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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 28, No 2: November 2022" : 64 Documents clear
Machine learning ensemble approach for healthcare data analytics Deepali Pankaj Javale; Sharmishta Suhas Desai
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp926-933

Abstract

In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level. Often in healthcare it is observed that the dataset is imbalanced. Therefore an Ensemble Approach using imbalanced dataset techniques Synthetic Minority Over-sampling Technique and Adaptive Synthetic oversampling methods with different evaluation methods like train-test, k-fold, Stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes and Logistic Regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and Random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and Random Forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and Random forest had least average train-test time. Thus the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.
Medical diagnostic support system based on breast thermography using Raspberry Pi and cloud computing Nabil Karim Chebbah; Mohamed Ouslim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp787-792

Abstract

Breast thermography is a promising medical imaging technique for the detection of breast cancer. However, providing a robust and portable computer-aided diagnostic system for breast thermography remains a tedious task. In this paper, a computer-aided diagnostic system based on breast thermography is developed and implemented on a Raspberry Pi 4 using the cloud computing services to provide the computing power needed for machine learning algorithms. Image processing techniques such as pre-processing and segmentation are employed to achieve an adequate feature extraction task. The Support Vector Machine classifier is used in the final stage to classify the breast as normal or abnormal. According to the experimental results, the proposed computer-aided diagnostic system has shown high performance in both the segmentation and classification steps. Furthermore, a low computation time was obtained when using the high computing capabilities of the cloud with the Raspberry Pi. We conclude that the implementation of such a decision support system on the Raspberry Pi especially when using the cloud computing services, can be a reliable tool for radiologists to predict breast abnormalities even in the rural backcountry where there is lack of health services.
Virtual tools in distance education: university satisfaction regarding its application as part of teaching strategies Guillermo Morales-Romero; José Antonio Arévalo-Tuesta; Lilia Rodas-Camacho; Elizabeth Auqui-Ramos; Carlos Palacios-Huaraca; César Trujillo-Hinojosa; Elvira Caceres-Cayllahua
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1049-1057

Abstract

When virtual education was implemented in Peru, the limitations of teachers in technological management were evident. For this reason, the research seeks to analyze the perception of university satisfaction regarding the use of virtual teacher tools as part of teaching strategies, in order to improve virtual teaching-learning, achieving student motivation and facilitating this meaningful learning through the use of virtual tools. The method used according to the investigative approach is qualitative, according to its scope it is descriptive and correlational. During the development of the research, it was identified that the satisfaction regarding the use of virtual tools by the teacher is focused on the critical, constructive and positive attitude towards virtual tools and in the acquittal of students' questions regarding the use of virtual tools. On the other hand, the indicator that is related to low student satisfaction focuses on the low diversity of methodological strategies used for the development of virtual learning sessions. Likewise, the Chi-square test shows the significant relationship between the perception of the teacher's competences regarding the use of virtual tools and the perception of the quality of the teaching offered to students during distance education.
Kelulut honey-filled pots detection using image processing based techniques Wan Nur Azhani W. Samsudin; Mohd Harizan Zul; Mohd Zamri Ibrahim; Rohana Abdul Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1028-1036

Abstract

Kelulut bee is one of the stingless bee species in Malaysia, which is not dangerous to human. Honey from Kelulut bee can be used for the treatment of a variety of illness. The awareness of honey nutrition in our health makes it received high demands from the consumers. Traditionally, beekeepers did the manual inspection to check the honey-filled pots by using the straw or needle. The high demand from the consumers and the greater size of Kelulut beehive make it impractical to check manually all the honeypots which are time-consuming. The hygiene of the collected honey is also important to produce a good quality of honey. Hence, an automated honey-filled pots detection system is proposed to overcome these limitations. The proposed system will reduce the time consuming and less prone to error of the wrong estimation of honey-filled pots. MATLAB software is used to process the image of the Kelulut beehive which is challenging due to the overlapped honeypots in the image. Using the proposed algorithm, it can detect whether the pots filled with honey or not by using image processing techniques and it will analyse the image which represents the percentage amount of honey in the beehives.
A unique deep-learning-based model with chest x-ray image for diagnosing COVID-19 Alyaa Mahdi Al-khafagy; Sarah Rafil Hashim; Rusul Ali Enad
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1147-1154

