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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 9,138 Documents
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Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs Yerlan Zaitin; Madina Mansurova; Murat Kunelbayev; Gulnur Tyulepberdinova; Talshyn Sarsembayeva; Adai Shomanov
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp518-529

Abstract

Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.
Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp222-237

Abstract

Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.
Modeling of web-based collaborative learning management system Youssef Lahmadi; Mohammed Ouadoud; Hasnae Mouzouri; Lahcen Oughdir
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1002-1009

Abstract

The challenges faced by most learning management systems (LMS) can be classified into two main areas: pedagogical and technical issues. A comprehensive exploration of these interrelated challenges provides valuable insights for developing a new and more effective model for LMS. In this paper, a novel conceptual model for a web-based collaborative LMS is introduced, merging two distinct learning theories: behaviorism and social constructivism. Through an analysis of the strengths and limitations of each theory, the study moves on to outline the fundamental principles and technical features of the proposed LMS model, which stems from this integration. In conclusion, the paper explores the implications of creating user-centered LMS solutions, with a specific focus on addressing the varied requirements of learners.
Design of routing protocol for enhancing quality of service in wireless ad hoc and sensor netw ork: LEQA Dawit Hadush Hailu; Berihu G. Gebrehaweria; Gebrehiwet Gebrekrstos Lema; Samrawit H. Kebede; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1589-1597

Abstract

Wireless sensor networks (WSNs) are now adopting mobile sensors due to their increased popularity in research and industry. To enhance WSNs' performance, mobility can be utilized to gather data. But, if the collector's route is fixed and movement is not manageable, current quality of service (QoS) strategies and protocols are ineffective in achieving timely data delivery while maintaining energy efficiency. In the real world, WSN networks use both actuator - actuator and sensor - actuator coordination. To conserve energy in communicati on tasks with heavy traffic and high volume, sensors/actuators can be relocated to desired locations. This study introduces a routing protocol that optimizes delivery latency and energy conservation in WSNs. The proposed latency, energy, and quality of ser vice aware (LEQA) protocol uses a cooperative approach to track the sink and coordinate communication between sensors and actuators. Each sensor schedules its time division multiple access (TDMA) to improve QoS metrics such as low energy consumption, low l atency, or packet loss. It also addresses sensor - actuator coordination and proposes a data communication protocol for efficient and fast communication with actuator nodes. This reduces energy consumption and minimizes latency.
Multilevel inverter: harmonic analysis with and without filters for RL load using SPWM techniques Champa Patanegere Nagarajappa; Abhay Anandarao Deshpande
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp756-767

Abstract

Multilevel inverter (MLI) gains more attraction compared to conventional inverter as it generates a staircase output voltage that mimics sine waves of desired output voltage. Cascaded H-bridge (CHB) topology is taken into consideration owing to several benefits over conventional inverters. Various levels of CHB (3 and 5 level) inverters are compared on diverse parameters. In this paper level shift sinusoidal pulse width modulation technique is considered resulting in reduced lower order harmonic (LOH) distortion and improve the quality of output current and voltage depending on the load. Since, the LOH are too dangerous for power electronic circuits. An attempt to shift all the LOH above 50th order depending on the modulation technique with analogy for selecting an appropriate switching frequency is highlighted. The effect of change in switching frequency and modulation index (MI) on RMS output voltage, % voltage total harmonic distortion (VTHD), output power factor with different modulation techniques such as phase disposition pulse width modulation (PsD PWM), phase opposite disposition (POD PWM), alternative phase opposition disposition (APOD PWM) is portrayed in the paper. Further, to boost the performance a unique filter circuits with optimal design values of Inductance and capacitance driven with IEEE 519-2022 standards. The effectiveness in terms of with and without filter is verified and validated using MATLAB.
Mobile design for medical care and minor emergencies applying telemedicine Misael Lazo-Amado; Laberiano Andrade-Arenas
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.pp1889-1902

