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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 64 Documents
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Search results for , issue "Vol 33, No 3: March 2024" : 64 Documents clear
Interoperability of Botswana’s healthcare systems using semantic prescription ontologies Eunice Chinatu Okon; Tshiamo Sigwele; Galani Malatsi; Tshepiso Mokgetse; Hlomani Hlomani
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1782-1792

Abstract

The developing country of Botswana’s health information system faces interoperability challenges mainly due to the lack of shared patient medical data and histories between private and public healthcare providers, which leads to increased medical errors, increased healthcare costs, and potentially fatal outcomes. This research proposes an intelligent electronic prescription ontology (IEPO) framework to share Botswana’s patient electronic health records (EHRs) between private and public healthcare systems for a standardized and semantically rich data exchange. IEPO was evaluated for interoperability using the recall metric for completeness to capture the degree of all relevant information for exchange and the precision metric for accuracy performance to gauge the degree of error minimization during interoperability. The harmonic means of precision and recall called the F1- score, offered the overall interoperability performance. IEPO outperformed related works by 75% in recall, 54% in precision, and 76% in F1-score, demonstrating improved interoperability performance. Furthermore, IEPO was evaluated for correctness and expressiveness through competency questions via queries, results confirming correct and expressive responses.
Detecting attacks on e-mail Yujia Fang; Gabriela Mogos
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1576-1588

Abstract

E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Na¨ıve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored.
An improved post-hurricane building damaged detection method based on transfer learning Guangxing Wang; Seong-Yoon Shin; Gwanghyun Jo
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1546-1556

Abstract

After a natural disaster, it is very important for the government to conduct a damaged assessment as soon as possible. Fast and accurate disaster assessment helps the government disaster relief departments allocate resources and respond quickly and effectively to minimize the losses caused by the disaster. Usually, the method of measuring disaster losses is to rely on manual field exploration and measurement, and then calculate and label the damaged buildings or land, or rely on unmanned collections to remotely collect pictures of the disaster-stricken area, and compare the original pictures to carry out the disaster annotation and calculation. These methods are time-consuming, labor-intensive, and inefficient. This paper proposes a post-hurricane building damage detection method based on transfer learning, which uses deep learning image classification algorithms to achieve post-disaster satellite image damage detection and classification, thereby improving disaster assessment efficiency and preparing for disaster relief and post-disaster reconstruction. The proposed method adopts the theory of transfer learning, establishes a disaster image detection model based on the convolutional neural network model, and uses the 2017 Hurricane Harvey data as the experimental data set. Experiments have proved that our proposed model accuracy of disaster detection reaches 97%, which is 1% higher than other models.
A scoping review of artificial intelligence-based robot therapy for children with disabilities Rusnani Yahya; Rozita Jailani; Fazah Akhtar Hanapiah; Nur Khalidah Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1855-1865

Abstract

The integration of artificial intelligence (AI)-based robot therapy (AIBRT) has become prominent in addressing the needs of children with disabilities, including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), learning disabilities, and speech delays. However, questions arise regarding the effectiveness of different AI techniques in enhancing therapy for children with specific needs. This review explores current literature on AIBRT for children with disabilities, aiming to understand the efficacy and potential of various AI techniques in improving their therapy. This paper presents a comprehensive search of research articles published from 2019 to September 2023. 39 articles focusing on AI-based robot platforms, the employed treatment or therapy methods, assessment procedures during therapy, and the variables or parameters used to measure intervention effectiveness have been discussed in detail. These AI-based robot platforms have been utilized to engage individuals diagnosed with ASD, offering therapeutic interventions and assessments. In conclusion, the integration of AI and robotics in therapy shows promise for enhancing the development and quality of life for children with disabilities. The findings of this review have implications for therapists, practitioners, and researchers interested in incorporating AI applications into therapy practices. This integration can lead to improved therapy outcomes, optimized children’s development, and enhanced quality of life.
One level deep convolutional neural network for facial key points detection Abdelaali Benaiss; Rachid El Ayachi; Mohamed Biniz; Mustapha Oujaoura
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1694-1704