Abstract

Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse Transcription- Polymerase Chain Reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and Reverse Transcription- Polymerase Chain Reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5 %. The proposed strategy appears excellent in terms of accuracy when compared with later strategies.
Internet of things search engines: toward a general architecture Fatima Zahra Fagroud; Lahbib Ajallouda; El Habib Ben Lahmar; Ahmed Zellou; Hicham Toumi; Sanaa El Filali; Youssef Baddi
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1117-1127

Abstract

Internet of things (IoT) represents one of the main data-producing fields on the Internet, given the diversity and growth of smart objects around the world. The advancement of IoT paradigm and the variety of IoT special characteristics present major challenges for IoT search, which attract a great importance by industrials and researchers. Until now, a good deal of research has been focused on the development and implementation of IoT search solutions and tools, though there are still many issues, which must be studied and solved. This paper is interested to IoT search issue and tries to give a guideline to researchers interested in this issue as well as the proposal of a new general architecture for internet of things search engines. The article presents the concept of IoT search engines, a study of various existing solutions and the proposal of a new architecture which is based on 3 components and which respects the various requirements of IoT search engines. The results of this work are prominent as well as they will help researchers to identify future research directions.
Speech-based gender recognition using linear prediction and mel-frequency cepstral coefficients Yusnita Mohd Ali; Emilia Noorsal; Nor Fadzilah Mokhtar; Siti Zubaidah Md Saad; Mohd Hanapiah Abdullah; Lim Chee Chin
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp753-761

Abstract

Gender discrimination and awareness are essentially practiced in social, education, workplace, and economic sectors across the globe. A person manifests this attribute naturally in gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic in speech recognition systems that can be found in many speech technology applications. However, retrieving salient gender-related information from a speech signal is a challenging problem since speech contains abundant information apart from gender. The paper intends to compare the performance of human vocal tract-based model i.e., linear prediction coefficients (LPC) and human auditory-based model i.e., Mel-frequency cepstral coefficients (MFCC) which are popularly used in other speech recognition tasks by experimentation of optimal feature parameters and classifier’s parameters. The audio data used in this study was obtained from 93 speakers uttering selected words with different vowels. The two feature vectors were tested using two classification algorithms namely, discriminant analysis (DA) and artificial neural network (ANN). Although the experimental results were promising using both feature parameters, the best overall accuracy rate of 97.07% was recorded using MFCC-ANN techniques with almost equal performance for male and female classes.
Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output Delson Therambath Rajanbabu; Iven Jose
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp830-839

Abstract

Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document.
Preservation of intangible and tangible cultural heritage using digital technology Lanto Ningrayati Amali; Muhammad Rifai Katili; Wandi Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp980-986

Abstract

There is presently a shortage of preservation of intangible cultural heritage and places for distributing tangible cultural heritage artifacts, regardless of their high value and usability for a nation. Despite efforts to protect cultural heritage, such as mapping and designing information systems to ensure the authenticity of information circulating in the community about intangible traditions and tangible sites obtained from different sources, many historical information places have been converted into new beliefs and buildings. Therefore, this research aims to provide information to promote public awareness about the distribution of tangible sites and intangible information about cultural heritage. A system development method with a prototype model comprising the stages of design and evaluation, system coding, and program testing, alongside system evaluation and usage, was employed. Subsequently, the results showed that the mapping information system increases the effectiveness and efficiency of delivering intangible and tangible cultural heritage information to the public and tourists.
Positive-sequence virtual-flux control of grid-connected converter during unsymmetrical voltage dips Francis Mulolani; Matthew Armstrong; Mohammed Elgendy; Ahmed Althobaiti
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp700-709

Abstract

One of the major problems in direct power-controlled grid-connected voltage source converters is that during voltage dips, the converter current increases to compensate for the reduced grid voltage. The most common voltage dips are unsymmetrical, and they cause unbalance and distortion in the converter current. This paper introduces a new, simple but effective algorithm which limits the current in a direct power-controlled grid-connected voltage source converter during voltage dips. A positive-sequence virtual-flux based control scheme is employed and this makes the current balanced and sinusoidal during unsymmetrical voltage dips. The proposed control clearly demonstrates the performance which is illustrated through various simulations and experimental work. The current during unsymmetrical voltage dips is limited in magnitude and is balanced, with low distortion. The Implementation of this control scheme will enable voltage source converters to stay connected to the grid during voltage dips as required by most grid codes.

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