Abstract

The large number of patients today in hospitals or clinics shows a total collapse in the medical areas, this is reflected by the lack of available specialists and the saturation of appointments to attend patients, taking minutes and hours to get to be attended, that is why it is proposed as an objective to design a mobile application for medical care and minor emergencies applying telemedicine offering the main processes of appointment registration, video calls, medical qualification, first aid, prescription management, and emergency calls. This mobile application is made with the Design Thinking methodology that will allow the team to find the main problems or risks of the patient and find a quick solution based on design. The results give response to the 150 patients showing their satisfaction of the proposed solution, also validation by experts is performed indicating a 97% acceptance and specialist doctors indicate satisfaction, accepting that the system is efficient. In conclusion, the applied objective is shown, finding the main problems and looking for quick solutions showing efficiency and satisfaction by patients, experts, and medical specialists.
Biomarkers of attention bias during public speaking anxiety Razak, Akmal; Feroz, Farah Shahnaz; Subramaniam, Siva Kumar; Shahbodin, Faaizah; Rajkumar, Sujatha
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp140-147

Abstract

The analysis of brain signals and their properties yields significant insights into the fundamental neural impairments associated with attention bias in individuals suffering from public speaking anxiety (PSA). This study aims to identify electroencephalogram (EEG) and performance biomarkers of attention bias in individuals with public speaking anxiety using the ex-Gaussian modeling technique, frontal alpha asymmetry (FAA) and delta-beta correlation (DBC). 12 subjects with high (H) PSA and 12 subjects with low (L) PSA performed the modified emotional stroop task. EEG data were captured using the low-cost 14-channel emotiv Epoc+. Results showed that the ex-Gaussian sigma was higher in the emotional condition in the high public speaking anxiety (HPSA) group, indicating attention bias. The study also found higher right FAA in HPSA compared to LPSA group. There was a negative correlation between σ and alpha power in the left region of the brain in the HPSA group, potentially related to attentional bias. Moreover, there was a notable trend towards significantly heightened DBC in the frontal and central regions of the brain among HPSA subjects. In conclusion, in biomedical engineering, the ex-Gaussian model, FAA and DBC are useful because they can identify EEG and performance biomarkers of attention bias in people with PSA.
Fetal electrocardiogram prediction using machine learning: a random forest-based approach mohammed moutaib; Mohammed Fattah; Yousef Farhaoui; Badraddine Aghoutane; Moulhime El Bekkali
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1076-1083

Abstract

Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.
Accuracy enhancement with artificial neural networks for bipolar disorder prediction Nisha Agnihotri; Sanjeev Kumar Prasad
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1695-1702

Abstract

The perfect physical health and mental wellbeing is an important aspect of human kind. Healthcare sectors involving machine learning and deep learning is providing good healthcare services is helping people for safeguarding them from being exploited with extra and unnecessary expenditures on medical check-ups. This gives treatments and many health services on time when needed. In this paper, different performance metrics are applied on online bipolar dataset named “Theory of mind in remitted bipolar disorder dataset” from Kaggle to evaluate the diagnosis for bipolar disorder feature prediction and analysis. In this study the proposed accuracy is better as compared to previous traditional models. As a result, artificial neural networks reduce the time taken in training and classification of dataset in prediction as given in result by optimal combination of epoch and hyperperameters.
Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid Abdelaziz Youssfi; Abdelmounaim Alioui; Youssef Ait El Kadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1420-1433

Abstract

This study presents a pioneering methodology for implementing the maximum power point tracking (MPPT) controller, based on fuzzy logic. Through a comprehensive performance analysis, we evaluate its effectiveness compared to the widely used perturb and observe (P&O) algorithm, which is a common MPPT technique. The main objective of our proposed MPPT approach is to improve the performance of a photovoltaic (PV) system. To evaluate the performance of the proposed MPPT controller and compare it with the P&O algorithm, we designed and simulated both controllers using MATLAB/Simulink. We also implemented a prototype of the controllers using an Arduino Mega board, and evaluated their performance under real operating conditions. The experimental results unequivocally confirm that the fuzzy logic-based MPPT controller outperforms the P&O algorithm in terms of performance, speed and accuracy. The fuzzy logic controller offers greater accuracy in tracking the maximum power point under various environmental circumstances, including variations in solar irradiation and connected load. Overall, this work contributes to the development of efficient and reliable MPPT controllers for PV systems, and provides a comparison of the performances of two popular MPPT techniques. Future research could explore other MPPT techniques and evaluate their performance using similar experimental setups.

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