Pub Date : 2021-11-26DOI: 10.1109/iccica52458.2021.9697184
R. Singh, Kishan Khandelia
Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.
{"title":"Web-based Computational Tools for Calculating Optimal Testing Pool Size for Diagnostic Tests of Infectious Diseases","authors":"R. Singh, Kishan Khandelia","doi":"10.1109/iccica52458.2021.9697184","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697184","url":null,"abstract":"Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"463 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":"115941878","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/iccica52458.2021.9697312
Swapnil D. Daphal, S. Koli
In recent years, plant disease detection and classification systems have helped in better farming practices. With the advent of artificial intelligence, agriculture automation has seen innovative methods to mitigate risk and losses in farming. In this paper use of deep learning for sugarcane, disease classification is analyzed. Around 1470 images with 5 categories have thoroughly experimented. Transfer learning methods like VGG-16 net and ResNet are compared for an identical set of input parameters. The results obtained show with the limited set of datasets, transfer learning schemes can provide good results. VGG-16 Net and ResNet have shown accuracy around 83.00 % & 91.00 %, respectively.
{"title":"Transfer Learning approach to Sugarcane Foliar disease Classification with state-of-the-art Sugarcane Database","authors":"Swapnil D. Daphal, S. Koli","doi":"10.1109/iccica52458.2021.9697312","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697312","url":null,"abstract":"In recent years, plant disease detection and classification systems have helped in better farming practices. With the advent of artificial intelligence, agriculture automation has seen innovative methods to mitigate risk and losses in farming. In this paper use of deep learning for sugarcane, disease classification is analyzed. Around 1470 images with 5 categories have thoroughly experimented. Transfer learning methods like VGG-16 net and ResNet are compared for an identical set of input parameters. The results obtained show with the limited set of datasets, transfer learning schemes can provide good results. VGG-16 Net and ResNet have shown accuracy around 83.00 % & 91.00 %, respectively.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","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":"128425341","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/iccica52458.2021.9697161
Pratiksha Nirale, M. Madankar
In this age of modern farming, it is very important to grow in terms of quality practice and the number of products offered. As in India, many people depend on growing crops and fruits. When it comes to counting and sorting fruits and vegetables by hand it takes a much higher amount of remuneration to pay the workers and they will not be able to get a good result. So, to overcome this problem of farmers and strengthen them with a low-cost savings plan this is the IoT and Machine Learning priority and fruit planning. In this research machine learning is used to detect the fruit phase and the recording process, the IoT camera is used with the microcontroller module which will be available to connect to coding and show computer usage.
{"title":"Analytical Study on IoT and Machine Learning based Grading and Sorting System for Fruits","authors":"Pratiksha Nirale, M. Madankar","doi":"10.1109/iccica52458.2021.9697161","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697161","url":null,"abstract":"In this age of modern farming, it is very important to grow in terms of quality practice and the number of products offered. As in India, many people depend on growing crops and fruits. When it comes to counting and sorting fruits and vegetables by hand it takes a much higher amount of remuneration to pay the workers and they will not be able to get a good result. So, to overcome this problem of farmers and strengthen them with a low-cost savings plan this is the IoT and Machine Learning priority and fruit planning. In this research machine learning is used to detect the fruit phase and the recording process, the IoT camera is used with the microcontroller module which will be available to connect to coding and show computer usage.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"12 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":"116853434","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/iccica52458.2021.9697171
C. Kalpana, B. Booba
In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.
