Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702593
C. Vaidhyanathan, R.P. Hariharan, T. Shashank, Shreya Desikan, Aviral Bhatia, Surya Prakash
Lungs are the vital organs for respiratory health, which can be infected by contaminated air and vulnerable. Due to increasing air pollution worldwide, millions of people are at risk of contracting a severe respiratory disorder. This study aims to diagnoses and prognoses these disorders’ syndrome to start recovery early for a patient. The paper proposes a DenseNet-SVM architecture to detect and classify diseases from the spectrograms extracted from breathing sounds generated by a test subject and estimate the respiratory disorders syndromes for seven types of categories: Upper Respiratory Tract Infection (URTI), Healthy, Bronchiectasis, Pneumonia, Chronic Obstructive Pulmonary Disease (COPD), Bronchiolitis, and Lower Respiratory Tract Infection (LRTI) with the corresponding Area Under Curve (AUC) of receiver operating characteristics (ROC) is 0.99, 0.82, 0.68, 0.98, 1.00, 0.67, 0.68 respectively for the unseen tested data. The study establishes a model that can detect respiratory diseases with breathing patterns and patient information with a deep learning approach.
{"title":"Diagnose and Prognose the Syndrome of Respiratory Disorder Through Breathing Pattern Deploying DenseNet-SVM Model","authors":"C. Vaidhyanathan, R.P. Hariharan, T. Shashank, Shreya Desikan, Aviral Bhatia, Surya Prakash","doi":"10.1109/ICIIP53038.2021.9702593","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702593","url":null,"abstract":"Lungs are the vital organs for respiratory health, which can be infected by contaminated air and vulnerable. Due to increasing air pollution worldwide, millions of people are at risk of contracting a severe respiratory disorder. This study aims to diagnoses and prognoses these disorders’ syndrome to start recovery early for a patient. The paper proposes a DenseNet-SVM architecture to detect and classify diseases from the spectrograms extracted from breathing sounds generated by a test subject and estimate the respiratory disorders syndromes for seven types of categories: Upper Respiratory Tract Infection (URTI), Healthy, Bronchiectasis, Pneumonia, Chronic Obstructive Pulmonary Disease (COPD), Bronchiolitis, and Lower Respiratory Tract Infection (LRTI) with the corresponding Area Under Curve (AUC) of receiver operating characteristics (ROC) is 0.99, 0.82, 0.68, 0.98, 1.00, 0.67, 0.68 respectively for the unseen tested data. The study establishes a model that can detect respiratory diseases with breathing patterns and patient information with a deep learning approach.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"54 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038484","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}
Coronary Heart Diseases (CHDs) are a fundamental explanation of enormous deaths on earth in the last decades and are a dangerous disease in India and worldwide and Coronary Heart Disease has developed as one of the most unmistakable and uninformed reasons for death all around the globe. Thus, a dependable, precise & achievable framework for analyzing these maladies for appropriate therapy. Artificial Intelligence evaluations & systems are being used to restore data collections to robotize investigation within enormous & uneasy information. Numerous scientists, as of late, have been utilizing a few Artificial Intelligence methods to facilitate well-being for industry & professionals analysis of coronary-disease infections. This work intends to make use of chronological medical data to forecast CHD using Machine Learning. The work introduces machine learning techniques of different models dependent on calculations, procedures, and analyzes exhibition. Also, in this paper three supervised learning methods: Linear Regression using stochastic gradient descent and Decision Tree to find out the relationship in CHD data to improve prediction rate.