Abstract

Facial landmark detection has a lot of applications in face recognition, face alignment, facial expression recognition, video surveillance and security systems. In the existing literature, there are multiple methods utilizing convolutional neural networks (CNNs) that address this problem in various ways. In many cases, the models use a tree-like structure of CNNs to achieve better results. This paper proposes a combination of three parallel deep convolutional neural networks (DCNNs) to estimate the accurate localization of each keypoint. The first one focuses on the whole face to outperform five points, including the eyes, nose, and mouth corners. The second one focuses on the eyes-nose parts to outperform three points, specifically the eyes and nose. The last one focuses on the nose-mouth parts to outperform three points, namely the nose and mouth corners. Further, we combine all outputs of the three DCNNs and take the average value of each detected key point as the final output. In the first step, we improvthe the parameter efficiency and accuracy of each DCNNs through a set of experiments using the labeled face parts in-the-wild database (LFPW) and the helen facial feature dataset (Helen). Then, we demonstrate that our approach yields more accurate estimations of facial key points than two state-of-the-art methods and commercial software in terms of accuracy.
Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning Valavan Woothukadu Thirumaran; Nalini Joseph; Umarani Srikanth
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1829-1842

Abstract

Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.
A novel approach for detecting sensor-based semiconductor fault yield classification using convolutional neural networks Mohammed Altaf Ahmed; Suleman Alnatheer
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1448-1464

Abstract

In the proposed research, data from the semiconductor industry is considered for analysis. In this research, there is a requirement for significantly more space for storage, processing will take significantly more time, and there will be a significant amount of duplicate data. So, the utilization of dimensionality reduction strategies is required so as to lessen the number of spectral bands while maintaining the maximum amount of relevant information. Our contribution can be broken down into two parts: To begin, we suggest a filter-based technique that we call interband redundancy analysis (IBRA). This method is based on a collinearity analysis that is performed among a band and its neighbors. By performing the given research, redundant bands can be omitted, which in turn significantly brings down the search space. Next, we take the findings of the IBRA and use a wrapper-based technique known as greedy spectral selection (GSS) to choose bands on the basis of the information entropy values of those bands. We are later training a convolutional neural network to evaluate how well the present selection is working. We also propose an optimization algorithm for performance enhancement known as bacterial foraging optimization.
Ethical hacking: real evaluation model of brute force attacks in password cracking Buthayna Al Sharaa; Saed Thuneibat
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1653-1659

Abstract

Despite ongoing efforts to convince users of the value of password security and to enforce password creation standards on them, in many information systems the human factor still plays a role. In addition, not only do most users’ password creation and management practices largely remain unchanged, but password cracking tools and more critically, computer hardware also continue to advance. In this paper we present a model in ethical hacking; the proposed model concentrated on brute force attacks for password cracking. The main novelty of our work is that it first presents a mathematical model that calculates the number of different password permutations of varying lengths. Then the brute force attack is modelled using the Markov chain model and a method is developed to formulate the conventional optimization problem, which is classified as a discrete nonlinear problem. The experiments’ results demonstrate and validate the method’s effectiveness and suitability.
Improving web-oriented information systems efficiency using Redis caching mechanisms Maksim Vladimirovich Privalov; Mariya Valerevna Stupina
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1667-1675

Abstract

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.
Non-linear control for enhanced solar power under partial shading and AC load variations Sabri Khadija; El Maguiri Ouadia; Farchi Abdelmajid
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1347-1362

Abstract

This paper solves the control problem to track the maximum power in grid-connected PV systems to catch up with the changes and meet the energy demand, given the irregular and arbitrary nature of the solar source. Our work addresses the following objectives: i) extracting the maximum available power under partial shading, ii) and having a unit power factor. To achieve the above objectives, we have integrated two control components. The first one is dedicated to the extraction of the maximum power point (MPPT) particle swarming algorithm (PSO) with a backstepping controller, by shaking on the DC/DC converter duty cycle to increase the robustness and stability of the system. The backstepping control of the three-phase voltage source inverter is the second part. To verify the effectiveness of the introduced system, modelling and simulation are verified in MATLAB/Simulink.

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