Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290557
John Colaco, R. Lohani
Due to the ongoing COVID-19 crisis, many people who are arriving in Goa are home quarantined. Therefore, to continuously check their health status, we authors have proposed Internet of Things based electronic wireless communication system which is monitoring the health parameters continuously by using biosensors such as Respiration sensor, Body temperature sensor, Heart rate sensor, and Oxygen sensor. The received values will be transmitted to the nearby medical center or COVID Hospital using wireless technology. The same will be displayed on the Adafruit server. Also, the messages are sent on doctor’s mobile devices dealing with quarantined people and Government authorities through server or GSM modem. If any of the health parameters such as the temperature of the human body, respiration rate, and Heartbeat rate are exceeding their normal rate or for health parameters such as oxygen level and respiration rate falls below the normal rate then the buzzer is ringing alarm and the preventive action is taken by receiving authorities. The above-proposed health monitoring system has analyzed using a soft computing technique called fuzzy logic.
{"title":"Health Care System for Home Quarantine People","authors":"John Colaco, R. Lohani","doi":"10.1109/ICCE50343.2020.9290557","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290557","url":null,"abstract":"Due to the ongoing COVID-19 crisis, many people who are arriving in Goa are home quarantined. Therefore, to continuously check their health status, we authors have proposed Internet of Things based electronic wireless communication system which is monitoring the health parameters continuously by using biosensors such as Respiration sensor, Body temperature sensor, Heart rate sensor, and Oxygen sensor. The received values will be transmitted to the nearby medical center or COVID Hospital using wireless technology. The same will be displayed on the Adafruit server. Also, the messages are sent on doctor’s mobile devices dealing with quarantined people and Government authorities through server or GSM modem. If any of the health parameters such as the temperature of the human body, respiration rate, and Heartbeat rate are exceeding their normal rate or for health parameters such as oxygen level and respiration rate falls below the normal rate then the buzzer is ringing alarm and the preventive action is taken by receiving authorities. The above-proposed health monitoring system has analyzed using a soft computing technique called fuzzy logic.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133888","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290578
Ajit Kumar, Rajkumar Patra, A. Ghosh
Breast Cancer is the most common malignancy in women affecting 2.1 million women every year and causing the maximum number of deaths in women due to cancer. It occurs as a result of the unusual development of cells in the breast tissue, which is generally referred to as a Tumor. A tumor does not signify cancer. It may be not cancerous (benign), pre-cancerous (pre-malignant), or cancerous (malignant). Various types of tests such as mammograms, MRIs, ultrasound, and biopsy are frequently used to identify breast cancer. Early detection and treatment will help to improve breast cancer outcomes as well as survival. Therefore, this paper consists of a relative study of the breast cancer prediction using different supervised machine learning algorithms like Logistics Regression, K-Nearest Neighbors, Decision Tree Classifier, Gaussian NB, and Support Vector Machine on the UCI repository dataset. Concerning the performance of all the models, the accuracy score, precision, recall, and F-score of each model have been compared. After using various models, we got to see that Logistic Regression is a well-suited algorithm for Breast cancer prediction and came up with better accuracy and other performance indices as compared with other models.
{"title":"Model Selection for Predicting Breast Cancer using Supervised Machine Learning Algorithms","authors":"Ajit Kumar, Rajkumar Patra, A. Ghosh","doi":"10.1109/ICCE50343.2020.9290578","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290578","url":null,"abstract":"Breast Cancer is the most common malignancy in women affecting 2.1 million women every year and causing the maximum number of deaths in women due to cancer. It occurs as a result of the unusual development of cells in the breast tissue, which is generally referred to as a Tumor. A tumor does not signify cancer. It may be not cancerous (benign), pre-cancerous (pre-malignant), or cancerous (malignant). Various types of tests such as mammograms, MRIs, ultrasound, and biopsy are frequently used to identify breast cancer. Early detection and treatment will help to improve breast cancer outcomes as well as survival. Therefore, this paper consists of a relative study of the breast cancer prediction using different supervised machine learning algorithms like Logistics Regression, K-Nearest Neighbors, Decision Tree Classifier, Gaussian NB, and Support Vector Machine on the UCI repository dataset. Concerning the performance of all the models, the accuracy score, precision, recall, and F-score of each model have been compared. After using various models, we got to see that Logistic Regression is a well-suited algorithm for Breast cancer prediction and came up with better accuracy and other performance indices as compared with other models.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129602431","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290540
Rakeshkumar A. Patel, B. Bhalja, Md. Aftab Alam
Analysis of stator current and vibration of induction motor has long been used, in researches as well as industries, for the condition monitoring purpose. This paper presents the hardware results of three phase induction motor condition monitoring by current and vibration analysis, presenting one novel diagnosis method while analyzing the frequency spectra. Here current and vibration data are collected with the help of Digital Storage Oscilloscope and Vibration Analyzer respectively. These collected data are used to obtain frequency components of current and vibration with the help of MATLAB program. Later, the set of the most dominating frequency components of current and vibration are compared with that of healthy motor in order to establish the healthy/faulty condition. A 3 HP, 2.2 KW, three-phase induction motor has been used for analysis purpose. Three types of major faults, namely, stator inter turn short, rotor broken bar and bearing defect are considered. The proposed method proves to be quite successful in predicting rotor and bearing faults, while not being so successful in detecting stator inter-turn faults.