{"title":"A Forecast of Coronary Heart Disease using Proficient Machine Learning Algorithms","authors":"Shivani Gaba, Alankrita Aggarwal, Shally Nagpal, Deepak Kumar, Pardeep Singh","doi":"10.1109/ICIIP53038.2021.9702640","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702640","url":null,"abstract":"Coronary Heart Diseases (CHDs) are a fundamental explanation of enormous deaths on earth in the last decades and are a dangerous disease in India and worldwide and Coronary Heart Disease has developed as one of the most unmistakable and uninformed reasons for death all around the globe. Thus, a dependable, precise & achievable framework for analyzing these maladies for appropriate therapy. Artificial Intelligence evaluations & systems are being used to restore data collections to robotize investigation within enormous & uneasy information. Numerous scientists, as of late, have been utilizing a few Artificial Intelligence methods to facilitate well-being for industry & professionals analysis of coronary-disease infections. This work intends to make use of chronological medical data to forecast CHD using Machine Learning. The work introduces machine learning techniques of different models dependent on calculations, procedures, and analyzes exhibition. Also, in this paper three supervised learning methods: Linear Regression using stochastic gradient descent and Decision Tree to find out the relationship in CHD data to improve prediction rate.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367340","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702678
Abhilasha Sharma, S. Pandit, S. Talluri
In the modern era, the demand for 5th generation (5G) communication technology is increasing day by day due to the increased data rate, higher bandwidth, and lower delay time of 5G. To find the throughput range or its expected value in a particular slot, the classification and regression models are used. The present research applies three machine learning algorithms to predict and classify the throughput of 5G. The data for this study is obtained from the internet repository. Two classification models and two regression models are tested to predict the throughput of the millimeter wave (mm-wave) 5G dataset. The performance of classification algorithms is verified using precision, recall, F1 score, overall classification accuracy, and speed. It is observed that the random forest (RF) classifier achieves better values of all the performance parameters as compared to the support vector machine (SVM) classifier. The performance of the regression models is checked using root mean square error, correlation, R-square, and execution time. The experimental results show that the random forest model achieves better values of these parameters as compared to the generalized linear regression model (GLM). In addition, the observations show less execution time of the generalized linear model than the random forest model.
{"title":"A Comparative Study to Classify and Predict the Throughput of Fifth Generation Wireless Technology Using Supervised Machine Learning Algorithms","authors":"Abhilasha Sharma, S. Pandit, S. Talluri","doi":"10.1109/ICIIP53038.2021.9702678","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702678","url":null,"abstract":"In the modern era, the demand for 5th generation (5G) communication technology is increasing day by day due to the increased data rate, higher bandwidth, and lower delay time of 5G. To find the throughput range or its expected value in a particular slot, the classification and regression models are used. The present research applies three machine learning algorithms to predict and classify the throughput of 5G. The data for this study is obtained from the internet repository. Two classification models and two regression models are tested to predict the throughput of the millimeter wave (mm-wave) 5G dataset. The performance of classification algorithms is verified using precision, recall, F1 score, overall classification accuracy, and speed. It is observed that the random forest (RF) classifier achieves better values of all the performance parameters as compared to the support vector machine (SVM) classifier. The performance of the regression models is checked using root mean square error, correlation, R-square, and execution time. The experimental results show that the random forest model achieves better values of these parameters as compared to the generalized linear regression model (GLM). In addition, the observations show less execution time of the generalized linear model than the random forest model.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121136798","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702673
Priyank Mishra, P. Thakur, G. Singh
With the exponential growth of data traffic and demand for digital devices, it has become necessary to interconnect all these devices and establish a reliable communication through the internet. The required technology covers a wide area network, and it must include from the physical layer to the application layer of the Open Systems Interconnection (OSI) model. Therefore, the technological aspects of smart cities demand the incorporation based on the internet of things (IoT) concerns. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G, or LTE (Long Term Evolution) have been discussed in this article as solutions to the communication demands of Smart City for IoT. This paper provides a detailed aspect of smart city with its requirements, architecture, smart city components and its open research challenges with opportunities. Further, the role of IoT for smart city is well elaborated. The potential application of smart cities with some practical experience is thoroughly discussed.
