Journal of electrical and electronic systems research最新文献

{"title":"Student Performance Classification: Data, Features and Classifiers","authors":"Muhammad Hareez Mohd Zaki, Mohd Azri Mohd Aziz, Suhana Sulaiman, Najidah Hambali","doi":"10.24191/jeesr.v23i1.008","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.008","url":null,"abstract":"","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":" 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Selection Methods Application Towards a New Dataset based on Online Student Activities","authors":"Muhammad Hareez Mohd Zaki, Mohd Azri Abdul Aziz, Suhana Sulaiman, Najidah Hambali","doi":"10.24191/jeesr.v23i1.004","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.004","url":null,"abstract":"","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"313 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135814078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“The Rise of Tech and the Question of Power” A Review on Detection and Classification of Multiple Power Quality Disturbances","authors":"Mohd Saiful Najib Ismail @ Marzuki, Ahmad Farid Abidin, Mohd Zamri Jusoh, Dalina Johari","doi":"10.24191/jeesr.v23i1.002","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.002","url":null,"abstract":"—Every single device responds differently to each disturbance, in addition, electrical devices themselves, at times, cause PQ disturbance. Recent studies are focussed on signal processing and classification method of PQ disturbances. The aim of the study is to find the best combination method of classifying multiple PQ disturbances. 80 papers were reviewed in this study. The papers are grouped into several categories related to signal processing and classification method. In this study, an Excel spreadsheet was used to outline the strengths and the weaknesses of the classification methods disscussed in the papers. It is found that the close similarity of characteristic hinders quality detection of PQ disturbance and secondly, previous researchers have chosen limited features extraction hence the lack of information for performing detection. Therefore, a development of modified methodologies is needed in improving the robustness in features extraction and better performance of PQ disturbances classifier. For future studies, it is suggested to focus on Stockwell Transform and Support Vector Machine combination in the methodology.","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"29 52","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oscillations Damping and Stability Improvement of Solar Photo-Voltaic Cell and Battery Energy Storage System Connected to a D.C.Microgrid using Metaheuristic Optimized PID Controller","authors":"Mohamed M.H Adam, Ameer Naeem, Muhamad Nabil Hidayat, Noor Fadzilah Mohamed Shariff, V., Naeem M,S. Hannoon, Vijayakumar Varadarajan","doi":"10.24191/jeesr.v23i1.007","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.007","url":null,"abstract":"","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"55 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Student Performance Classification using Support Vector Machine (SVM) with Polynomical Kernel on Online Student Activities","authors":"Muhammad Hareez Mohd Zaki, Mohd Azri Mohd Aziz, Suhana Sulaiman, Najidah Hambali","doi":"10.24191/jeesr.v23i1.009","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.009","url":null,"abstract":"—The increasing usage of classification algorithms has encouraged researchers to explore many topics, including academic-related topics. In addition, the availability of data from various academic information management systems in recent years has been increasing, causing classification to become a technique that is in demand by educational institutes. Thereby, having a classification technique is important in researching the data on students’ performance. The purpose of this study is to classify students’ performance by using a polynomial kernel of Support Vector Machine (SVM) on online students’ activities. A new dataset is proposed in this study, which consists of academic and student online behaviours that influence the students’ performance. The proposed dataset also undergoes pre-processing stage to improve the accuracy and identify the significance of the proposed features. The experiment for SVM-POLY classification performance was set with a range of values on the parameters to be optimised by an optimisation algorithm, Grid Search. Classification accuracy, Precision, Recall and f1-score were applied to observe the result and determine the best classifier performance. The experimental results show that SVM – POLY, with a gamma value of 0.005, regularisation value of 0.1 and degree value of 1, come out with the best performance compared to a default value of SVM – POLY. The study is significant towards educational data mining in analysing the students’ performance during online students’ activities.","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"58 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Lightning Electromagnetic Field due to Lightning Strikes on a Tall Structure","authors":"Norhidayu Rameli, Mohd Zainal Abidin Abd Kadi, Mahdi Izadi, Norhafiz Azis","doi":"10.24191/jeesr.v23i1.003","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.003","url":null,"abstract":"","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"127 37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Level Shifter Signal Conditioning Circuit Design for 3-electrode Cell Portable Redox Sensor","authors":"Nur Rabi’atul ‘Adawiyah Muhd Zain, Zatieyl Aqmar Nordin, Aimi Bazilah Rosli, Wan Fazlida Hanim Abdullah","doi":"10.24191/jeesr.v23i1.011","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.011","url":null,"abstract":"—This paper presents the effect of level shifter signal conditioning circuits in a low-cost portable potentiostat, implemented by combining several circuit modules and controlled