozniza Ali; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp369-377

Abstract

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases.
PID based on a single artificial neural network algorithm for DC-DC boost converter Dhaouadi Guiza; Djamel Ounnas; Soufi Youcef; Abdelmalek Bouden
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp160-169

Abstract

This research focuses on developing a proportional integral derivative controller based on a single artificial neural network (PID-SANN). The proposed control strategy drives the direct current (DC-DC) boost converter output voltage to follow the desired reference value. This controller calculates the PID gains via a learning algorithm based on an artificial single-neuron network, which overcomes the computational complexity of PID gains using analytical methods and automatically adjusts the controller parameters. The developed PID-SANN method offers the boost converter the appropriate duty ratio, which permits controlling the output voltage value despite fluctuations in the resistive load or input voltage. The obtained results confirm that the developed method can successfully surmount the constraints of conventional PID controllers and direct the output voltage of the considered DC-DC converter to follow the required value precisely.
Employing various topologies to improve the interleaved boost converters performance Areej Hamzah Makee; Ali Hussein Al-Omari
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp54-67

Abstract

In recent years, there has been a significant amount of literature concerning high step-up DC-DC converters. Whereas the voltage level needs to be raised to a higher value so that the DC conversion can meet the AC mains value. A boost converter is the most popular circuit in the field, but because of its drawbacks, employing it is limited. Interleaved boost converters have attracted considerable attention and are a promising solution for high-power step-up and power factor correction applications. To demonstrate the importance of this topology, a comparison has been made with single-switch converters. For this reason, this paper classifies the topologies into two main categories based on the number of main switching elements: Single-switch and interleaved boost converters. Then, each category was classified, including the conventional and the modified types, to cover the effect of adding voltage multiplier cells, coupled inductors, and a combination of coupled inductors and voltage multiplier cells. Each converter’s performance was evaluated by comparing the voltage gain of each unit. Also, this paper highlighted these converters’ essential characteristics, topological strategies, benefits, and demerits to clarify the differentiating solutions. Additionally, this work presents the difficulties or directions for developing novel topologies.
Underdetermined direction of arrival estimation for multiple input and multiple outputs sparse channel based on Bayesian learning framework Anughna Narayanaswamy; Ramesha Muniyappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp170-179

Abstract

Direction of arrival (DOA) estimation for a sparse channel has attracted serious attention recently. Better signal analysis and denoising achieve accuracy in DOA determination. This paper proposes an underdetermined DOA estimation for multiple input and multiple outputs (MIMO) sparse channels. A novel multi-kernel-based non-negative sparse Bayesian learning (MK NNSBL) framework is implemented using the multiplied form of basis vector within the manifold matrix for a defined grid. Meanwhile, virtual antenna locations are reconfigured by exploiting the conventional cuckoo search algorithm (CCSA) for the fine reception of incoming signals on a nonuniform linear array (NULA). The simulated results reveal that the novel approach outperforms in its optimal root mean square error (RMSE) for various signal-to-noise ratio (SNR) limits and the compilation time. The convergence comparative graph indicates the improved performance in the proposed framework over existing algorithms.
Behaviour based botnet detection with traffic analysis and flow intervals at the host level Sneha Padhiar; Ritesh Patel
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp350-358

Abstract

A botnet is one of the most dangerous forms of security issues. It infects unsecured computers and transmit malicious commands. By using botnet, the attacker can launch a variety of attacks, such as distributed denial of service (DDoS), data theft, and phishing. The botnet may contain a lot of infected hosts and its size is usually large. In this paper, we addressed the problem of botnet detection based on network’s flows records and activities in the host. We proposed a host-based approach that detects a host, that has been compromised by observing the flow of in-out bound traffic. To prove the existence of command and control communication, we examine host network flow. Once the bot process has been identified in the host being monitored, this knowledge allows blocking any in/out traffic with the bot’s server. In addition to providing information about the compromised machine’s IP address and how it communicates with servers, the log file is generated, which can provide data about the command and control (C&C) servers. Most existing work on detecting botnet is based on flow-based traffic analysis by mining their communication patterns. Our work distinguishes itself from other methods of bot detection from its ability to use real-time host-related data for detection.
A 28/38 GHz tuned reconfigurable antenna for 5G mobile communications Samar A. Refaat; Hesham A. Mohamed; Abdelhady M. Abdelhady; Ashraf S. Mohra
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp248-258

Abstract

In this paper, a compact tuned reconfigurable microstrip antenna for fifth generation (5G) mobile communications is designed to operate at 28 GHz or 38 GHz or both frequencies. The proposed antenna can be reconfigured by using a group of PIN diodes switches across a slit in the upper traditional patch antenna or through the ground plane side. The tuning between the 28 GHz and 38GHz frequency bands can be achieved through ON/OFF states of the PIN diodes switches. The tuned reconfigurable antenna is simulated using CST software package and then fabricated and measured. The simulated and measured results show good agreement with a little deviation. The proposed tuned antenna is small in size with 18×11.25 mm2 overall area.
Development of a design optimization algorithm for a bowtie antenna Aaron Don M. Africa; Rica Rizabel M. Tagabuhin; Jan Jayson Tirados
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp229-237

Abstract

A bowtie antenna is one of the simple dipole antennas with an omnidirectional pattern utilized in several applications. It is used in industrial applications, scientific applications, and medical applications. Its elementary design can be subjected to modification to expand the applications of the dipole and improve its performance. This paper aims to develop a design optimization algorithm for a bowtie antenna with an adaptive finite impulse response (FIR) filter. In the paper, the different designs of the bowtie antenna are simulated using MATLAB software. The design of antennas is constructed using the partial differential equation (PDE) toolbox in MATLAB software. The designs explored in the paper are the slotted microstrip bowtie antenna and the double flare bowtie antenna. A traditional bowtie was also simulated to be used as a reference for the evaluation of the modified antennas. The dimensions of the designs are kept closely like draw accurate conclusions about the effects of the refinements done. The effects of the modification of the designs on the directivity and return loss are determined to assess the effectiveness of the design alterations.
Bone osteosarcoma tumor classification Kamel Hussien Rahouma; Ahmed Salama Abdellatif
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp582-587

Abstract

Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy.
Visualization-based monitoring in early warning systems with wireless sensor networks Minh T. Nguyen; Cuong V. Nguyen; Huyen N. Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp281-289

Abstract

With the impact of global climate change, natural disasters such as prolonged drought, earthquakes, and tsunamis, have constantly increased over recent decades, putting those living in these areas in great danger. A natural disaster warning system has been established as an indispensable need to minimize possible high risks that cause human casualties. Several current natural disaster warning systems focus on building wireless sensor networks for forecasting and monitoring disasters as well as natural phenomena. This paper aims to develop a comprehensive model that integrates data visualization operations to identify and simultaneously predict threat proceedings in natural disasters. This technique can handle big data based on sensing data from wireless sensor networks and shows overview graphs about disasters' variability, floods, and earthquakes, in the areas. Based on the results collected from data visualization techniques, the system can issue alerts about the interest of the region in real time. In addition, we propose some levels for the warning system in which the networks only focus on the area with essential data that must be warned. This can save energy consumption for other areas of safety. This work shows promising points of effectiveness.

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