{"title":"Enabling Technologies for IoT based Smart City","authors":"Priyank Mishra, P. Thakur, G. Singh","doi":"10.1109/ICIIP53038.2021.9702673","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702673","url":null,"abstract":"With the exponential growth of data traffic and demand for digital devices, it has become necessary to interconnect all these devices and establish a reliable communication through the internet. The required technology covers a wide area network, and it must include from the physical layer to the application layer of the Open Systems Interconnection (OSI) model. Therefore, the technological aspects of smart cities demand the incorporation based on the internet of things (IoT) concerns. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G, or LTE (Long Term Evolution) have been discussed in this article as solutions to the communication demands of Smart City for IoT. This paper provides a detailed aspect of smart city with its requirements, architecture, smart city components and its open research challenges with opportunities. Further, the role of IoT for smart city is well elaborated. The potential application of smart cities with some practical experience is thoroughly discussed.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"106-108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121195096","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702646
Sangeetha Annam, Anshu Singla
Soil heavy metal concentration not only leads to various health hazards in human life, but it also affects the physical, chemical, and biological properties of the soil. In due course, hyperspectral images, bearing hundreds of bands has become popular in the study of heavy metal content estimation present in soil. A preprocessed hyperspectral image has been estimated for the soil heavy metal content like Arsenic (As), Cadmium (Cd), and lead (Pb) using linear mixture model under spectral unmixing. Various supervised and unsupervised classification techniques were applied on the hyperspectral image and found that K-Means clustering technique yield better results up to 98.3 % accuracy and CEM yields 96.61% accuracy for supervised classification technique. The proposed model estimates and compare the heavy metal contents with the least possible sum-squared residual of 0.2 nothing but the amount of variance in the data under study leaving the correctness of the data to an accuracy of 99.8%.
{"title":"Spectral unmixing of heavy metal content in agricultural soil using hyperspectral remote sensing data","authors":"Sangeetha Annam, Anshu Singla","doi":"10.1109/ICIIP53038.2021.9702646","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702646","url":null,"abstract":"Soil heavy metal concentration not only leads to various health hazards in human life, but it also affects the physical, chemical, and biological properties of the soil. In due course, hyperspectral images, bearing hundreds of bands has become popular in the study of heavy metal content estimation present in soil. A preprocessed hyperspectral image has been estimated for the soil heavy metal content like Arsenic (As), Cadmium (Cd), and lead (Pb) using linear mixture model under spectral unmixing. Various supervised and unsupervised classification techniques were applied on the hyperspectral image and found that K-Means clustering technique yield better results up to 98.3 % accuracy and CEM yields 96.61% accuracy for supervised classification technique. The proposed model estimates and compare the heavy metal contents with the least possible sum-squared residual of 0.2 nothing but the amount of variance in the data under study leaving the correctness of the data to an accuracy of 99.8%.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123616019","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702556
Sandhya V, A. Padyana
Accurate forecasts of local and regional agricultural production are essential for agricultural market contractors and farmers to assist prize agreements as early as possible in the crop growing season. Predicting the crop yield well ahead of its harvest would help farmers and market contractors strategize befitting actions to market and store their produce. These kinds of predictions will also help farmers minimize losses due to crop failure and can also help businesses that depend on agricultural products to plan their business logistics and resources. In this paper, a method is proposed which would help predict the estimate of the crop yield for a specific land based on the analysis of geographical and climatic data using Machine Learning. Regression models such as Decision Tree Regression, K-Nearest Neighbor Regression, Gaussian Process Regression and Support Vector Regression are used along with feature selection, feature scaling, cross validation and hyperparameter tuning techniques to enhance their performance.