by Advanced RISC Machines Cortex processor. Two architectures of level shifter modules are simulated: Non-inverting Summing Amplifier and Inverting Summing Amplifier. The main objective is to improve signal detection readability by widening the input voltage signal processing and increasing the amplitude range of the sensor output signal before the analog to digital signal conversion. The potential amplitude range of redox reaction captured by trans-impedance module at -3 V to 3 V is reduced by 50%. Then, the output voltage is converted by analog digital converter to be interfaced with microcontroller for Differential Pulse Voltammetry current properties. The designed potentiostat has a current readout range of ± 500mA and is validated to be at 15% difference with the standard laboratory potentiostat.","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"59 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic Literature Review of Machine Learning Methods in Insulin Secretion Model Analysis","authors":"Mohd Hussaini Abbas, Nor Azlan Othman, Samsul Setumin, Nor Salwa Damanhuri, Rohaiza Baharudin, Nur Sa’adah Muhamad Sauki, Sarah Addyani Shamsuddin","doi":"10.24191/jeesr.v23i1.010","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.010","url":null,"abstract":"— Endogenous insulin secretion ( U N ) plays a critical role in maintaining glucose homeostasis. Pathological changes in U N enable early detection of metabolic inefficiency prior to the onset of diabetes mellitus (DM). Numerous researches have been carried out to establish the most effective method for assessing the participant’s glycemic state by identifying their U N profile. In contrast to insulin sensitivity ( SI ), there is no gold standard for U N profile. Thus, the deconvolution of C-peptide measurements is used in the majority of research to identify the U N profile. Due to the fact that C-peptide and insulin are co-secreted equimolarly from pancreatic β -cells, the latter method is shown to be accurate. Although studies have shown that the machine learning-based strategies can yield very positive outcomes in other areas of DM diagnosis, there is currently little research that employing machine learning for quantifying the U N profile to enable early diagnosis of metabolic dysfunction. Hence, the main objective of this study is to conduct a thorough search on machine learning-based modelling strategies that were used to identify the individual-specific U N profile through the development of a U N model. Additionally, this study will investigate whether the data acquired from the U N model can be used to quantify a person’s metabolic condition (either normal, pre-diabetic or T2D). The literature search turned up prospective studies linking machine learning and U N in its search and analysis. Meta-analyses summarize the available data and highlight various methodological stances. Thus, the exploratory of machine learning classification and regression technique can be portrayed in 3 different scenarios during the identification of U N profile. The 3 scenarios are: the study of insulin secretion through analyzing the insulin sensitivity, the study of U N without taking into considerations or in-depth study of U 1 and U 2 , and the study of insulin secretion using deconvolution of plasma C-peptide concentrations. It is evident that while Decision Tree (DT) is ideal for the first scenario, Random Forest (RF) is the better option for the other two scenarios. Further optimization can be implemented with the use of these techniques under supervised learning to improve diagnosis and comprehend the pathogenesis of diabetes, particularly in U N .","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":" 34","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on Object Detection Algorithms based Deep Learning Methods","authors":"Wan Xing, Mohd Rizman Sultan Mohd, Juliana Johari, Fazlina Ahmat Ruslan","doi":"10.24191/jeesr.v23i1.001","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.001","url":null,"abstract":"","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"41 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating Imbalanced Classification Problems in Academic Performance with Resampling Methods","authors":"A’zraa Afhzan Ab Rahim, Norlida Buniyamin","doi":"10.24191/jeesr.v23i1.006","DOIUrl":"https://doi.org/10.24191/jeesr.v23i1.006","url":null,"abstract":"—The imbalanced dataset is a common problem in the educational performance environment, where the number of students with poor performance is much less than those who perform well. This can create problems when predicting academic performance using machine learning algorithms, which assume that the datasets have a balanced distribution across all classes. We compared three resampling methods: SMOTE, Borderline SMOTE, and ADASYN, and used five different classifiers (Logistic Regression, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, and Decision Tree) on three imbalanced educational datasets. We used five-fold cross-validation to assess two performance indicators: accuracy and recall. Although accuracy indicates the overall performance, we focus more on recall values because it is more incumbent to identify poor-performing students so that necessary interventions can be executed promptly. Our results showed that when resampling improved recall values, ADASYN outperforms SMOTE and Borderline SMOTE consistently, better classifying the poor-performing students. Overall, our results suggest that resampling methods can be effective in addressing the problem of imbalanced classification in academic performance. However, the choice of resampling method should be carefully considered, as the performance of different methods can vary depending on the classifier used.","PeriodicalId":470905,"journal":{"name":"Journal of electrical and electronic systems research","volume":"26 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}