Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an auto-segmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM.
{"title":"Machine Learning Approach for Diagnosis and Prognosis of Cardiac Arrhythmia Condition Using a Minimum Feature Set and Auto-Segmentation-Based Window Optimisation","authors":"Swetha Rameshbabu, Sabitha Ramakrishnan","doi":"10.5755/j02.eie.34357","DOIUrl":"https://doi.org/10.5755/j02.eie.34357","url":null,"abstract":"Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an auto-segmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139308247","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}
S. Chmielarz, Tomasz Molenda, W. Korski, Krzysztof Oset, Waldemar Sobierajski
The article presents selected basic problems occurring in the design of intrinsically safe apparatuses, solutions to some of them, and points out mistakes made during their design. The article addresses selected issues related to the design of intrinsically safe apparatuses and systems and includes a systematisation of the news and conclusions. The article also presents several project solutions for hazardous areas.
{"title":"Overview of Problems and Solutions in the Design of Intrinsically Safe Apparatuses","authors":"S. Chmielarz, Tomasz Molenda, W. Korski, Krzysztof Oset, Waldemar Sobierajski","doi":"10.5755/j02.eie.34552","DOIUrl":"https://doi.org/10.5755/j02.eie.34552","url":null,"abstract":"The article presents selected basic problems occurring in the design of intrinsically safe apparatuses, solutions to some of them, and points out mistakes made during their design. The article addresses selected issues related to the design of intrinsically safe apparatuses and systems and includes a systematisation of the news and conclusions. The article also presents several project solutions for hazardous areas.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139307106","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}
Jie Zan, Yaosheng Hu, Shoufeng Jin, Ruichao Zhang, Rafal Stanislawski
To address the problems of unstable accuracy, low efficiency, and subjective influence of manual counting, a machine vision-based method to count the quantity of tobacco shreds is proposed for the first time. In this paper, the complex tobacco shred image is obtained by backlight imaging. The adaptive threshold segmentation method is used to segment tobacco shreds. The pixel area of the tobacco shred area is calculated by connected domain labelling. Second, independent tobacco shreds and adhesive tobacco shreds were identified based on the pixel area, and the quantity of segmented tobacco shreds was counted for the first time. Subsequently, in complex scenarios (such as tobacco shreds adhesive and overlapping), an image is usually obtained by manually drawing the contours of the adhesive and overlapping tobacco shreds on the basis of primary statistics. Finally, different individuals are distinguished, segmentation is completed, and tobacco shred quantity statistics are realised. The experimental results show that the average accuracy is 100.0 % for quantitative statistics of independent tobacco shred images. For tobacco shred images with adhesive and overlapping interference, the minimum accuracy is 90 %, and the accuracy increases with the increase in tobacco shred quantity. Furthermore, the efficiency of the tobacco shred quantity statistics conducted by the method in this paper was only affected by complex scenarios. Compared to artificial processing, the efficiency was increased by more than 100 %. The work in this paper can provide the technical basis for measuring the dimensions of tobacco shreds.
{"title":"Machine Vision Method for Quantitative Statistics Analysis of Industrial Product Images","authors":"Jie Zan, Yaosheng Hu, Shoufeng Jin, Ruichao Zhang, Rafal Stanislawski","doi":"10.5755/j02.eie.35083","DOIUrl":"https://doi.org/10.5755/j02.eie.35083","url":null,"abstract":"To address the problems of unstable accuracy, low efficiency, and subjective influence of manual counting, a machine vision-based method to count the quantity of tobacco shreds is proposed for the first time. In this paper, the complex tobacco shred image is obtained by backlight imaging. The adaptive threshold segmentation method is used to segment tobacco shreds. The pixel area of the tobacco shred area is calculated by connected domain labelling. Second, independent tobacco shreds and adhesive tobacco shreds were identified based on the pixel area, and the quantity of segmented tobacco shreds was counted for the first time. Subsequently, in complex scenarios (such as tobacco shreds adhesive and overlapping), an image is usually obtained by manually drawing the contours of the adhesive and overlapping tobacco shreds on the basis of primary statistics. Finally, different individuals are distinguished, segmentation is completed, and tobacco shred quantity statistics are realised. The experimental results show that the average accuracy is 100.0 % for quantitative statistics of independent tobacco shred images. For tobacco shred images with adhesive and overlapping interference, the minimum accuracy is 90 %, and the accuracy increases with the increase in tobacco shred quantity. Furthermore, the efficiency of the tobacco shred quantity statistics conducted by the method in this paper was only affected by complex scenarios. Compared to artificial processing, the efficiency was increased by more than 100 %. The work in this paper can provide the technical basis for measuring the dimensions of tobacco shreds.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139308877","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}
Israa Akram Alzuabidi, Layla safwat Jamil, A. A. Ahmed, Shahrul Azman Mohd Noah, Mohammad Kamrul Hasan
Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.