{"title":"Condition Monitoring of Three-Phase Induction Motor","authors":"Rakeshkumar A. Patel, B. Bhalja, Md. Aftab Alam","doi":"10.1109/ICCE50343.2020.9290540","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290540","url":null,"abstract":"Analysis of stator current and vibration of induction motor has long been used, in researches as well as industries, for the condition monitoring purpose. This paper presents the hardware results of three phase induction motor condition monitoring by current and vibration analysis, presenting one novel diagnosis method while analyzing the frequency spectra. Here current and vibration data are collected with the help of Digital Storage Oscilloscope and Vibration Analyzer respectively. These collected data are used to obtain frequency components of current and vibration with the help of MATLAB program. Later, the set of the most dominating frequency components of current and vibration are compared with that of healthy motor in order to establish the healthy/faulty condition. A 3 HP, 2.2 KW, three-phase induction motor has been used for analysis purpose. Three types of major faults, namely, stator inter turn short, rotor broken bar and bearing defect are considered. The proposed method proves to be quite successful in predicting rotor and bearing faults, while not being so successful in detecting stator inter-turn faults.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134553029","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}
Image registration is one of the most essential applications of image processing. In image registration, two images are compared to find a similarity metric and necessary adjustments are made to one of the images to minimize the similarity metric and align it to the other one (reference image). This minimization is performed using an optimization algorithm. Here, some of the newly developed meta-heuristic algorithms, namely Bat Algorithm and Grey Wolf Optimization are used to implement the image registration process with Mutual Information as the similarity metric. Along with these a Particle Swarm Optimization based image registration is also performed to the same sample sets. The performance results of these three implementations are compared on basis of both speed and quality of registration to find the overall best solution. The three algorithms are found to be very competitive when compared as optimizer in image registration process.
{"title":"Image Registration using Bio-inspired Algorithms","authors":"Kaushik Shaw, Puja Pandey, Shyandeep Das, Debasmita Ghosh, Pratikshan Malakar, Supriya Dhabal","doi":"10.1109/ICCE50343.2020.9290541","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290541","url":null,"abstract":"Image registration is one of the most essential applications of image processing. In image registration, two images are compared to find a similarity metric and necessary adjustments are made to one of the images to minimize the similarity metric and align it to the other one (reference image). This minimization is performed using an optimization algorithm. Here, some of the newly developed meta-heuristic algorithms, namely Bat Algorithm and Grey Wolf Optimization are used to implement the image registration process with Mutual Information as the similarity metric. Along with these a Particle Swarm Optimization based image registration is also performed to the same sample sets. The performance results of these three implementations are compared on basis of both speed and quality of registration to find the overall best solution. The three algorithms are found to be very competitive when compared as optimizer in image registration process.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133641973","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290655
Dalia Acharjee, M. Das, S. K. Samanta, Piyali Basak, Sukumar Roy
In this preset work bio-sourced hydroxyapatite (HAP) from waste egg shells and synthetic hydroxyapatite from calcium hydroxide were synthesized. A comparative analysis and characterization were done using XRD, FTIR, green density, porosity, cytotoxicity and hemolysis studies. It was found that both types of materials are almost similar in their properties and highly biocompatible. The biologically derived egg shell hydroxyapatite can be an ideal substitute for synthetically derived hydroxyapatite.
