Pub Date : 2023-01-01DOI: 10.1615/intjmultcompeng.2023050106
Ramesh K, Duraivel AN, Lekashri S, Manikandan SP, Ashokkumar M
The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. Monitoring ECG signals is a crucial area of research because it enables early detection of catastrophic heart problems in people. Since conventional signal identification only considers one reference beat for identifying ECG signals, each individual's detection rate varies. In this paper, field-programmable gate array (FPGA) is employed to speed up ECG signal diagnosis and measure appropriate outcome to demonstrate that suggested ECG diagnosis algorithm is appropriate for hardware acceleration. The ECG diagnosis algorithm rapidly determine reference beats that change depending on person and analyze each person's signal executed at FPGA in real-time. In this paper, Noise removal from input ECG data set is performed by adaptive filter technique and base line wander is also removed. Machine learning in ECG classification is done by Artificial Neural Network (ANN) that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.
{"title":"Computational Framework for Prediction of Cardiac Disorders by analyzing ECG signals Using Machine Learning Technique","authors":"Ramesh K, Duraivel AN, Lekashri S, Manikandan SP, Ashokkumar M","doi":"10.1615/intjmultcompeng.2023050106","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023050106","url":null,"abstract":"The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. Monitoring ECG signals is a crucial area of research because it enables early detection of catastrophic heart problems in people. Since conventional signal identification only considers one reference beat for identifying ECG signals, each individual's detection rate varies. In this paper, field-programmable gate array (FPGA) is employed to speed up ECG signal diagnosis and measure appropriate outcome to demonstrate that suggested ECG diagnosis algorithm is appropriate for hardware acceleration. The ECG diagnosis algorithm rapidly determine reference beats that change depending on person and analyze each person's signal executed at FPGA in real-time. In this paper, Noise removal from input ECG data set is performed by adaptive filter technique and base line wander is also removed. Machine learning in ECG classification is done by Artificial Neural Network (ANN) that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135660937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1615/intjmultcompeng.2023050009
PRASANNA R, Ragupathi T, Ganesh Kumar N, Banu Priya Prathaban, Aswath S, Rajesh kanna R
This article proposes a novel biomedical system integrating Internet of Things (IoT) and Mixed Reality (MR) technologies for detecting, tracking and preventing asymptomatic COVID patients from entering into public places which prevents the further spread of COVID-19 infection. Asymptomatic patients are the very active carriers for virus transmission and the most challenging condition in mitigating the virus transmission are contact tracking and contact tracing of asymptomatic patients. The proposed system can be implemented in public places such as theatres, malls, railway stations, airport, markets, conferences, and other gatherings for screening people to detect asymptomatic COVID patients and restrict them from entry. The arrest or decrease in spread of COVID infection during pandemic situation is the most challenging factor around the globe. However, with the proposed system, detection and prevention of asymptomatic COVID patients will result in drastic decrease in the spread of COVID infection during pandemic situation. The proposed system comprises of an IoT based sensing system to get the current sensor values and an MR vision software system to retrieve the pre-saved sensor values from the server. The MR vision system compares the present sensor values and the server values of the human and displays accurately with green MR images for permitted persons and red MR images for restricted asymptomatic COVID patients.
{"title":"Computational Biomedical Framework Using IoT and MR for Detecting, Tracking and Preventing Asymptomatic COVID-19 Patients","authors":"PRASANNA R, Ragupathi T, Ganesh Kumar N, Banu Priya Prathaban, Aswath S, Rajesh kanna R","doi":"10.1615/intjmultcompeng.2023050009","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023050009","url":null,"abstract":"This article proposes a novel biomedical system integrating Internet of Things (IoT) and Mixed Reality (MR) technologies for detecting, tracking and preventing asymptomatic COVID patients from entering into public places which prevents the further spread of COVID-19 infection. Asymptomatic patients are the very active carriers for virus transmission and the most challenging condition in mitigating the virus transmission are contact tracking and contact tracing of asymptomatic patients. The proposed system can be implemented in public places such as theatres, malls, railway stations, airport, markets, conferences, and other gatherings for screening people to detect asymptomatic COVID patients and restrict them from entry. The arrest or decrease in spread of COVID infection during pandemic situation is the most challenging factor around the globe. However, with the proposed system, detection and prevention of asymptomatic COVID patients will result in drastic decrease in the spread of COVID infection during pandemic situation. The proposed system comprises of an IoT based sensing system to get the current sensor values and an MR vision software system to retrieve the pre-saved sensor values from the server. The MR vision system compares the present sensor values and the server values of the human and displays accurately with green MR images for permitted persons and red MR images for restricted asymptomatic COVID patients.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135709789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1615/intjmultcompeng.2023043190
Himanshu Upreti, Satyaranjan R. Mishra, Alok Kumar Pandey, Pradyumna K. Pattnaik
The main objective of this work is to examine the nature of heat transfer and thermodynamics on Darcy-Forchheimer flow over porous Riga plate using Casson hybrid nanofluid. The impact of external forces i.e., slip velocity and magnetic field are discussed for pure fluid, nanofluid and hybrid nanofluid. The Hamilton-Crosser model of thermal conductivity is applied for the nanofluid as well as hybrid nanofluid. The existing nonlinear partial differential equations are solved by Runge-Kutta-Fehlberg (RKF) technique. The present code is validated numerically with previous works and found in good agreement with them. The results affirm that all fluids velocities declined with increase in Casson factor values. Moreover, increasing magnetization, the entropy profiles are depreciated significantly for the case of pure fluid, nanofluid and hybrid nanofluid. This comparative study reveals that hybrid nanofluid dominates on both nanofluid and pure fluid.