{"title":"A Hybrid Technique for Detecting Extremism in Arabic Social Media Texts","authors":"Israa Akram Alzuabidi, Layla safwat Jamil, A. A. Ahmed, Shahrul Azman Mohd Noah, Mohammad Kamrul Hasan","doi":"10.5755/j02.eie.34743","DOIUrl":"https://doi.org/10.5755/j02.eie.34743","url":null,"abstract":"Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139307194","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}
Mario Vrazic, A. Peršič, Peter Virtic, Tomislav Ivanis
Electric vehicles, especially cars, have been in the spotlight for some time now. In the focus of environmentalists, engineers, users, media, etc. With the growth and advancement of the market for such vehicles, other electric vehicles are also focussing on. One of such vehicles is boats, particularly smaller boats up to 8–10 meters in length. Of course, the biggest problem here is charging. The general idea is to use battery modules that can be easily carried and enable hot swapping. This paper investigates scenarios and simulations of the control system for hot swapping of the battery module. Simulations of connection of two and three battery modules to parallel operation and current control are presented in this paper, as well as applied control rules.
{"title":"Current Control of Battery Pack Modules in Parallel Connection According to SoC","authors":"Mario Vrazic, A. Peršič, Peter Virtic, Tomislav Ivanis","doi":"10.5755/j02.eie.35451","DOIUrl":"https://doi.org/10.5755/j02.eie.35451","url":null,"abstract":"Electric vehicles, especially cars, have been in the spotlight for some time now. In the focus of environmentalists, engineers, users, media, etc. With the growth and advancement of the market for such vehicles, other electric vehicles are also focussing on. One of such vehicles is boats, particularly smaller boats up to 8–10 meters in length. Of course, the biggest problem here is charging. The general idea is to use battery modules that can be easily carried and enable hot swapping. This paper investigates scenarios and simulations of the control system for hot swapping of the battery module. Simulations of connection of two and three battery modules to parallel operation and current control are presented in this paper, as well as applied control rules.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139307874","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}
Lin Zhong, Wei Rao, Xiaohang Zhang, Zhibin Zhang, Grzegorz Krolczyk
Due to improper setting of operating parameters, cigarette machines are subject to a high unqualified production rate. For this reason, in this study, a multiobjective optimisation (MOP) method based on the metaheuristic intelligence optimisation is proposed in this study. First, to eliminate interference parameters, the random forest (RF) is used to analyse the parameter importance of the cigarette machine and select the most important operation parameters for the multiobjective optimisation. Second, an artificial neural network (ANN) optimised by the grey wolf optimiser is designed to establish a mirror model of the cigarette machine to fast calculate the machine output quality factors, including the rod break rate, single cigarette weight, and circumference index. Lastly, an improved multiobjective grey wolf optimisation algorithm is used to optimise these three quality factors simultaneously to obtain the optimal operating parameters of the cigarette machine. A machine swarm (including four cigarette machines) in the real world is used to evaluate the developed optimisation method, and the testing results demonstrate that the proposed multiobjective optimisation method is able to improve the three quality factors by at least 50 %, which greatly reduces the unqualified rate of cigarettes.
{"title":"Operation Parameters Optimisation of a Machine Swarm Using Artificial Intelligence","authors":"Lin Zhong, Wei Rao, Xiaohang Zhang, Zhibin Zhang, Grzegorz Krolczyk","doi":"10.5755/j02.eie.35085","DOIUrl":"https://doi.org/10.5755/j02.eie.35085","url":null,"abstract":"Due to improper setting of operating parameters, cigarette machines are subject to a high unqualified production rate. For this reason, in this study, a multiobjective optimisation (MOP) method based on the metaheuristic intelligence optimisation is proposed in this study. First, to eliminate interference parameters, the random forest (RF) is used to analyse the parameter importance of the cigarette machine and select the most important operation parameters for the multiobjective optimisation. Second, an artificial neural network (ANN) optimised by the grey wolf optimiser is designed to establish a mirror model of the cigarette machine to fast calculate the machine output quality factors, including the rod break rate, single cigarette weight, and circumference index. Lastly, an improved multiobjective grey wolf optimisation algorithm is used to optimise these three quality factors simultaneously to obtain the optimal operating parameters of the cigarette machine. A machine swarm (including four cigarette machines) in the real world is used to evaluate the developed optimisation method, and the testing results demonstrate that the proposed multiobjective optimisation method is able to improve the three quality factors by at least 50 %, which greatly reduces the unqualified rate of cigarettes.","PeriodicalId":507694,"journal":{"name":"Elektronika ir Elektrotechnika","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139306331","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}
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