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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 62 Documents
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Search results for , issue "Vol 34, No 1: April 2024" : 62 Documents clear
Syntactic analysis of complex sentences containing Arabic psychological verbs Asmaa Amzali; Asmaa Kourtin; Mohammed Mourchid; Abdelaziz Mouloudi; Samir Mbarki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp312-321

Abstract

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.
Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose Dava Aulia; Riyanarto Sarno; Shintami Chusnul Hidayati; Alfian Nur Rosyid; Muhammad Rivai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp264-275

Abstract

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.
An optimal model for detection of lung cancer using convolutional neural network Kavitha Belegere Chandraiah; Naveen Kalenahalli Bhoganna
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp134-143

Abstract

In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data.
Fuzzy logic controller-based Luo converter for light electric vehicles Rangaswamy Balamurugan; Komarapalayam Subramaniam Vairavel; Marimuthu Kalimuthu; Veerappan Kiruthiga Devi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp641-646

Abstract

A brushless DC motor (BLDC) drive fed by a Luo converter is presented in this paper for the use in electric vehicle (EV) applications. The proposed Luo converter provides stable and ripple free output for EV. An approach for getting the stable output voltage is by using a fuzzy logic controller to control the Luo converter. It helps to generate the appropriate pulse with respect to the feedback voltage. This proposed system has the advantages like voltage increase, high-gain output with low ripples and high efficiency. The performance of this proposed drive is tested through hardware prototype at varying line voltage levels and results are demonstrated. A comparative analysis is presented to justify the effectiveness of the proposed Luo converter fed EV motor.
Cultivating excellence: a case study of enterprise architecture transformational journey in higher education Fatimah Azzaharah Amin; Surya Sumarni Hussein; Nor Aziah Daud; Nur Azaliah Abu Bakar; Wan Azlin Zurita Wan Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp548-555

Abstract

An enterprise architecture (EA) is a critical framework that clarifies the complex elements of organizations, including business processes and technology. It coordinates the interaction of these elements to achieve predetermined business goals. Higher education institutions (HEIs) may become susceptible to rigidity, duplication, and intricate operations without a comprehensive EA. The primary aims of this study are to ascertain the necessary information for each EA domain: business, data, and application at Universiti Teknologi MARA (UiTM) and to design the landscape map viewpoint architecture for UiTM by utilizing the EA framework. This research uses qualitative methods, namely interviews and document analysis, to comprehensively comprehend the intricacies and prerequisites within every EA domain. Critical insights for each UiTM domain: business, data, application, and technology were uncovered through the discernments. From the thematic analysis, the frequency of issues according to the EA domain is business 13 issues, application eight issues, data seven issues, and technology seven issues. Following a thorough analysis, these findings led to the development of the landscape map viewpoint architecture diagram. In conclusion, the results of this research hold the potential to provide HEIs with invaluable knowledge for enhancing their organizational transformation via EA, thereby propelling the overall quality and efficacy of higher education systems forward.
Exploring user satisfaction and improvement opportunities in public charging pile resources Licheng Xu; Asmiza A. Sani; Shuai Xie; Liyana Shuib
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp482-496

Abstract

The existing market of public charging pile services for electric vehicle (EV) users has occupied a particular market share. However, instead of solely focusing on pre-planning the construction of charging piles, it is crucial to address the shortcomings of the existing charging pile service and develop effective marketing strategies. This approach can help optimize the utilization of charging pile resources and minimize wastage. In this study, we explore EV users’ comments on the public charging pile service and adopt a natural language pre-training model to classify comments for extracting positive and negative comments. For these two types of comments respectively, we construct the text-to-knowledge to mine the keywords from multiple dimensions. We further excavate the words correlated with the keywords by utilizing dependency parsing to create relational dependency graphs. Taken together, we identify key factors influencing EV user satisfaction or dissatisfaction and uncover the relationships among these factors. These insights provide valuable information for charging pile operators to develop targeted marketing strategies and improvement plans for the existing public charging pile resources, ultimately enhancing the overall user experience.
A smart emergency response system based on deep learning and Kalman filter: the case of COVID-19 Hounaida Frikha; Ferdaous Kamoun-Abid; Amel Meddeb-Makhoulf; Faouzi Zarai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp630-640

Abstract

During an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19.
Uplink-downlink resource optimization for provisioning 5G application considering SUI fading channel models Vanita Kaba; Rajendra R. Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp350-361

Abstract

Recently, superposition coding (SC), Stanford University Interim (SUI) fading channel, user clustering, successive interference cancellation (SIC) mechanism has been incorporated into the power allocation design for attaining good performance. Nonetheless, the existing model works with cluster size up to 4 user per cluster and induce higher control channel overhead. In addressing this paper introduce an effective resource allocation (ERA) design for power domain non-orthogonal multiple access (PD-NOMA) system. The ERA, first employ Zadoff Chu (ZC) coding, then power is allocated according to the distance considering 5 user per cluster. Finally, multi-level SIC is done at the receiver end. The ERA performance is studied by varying signal-to-noise ratio (SNR) in terms of bit error rate (BER) considering a maximum of five users per cluster. Further, the ERA performance in terms of BER is tested under different scenario defined in SUI propagation scenario such as higher pathloss, moderate path loss, and low path loss. The throughput performance under varied density and speed is also studied; the ERA achieves much better performance in terms of ERA, SNR and throughput than standard multi-user downlink resource allocation (MUDRA) model.
Performance evaluation of adaptive offloading model using hybrid machine learning and statistic prediction Siwoo Byun; Seok-Woo Jang; Joonho Byun
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp463-471

Abstract

We introduce fast sensor diagnosis and focuses on intelligent offloading skills to enhance the sensor data screening efficiency. This study proposed the adaptive offloading model based on statistics-based prediction feedback and sensor candidate filtering. For the statistics-based filtering, sliding sensor grids and compounded sensor context were devised. This study also proposed hybrid prediction model using support vector machine (SVM) and k-nearest neighbors (KNN) machine training for the adaptive offloading. Therefore, the sensor information that is highly likely to be the cause of the actual device faults can be selected and transmitted, resulting in improved offloading performance. The test results through Google Colab show that the fault prediction accuracy of proposed models is 95%.
Development of biomechanical behaviour of magnesium alloys for biomedical context Hamza Abu Owida; Feras Alnaimat; Bassam Al-Naami; Jamal Al-Nabulsi; Nidal Turab
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp98-108

Abstract

Magnesium alloys, which belong to the category of biodegradable metals, have a significant amount of potential to be utilized as implant materials, and as a result, they draw a lot of attention. This article is a review that summarizes the mechanical properties of magnesium alloys that are used in medical applications. This article illustrates the mechanical behaviors of magnesium alloys that are used in biomedical applications as well as the ways that may be used to improve the mechanical characteristics of biodegradable magnesium alloys. In conclusion, the difficulties that will need to be overcome in the creation of biodegradable magnesium alloys are discussed.

Page 6 of 7 | Total Record : 62

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