{"title":"Thermodynamics analysis of Casson hybrid nanofluid flow over a porous Riga plate with slip effect","authors":"Himanshu Upreti, Satyaranjan R. Mishra, Alok Kumar Pandey, Pradyumna K. Pattnaik","doi":"10.1615/intjmultcompeng.2023043190","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023043190","url":null,"abstract":"The main objective of this work is to examine the nature of heat transfer and thermodynamics on Darcy-Forchheimer flow over porous Riga plate using Casson hybrid nanofluid. The impact of external forces i.e., slip velocity and magnetic field are discussed for pure fluid, nanofluid and hybrid nanofluid. The Hamilton-Crosser model of thermal conductivity is applied for the nanofluid as well as hybrid nanofluid. The existing nonlinear partial differential equations are solved by Runge-Kutta-Fehlberg (RKF) technique. The present code is validated numerically with previous works and found in good agreement with them. The results affirm that all fluids velocities declined with increase in Casson factor values. Moreover, increasing magnetization, the entropy profiles are depreciated significantly for the case of pure fluid, nanofluid and hybrid nanofluid. This comparative study reveals that hybrid nanofluid dominates on both nanofluid and pure fluid.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135613214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1615/intjmultcompeng.2023049047
Valeriy Buryachenko
{"title":"Fast Fourier transform method for peridynamic bar of periodic structure","authors":"Valeriy Buryachenko","doi":"10.1615/intjmultcompeng.2023049047","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023049047","url":null,"abstract":"","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135311376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1615/intjmultcompeng.2022041262
kishore balasubramanian, Ananthamoorthy N P, Ramya K
Diseases like pneumonia, influenza, bronchitis, corona virus (COVID – 19) are some of the major respiratory infections that have made a major impact globally leading to disability and death around the world. Automated detection of lung infections from medical imaging combined with computer vision has a lot of promise for improving healthcare towards COVID-19 and its consequences due to restricted healthcare emergencies. Finding the affected tissues and segmenting them from lung X-ray and CT images is difficult due to comparable neighbouring tissues, hazy boundaries, and unpredictable infections. To overcome these issues, we propose a novel deep learning framework that employs attention-based feature vectors and cross average pooling to detect the lung infection from the images. Multimodal images, after enhancement are processed independently through a pretrained DenseNet where the feature extraction is performed from fully connected and average pooled layers. Instead of assigning equal weight to each feature value in the feature vectors, an attention weight is assigned to each feature to highlight how much attention should be paid to it. The obtained attention-based features are then fused using cross average pooling method to produce a discriminatory feature set leading to improved diagnosis. The fused features are passed through a proposed deep learning modified neural network classifier to diagnose the repository infection. Experiments are performed on the standard Kaggle and Mendeley datasets and the results indicated an average accuracy of 99.2% with appreciable Kappa-index and F1-Score. The results of our DL method for categorising respiratory tract infections we
{"title":"An End-End Deep Learning Framework for lung infection recognition using Attention-based features and Cross average pooling","authors":"kishore balasubramanian, Ananthamoorthy N P, Ramya K","doi":"10.1615/intjmultcompeng.2022041262","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2022041262","url":null,"abstract":"Diseases like pneumonia, influenza, bronchitis, corona virus (COVID – 19) are some of the major respiratory infections that have made a major impact globally leading to disability and death around the world. Automated detection of lung infections from medical imaging combined with computer vision has a lot of promise for improving healthcare towards COVID-19 and its consequences due to restricted healthcare emergencies. Finding the affected tissues and segmenting them from lung X-ray and CT images is difficult due to comparable neighbouring tissues, hazy boundaries, and unpredictable infections. To overcome these issues, we propose a novel deep learning framework that employs attention-based feature vectors and cross average pooling to detect the lung infection from the images. Multimodal images, after enhancement are processed independently through a pretrained DenseNet where the feature extraction is performed from fully connected and average pooled layers. Instead of assigning equal weight to each feature value in the feature vectors, an attention weight is assigned to each feature to highlight how much attention should be paid to it. The obtained attention-based features are then fused using cross average pooling method to produce a discriminatory feature set leading to improved diagnosis. The fused features are passed through a proposed deep learning modified neural network classifier to diagnose the repository infection. Experiments are performed on the standard Kaggle and Mendeley datasets and the results indicated an average accuracy of 99.2% with appreciable Kappa-index and F1-Score. The results of our DL method for categorising respiratory tract infections we","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"52 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1615/intjmultcompeng.2022041735
N. Noii, Amirezza Khodadadian, T. Wick
{"title":"Bayesian Inversion Using Global-Local Forward Models Applied to Fracture Propagation in Porous Media","authors":"N. Noii, Amirezza Khodadadian, T. Wick","doi":"10.1615/intjmultcompeng.2022041735","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2022041735","url":null,"abstract":"","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67459735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1615/intjmultcompeng.2022042334
P. Cheng, Hehua Zhu, Wei Sun, Yi Shen, J. Fish
{"title":"A concurrent multiscale approach for fracturing of brittle composites based on the superposition-based phase field model","authors":"P. Cheng, Hehua Zhu, Wei Sun, Yi Shen, J. Fish","doi":"10.1615/intjmultcompeng.2022042334","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2022042334","url":null,"abstract":"","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67460157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1615/intjmultcompeng.2022041399
Pei Xi, Y. Huo, De-fu Zhu, C. Xin, Zhonglun Wang
{"title":"Numerical Investigation of the Failure Mechanism and Countermeasures of the Roadway Surrounding Rocks within Deep Soft Rock","authors":"Pei Xi, Y. Huo, De-fu Zhu, C. Xin, Zhonglun Wang","doi":"10.1615/intjmultcompeng.2022041399","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2022041399","url":null,"abstract":"","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67459931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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