{"title":"Machine Learning based Crop Yield Prediction on Geographical and Climatic Data","authors":"Sandhya V, A. Padyana","doi":"10.1109/ICIIP53038.2021.9702556","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702556","url":null,"abstract":"Accurate forecasts of local and regional agricultural production are essential for agricultural market contractors and farmers to assist prize agreements as early as possible in the crop growing season. Predicting the crop yield well ahead of its harvest would help farmers and market contractors strategize befitting actions to market and store their produce. These kinds of predictions will also help farmers minimize losses due to crop failure and can also help businesses that depend on agricultural products to plan their business logistics and resources. In this paper, a method is proposed which would help predict the estimate of the crop yield for a specific land based on the analysis of geographical and climatic data using Machine Learning. Regression models such as Decision Tree Regression, K-Nearest Neighbor Regression, Gaussian Process Regression and Support Vector Regression are used along with feature selection, feature scaling, cross validation and hyperparameter tuning techniques to enhance their performance.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123208347","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702579
Aman Sharma, Khushi Shah, S. Verma
One of the most unique features that a human body can possess is the Face. This feature can be used to create a system that uniquely differentiates among different people. Face Recognition is one such system that detects a particular face by facial features. In contrast to the traditional methods of collecting attendance by calling out students' names by the teachers in a university/school or marking it in the registers at the main gate of any organization, this one consumes less time, effort, is more efficient, and also is a contactless method of doing the same. In this paper, we worked on a model that uses facial recognition technique to mark students’ attendance in an automated attendance management system using the Haar cascade classifier and LBPH algorithm. This one-time generation of dataset and face detection from the existing recognized images in this proposed system, is a more accurate and more improved system to collect attendance, thus leaving behind the tedious manual task.
{"title":"Face Recognition using Haar Cascade and Local Binary Pattern Histogram in OpenCV","authors":"Aman Sharma, Khushi Shah, S. Verma","doi":"10.1109/ICIIP53038.2021.9702579","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702579","url":null,"abstract":"One of the most unique features that a human body can possess is the Face. This feature can be used to create a system that uniquely differentiates among different people. Face Recognition is one such system that detects a particular face by facial features. In contrast to the traditional methods of collecting attendance by calling out students' names by the teachers in a university/school or marking it in the registers at the main gate of any organization, this one consumes less time, effort, is more efficient, and also is a contactless method of doing the same. In this paper, we worked on a model that uses facial recognition technique to mark students’ attendance in an automated attendance management system using the Haar cascade classifier and LBPH algorithm. This one-time generation of dataset and face detection from the existing recognized images in this proposed system, is a more accurate and more improved system to collect attendance, thus leaving behind the tedious manual task.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131371270","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}
Brain tumors are taken into consideration to be an extreme form of ailment with inside the medical field. Brain Tumors are a purpose for the peculiar and out of control division and growth of cells with inside the brain region itself. If this out-of-control increase will become greater than 60% then the affected person is not able to recover. Human inspection is the usual approach for detecting any contamination in MR brain images. This technique is impractical for a big quantity of data. Therefore, computerized tumor detection strategies are advanced as they might keep radiologists time. The step for tumor detection begins off evolved with the acquisition of an MRI test photo of the tumor. MRI images have grey and white matter and the vicinity affected by the tumor is of excessive intensity. The proposed work is split into four parts as preprocessing, feature extraction, augmentation after which classification has finished the usage of a machine learning algorithm.
{"title":"Brain Tumor Detection System Using Improved Convolutional Neural Network","authors":"Raj Kumar, Ashutosh Kumar Singh, Goutam Datta, Ashwani Kumar, H. Garg","doi":"10.1109/ICIIP53038.2021.9702648","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702648","url":null,"abstract":"Brain tumors are taken into consideration to be an extreme form of ailment with inside the medical field. Brain Tumors are a purpose for the peculiar and out of control division and growth of cells with inside the brain region itself. If this out-of-control increase will become greater than 60% then the affected person is not able to recover. Human inspection is the usual approach for detecting any contamination in MR brain images. This technique is impractical for a big quantity of data. Therefore, computerized tumor detection strategies are advanced as they might keep radiologists time. The step for tumor detection begins off evolved with the acquisition of an MRI test photo of the tumor. MRI images have grey and white matter and the vicinity affected by the tumor is of excessive intensity. The proposed work is split into four parts as preprocessing, feature extraction, augmentation after which classification has finished the usage of a machine learning algorithm.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131380778","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702680
Anupama Jamwal, Shruti Jain
Color visual deficiency is the weakness of shading vision. It is the decreased capacity to recognize references between various tones. Numerous sorts of visual weakness influence eye vision in various manners a particularly red-green, blue-yellow, and so forth. In any case, these days the normal kind of visual impairment is alluded to as red-green in which individuals can't separate between red and green. An insufficient individual discovers both the tones as similar one and a few groups saw it as beige tone. In this research paper, the authors designed a detection model to detect color blindness. Initially, data is collected from the ophthalmologist, pre-processed, and detected different color blindness. Authors can detect red, green, blue, gray cones. Usually, the sensitivity curves of the cones are different, making it harder to distinguish red from green, and making the overall perception of colors. Color-blindness is most prevalent among males with the most common being Red/Green. The level of neural experimentation to read the signals from the retina is to determine how a particular individual perceives a particular color has never been done. Colorblind people cannot differentiate in color when they are in extreme abundance as in an array.