{"title":"Study on Structure and Properties of Crystalline Hydroxyapatite obtained from Biological and Synthetic Sources","authors":"Dalia Acharjee, M. Das, S. K. Samanta, Piyali Basak, Sukumar Roy","doi":"10.1109/ICCE50343.2020.9290655","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290655","url":null,"abstract":"In this preset work bio-sourced hydroxyapatite (HAP) from waste egg shells and synthetic hydroxyapatite from calcium hydroxide were synthesized. A comparative analysis and characterization were done using XRD, FTIR, green density, porosity, cytotoxicity and hemolysis studies. It was found that both types of materials are almost similar in their properties and highly biocompatible. The biologically derived egg shell hydroxyapatite can be an ideal substitute for synthetically derived hydroxyapatite.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132202976","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290495
Trishit Banerjee
The study analyzes the impact of the S&P500 returns along with influence of S&P500 Information Technology stocks (S&P500-IT) on Apple Inc. daily returns. This study also gives an insight into the connection between S&P500 Composite (S&P500-C) and Apple Inc. and S&P500-IT. The constant fluctuation of S&P500-C was noted in the time period. However, the rapid variation in regular returns at the beginning of 2018 has also been a part of the observation. The variation of the S&P500 in the case of IT stocks in 2018 was scrutinized. For the S&P500-C index, two linear estimation models for the daily returns of Apple Inc. have been generated. The indexes of S&P markets were regarded as predictors and the variable effects were measured for daily returns of Apple Inc. The models were later modified into a multiple linear regression model including S&P500-IT and S&P500-C as mutual predictors. A structural break was examined with the Chow analysis. The index of S&P500-IT and S&P500-C in the complex-regression model exhibits a negative effect on the daily returns of Apple Inc., due to multi co-linearity of the daily returns with S&P500-IT stocks. The structural breaks were insignificant in the improved regression model.
{"title":"Forecasting Apple Inc. Stock Prices Using S&P500– An OLS Regression Approach with Structural Break","authors":"Trishit Banerjee","doi":"10.1109/ICCE50343.2020.9290495","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290495","url":null,"abstract":"The study analyzes the impact of the S&P500 returns along with influence of S&P500 Information Technology stocks (S&P500-IT) on Apple Inc. daily returns. This study also gives an insight into the connection between S&P500 Composite (S&P500-C) and Apple Inc. and S&P500-IT. The constant fluctuation of S&P500-C was noted in the time period. However, the rapid variation in regular returns at the beginning of 2018 has also been a part of the observation. The variation of the S&P500 in the case of IT stocks in 2018 was scrutinized. For the S&P500-C index, two linear estimation models for the daily returns of Apple Inc. have been generated. The indexes of S&P markets were regarded as predictors and the variable effects were measured for daily returns of Apple Inc. The models were later modified into a multiple linear regression model including S&P500-IT and S&P500-C as mutual predictors. A structural break was examined with the Chow analysis. The index of S&P500-IT and S&P500-C in the complex-regression model exhibits a negative effect on the daily returns of Apple Inc., due to multi co-linearity of the daily returns with S&P500-IT stocks. The structural breaks were insignificant in the improved regression model.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128401092","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}
Pub Date : 2020-09-05DOI: 10.1109/icce50343.2020.9290593
{"title":"ICCE 2020 List Reviewer Page","authors":"","doi":"10.1109/icce50343.2020.9290593","DOIUrl":"https://doi.org/10.1109/icce50343.2020.9290593","url":null,"abstract":"","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126299622","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290539
Rishov Nag, Soumik De, Nabhoneel Majumdar, Pratik Dutta
This paper addresses the fact that there is no traffic tweet classification methods that try to identify the cause of the congestion. Our goal is to perform a multiclass classification of traffic-related tweets into traffic-congestion-cause-based groups. We perform various clustering techniques on our dataset, which we obtained from the Kolkata Traffic Police's Twitter handle. The clustering gives us the desired result of classifying the tweets into four broad categories based on the type of event causing the congestion.