{"title":"Detection of Cones for Different Color Visual Impairment","authors":"Anupama Jamwal, Shruti Jain","doi":"10.1109/ICIIP53038.2021.9702680","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702680","url":null,"abstract":"Color visual deficiency is the weakness of shading vision. It is the decreased capacity to recognize references between various tones. Numerous sorts of visual weakness influence eye vision in various manners a particularly red-green, blue-yellow, and so forth. In any case, these days the normal kind of visual impairment is alluded to as red-green in which individuals can't separate between red and green. An insufficient individual discovers both the tones as similar one and a few groups saw it as beige tone. In this research paper, the authors designed a detection model to detect color blindness. Initially, data is collected from the ophthalmologist, pre-processed, and detected different color blindness. Authors can detect red, green, blue, gray cones. Usually, the sensitivity curves of the cones are different, making it harder to distinguish red from green, and making the overall perception of colors. Color-blindness is most prevalent among males with the most common being Red/Green. The level of neural experimentation to read the signals from the retina is to determine how a particular individual perceives a particular color has never been done. Colorblind people cannot differentiate in color when they are in extreme abundance as in an array.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131794855","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702642
Bobbinpreet Kaur, Amit Verma
Advanced healthcare technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and deep learning, are required to counter and even prepare for new illnesses. As a result, we are examining IA's capacity to control and manage COVID-19 (Coronavirus) and other emerging pandemics. Using COVID-19 or Coronavirus and Artificial Intelligence or AI keywords, the material may be quickly found in the PubMed database. COVID-19 AI's existing understanding was analyzed to see how it may be used to increase COVID-19 AI's overall usefulness. Seven COVID-19 pandemic-related AI applications have been documented. The technology has the potential to locate the infection, track it through the system, and make forecasts about when the virus will infiltrate the whole system again. Decision-making tools are desperately needed to help combat this outbreak and allow healthcare institutions to gather enough information in real time to halt its spread. The primary objective of AI is to mimic human thinking using an expert methodology. COVID-19 vaccination production may also play a critical part in making sense of and advocating a similar project. This kind of technology is helpful in screening because of its emphasis on discoveries.
{"title":"Artificial Intelligence in the Fight Against Covid-19 (Coronavirus)","authors":"Bobbinpreet Kaur, Amit Verma","doi":"10.1109/ICIIP53038.2021.9702642","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702642","url":null,"abstract":"Advanced healthcare technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and deep learning, are required to counter and even prepare for new illnesses. As a result, we are examining IA's capacity to control and manage COVID-19 (Coronavirus) and other emerging pandemics. Using COVID-19 or Coronavirus and Artificial Intelligence or AI keywords, the material may be quickly found in the PubMed database. COVID-19 AI's existing understanding was analyzed to see how it may be used to increase COVID-19 AI's overall usefulness. Seven COVID-19 pandemic-related AI applications have been documented. The technology has the potential to locate the infection, track it through the system, and make forecasts about when the virus will infiltrate the whole system again. Decision-making tools are desperately needed to help combat this outbreak and allow healthcare institutions to gather enough information in real time to halt its spread. The primary objective of AI is to mimic human thinking using an expert methodology. COVID-19 vaccination production may also play a critical part in making sense of and advocating a similar project. This kind of technology is helpful in screening because of its emphasis on discoveries.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975346","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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