{"title":"A Study on Event Identification on Social Media Data","authors":"Rishov Nag, Soumik De, Nabhoneel Majumdar, Pratik Dutta","doi":"10.1109/ICCE50343.2020.9290539","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290539","url":null,"abstract":"This paper addresses the fact that there is no traffic tweet classification methods that try to identify the cause of the congestion. Our goal is to perform a multiclass classification of traffic-related tweets into traffic-congestion-cause-based groups. We perform various clustering techniques on our dataset, which we obtained from the Kolkata Traffic Police's Twitter handle. The clustering gives us the desired result of classifying the tweets into four broad categories based on the type of event causing the congestion.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131701565","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290604
Sandip Paul, K. Ray, D. Saha
Hybrid deep neural-symbolic architecture for event-detection employs a deep neural network at the back-end to perform low-level reasoning and a symbolic logical module to perform high-level cognitive reasoning. The currently known hybrid architectures use classical Answer Set Programming(ASP), which is unable to perform fuzzy reasoning with uncertainty. Moreover these systems don’t extract new rules from the available data. On the other hand, there are neuro-fuzzy systems that can extract fuzzy rules from data by means of Gaussian Restricted Boltzman Machines (GRBM). Both the aspects should be merged together to achieve human-like intelligent reasoning and learning from environment. But the success of such an integration depends upon the chosen logical system, that can support fuzzy reasoning with uncertainty, as well as, can support the extracted knowledge from GRBM. This work investigates the feasibility of using interval-valued fuzzy logic programming for this purpose. This work focuses on the theoretical aspects from logic programming perspective.
{"title":"Application of Fuzzy Answer set Programming in Hybrid Deep Neural-Symbolic Architecture","authors":"Sandip Paul, K. Ray, D. Saha","doi":"10.1109/ICCE50343.2020.9290604","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290604","url":null,"abstract":"Hybrid deep neural-symbolic architecture for event-detection employs a deep neural network at the back-end to perform low-level reasoning and a symbolic logical module to perform high-level cognitive reasoning. The currently known hybrid architectures use classical Answer Set Programming(ASP), which is unable to perform fuzzy reasoning with uncertainty. Moreover these systems don’t extract new rules from the available data. On the other hand, there are neuro-fuzzy systems that can extract fuzzy rules from data by means of Gaussian Restricted Boltzman Machines (GRBM). Both the aspects should be merged together to achieve human-like intelligent reasoning and learning from environment. But the success of such an integration depends upon the chosen logical system, that can support fuzzy reasoning with uncertainty, as well as, can support the extracted knowledge from GRBM. This work investigates the feasibility of using interval-valued fuzzy logic programming for this purpose. This work focuses on the theoretical aspects from logic programming perspective.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131229760","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}
Pub Date : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290715
Sumagna Dey, S. Biswas, Srija Nandi, Subhrapratim Nath, Indrajit Das
The extensive outbreak of COVID-19 has created a worldwide health crisis. Transmission of this disease occurs among people through droplets which causes severe respiratory distress and in turn can also lead to fatal death. At the pinnacle of this pandemic, scientists endeavor to discover the medication for the COVID-19 victims. Artificial Intelligence algorithms, especially, deep learning, on the other hand, is used for the diagnosis of the COVID-19 patients but this requires an enormous radiographic data set to effectively provide an optimized outcome for a particular scenario. This work presents a new technique called ‘Deep Greedy Network’ which will work efficiently with a finite number of datasets. In spite of peculiarity caused due to limited dataset, the anomaly of overfitting and underfitting could be effectively overcome using the proposed algorithm. This, in turn, is simultaneously going to be both cost-effective and efficient. The proposed architecture ensures the efficacious result after the proper judgement of the trained model on the given X-ray datasets of COVID-19 cases.
{"title":"Deep Greedy Network: A Tool for Medical Diagnosis on Exiguous Dataset of COVID-19","authors":"Sumagna Dey, S. Biswas, Srija Nandi, Subhrapratim Nath, Indrajit Das","doi":"10.1109/ICCE50343.2020.9290715","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290715","url":null,"abstract":"The extensive outbreak of COVID-19 has created a worldwide health crisis. Transmission of this disease occurs among people through droplets which causes severe respiratory distress and in turn can also lead to fatal death. At the pinnacle of this pandemic, scientists endeavor to discover the medication for the COVID-19 victims. Artificial Intelligence algorithms, especially, deep learning, on the other hand, is used for the diagnosis of the COVID-19 patients but this requires an enormous radiographic data set to effectively provide an optimized outcome for a particular scenario. This work presents a new technique called ‘Deep Greedy Network’ which will work efficiently with a finite number of datasets. In spite of peculiarity caused due to limited dataset, the anomaly of overfitting and underfitting could be effectively overcome using the proposed algorithm. This, in turn, is simultaneously going to be both cost-effective and efficient. The proposed architecture ensures the efficacious result after the proper judgement of the trained model on the given X-ray datasets of COVID-19 cases.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126485197","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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