{"title":"Bio-Inspired Firefly Algorithm A Methodical Survey – Swarm Intelligence Algorithm","authors":"C. Kalpana, B. Booba","doi":"10.1109/iccica52458.2021.9697171","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697171","url":null,"abstract":"In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"41 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":"129644425","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/iccica52458.2021.9697153
Jayant P. Mehare, M. Bartere
BlockChain (BC) has attracted a lot of attention, Because of its advanced immutability, security and special protection features. BC may overcome security and protection issues of the Internet of Things (IoT). In every event, BC is computationally expensive, limited flexibility also introduces crucial transfer speed overheads and delays too. Those are unsuitable for the IoT applications. IoT is still in its early stages, but it is expected to have a significant influence on the items we use in day today life. In addition with that the usage of IoT with the lessor security will lead to the malfunctioning of the operations from the external threats. The abusive operations attract every researcher to build up a mechanism to ensure the security of IoT platforms. Since the existing security methods are ineffective in the protection IoT systems, Blockchain is emerging as a potential solution for creating more secured IoT architectures in the future. In this paper, we presented comparative analysis among various applications in which the framework of IoT is based on Blockchain organization, and how they used BC with IoT innovation to achieve the security and other objectives.
{"title":"Comparative Analysis of IoT based Blockchain Secure framework for Various Applications","authors":"Jayant P. Mehare, M. Bartere","doi":"10.1109/iccica52458.2021.9697153","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697153","url":null,"abstract":"BlockChain (BC) has attracted a lot of attention, Because of its advanced immutability, security and special protection features. BC may overcome security and protection issues of the Internet of Things (IoT). In every event, BC is computationally expensive, limited flexibility also introduces crucial transfer speed overheads and delays too. Those are unsuitable for the IoT applications. IoT is still in its early stages, but it is expected to have a significant influence on the items we use in day today life. In addition with that the usage of IoT with the lessor security will lead to the malfunctioning of the operations from the external threats. The abusive operations attract every researcher to build up a mechanism to ensure the security of IoT platforms. Since the existing security methods are ineffective in the protection IoT systems, Blockchain is emerging as a potential solution for creating more secured IoT architectures in the future. In this paper, we presented comparative analysis among various applications in which the framework of IoT is based on Blockchain organization, and how they used BC with IoT innovation to achieve the security and other objectives.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"81 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":"116818407","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/iccica52458.2021.9697194
R. Shinde, P. Chandankhede
The biomedical field has developed to a greater extent since digital technology has emerged. Digital communication can connect to everything. In this project, the activities of the patient can be detected by using body sensors, and the data collected by the sensor will be processed with the help of Node-MCU. The processed data will be sent to the cloud server, and the data can be viewed through an android application by the user. The main objective of this paper is to calculate patients' activities based check their body temperature, pulse rates, and if patients are very critical, then what type of environment can we provide for them? For that, we can check the room temperature and the room humidity also. The activities of patients, like sitting, resting, standing, and so on, can be viewed on the Android app.
{"title":"Use of Body Sensors for Implementation of Human Activity Recognition","authors":"R. Shinde, P. Chandankhede","doi":"10.1109/iccica52458.2021.9697194","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697194","url":null,"abstract":"The biomedical field has developed to a greater extent since digital technology has emerged. Digital communication can connect to everything. In this project, the activities of the patient can be detected by using body sensors, and the data collected by the sensor will be processed with the help of Node-MCU. The processed data will be sent to the cloud server, and the data can be viewed through an android application by the user. The main objective of this paper is to calculate patients' activities based check their body temperature, pulse rates, and if patients are very critical, then what type of environment can we provide for them? For that, we can check the room temperature and the room humidity also. The activities of patients, like sitting, resting, standing, and so on, can be viewed on the Android app.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113971975","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/iccica52458.2021.9697307
Abhik Paul, R. Bania
Malaria is one of the life-threatening diseases which spread by the Plasmodium parasites. Traditionally, microscopists analyze the microscopic blood smear images but it is time consuming and may leads to false negatives. Automated detection of malaria from the thin blood smear slide images is a challenging task. However, in the domain of medical and healthcare applications, classification accuracy plays a vital role. The higher level of false negatives in medical diagnosis systems may raise the risk of the patients by not employing the required treatment they exactly need. In this article, we have developed three Convolution Neural Network (CNN) models for the prediction of malaria from the red blood cell images into infected parasite red blood cells and uninfected parasite red blood cells. Finally, out of the three setups, proposed CNN setup-1 with kernel size 3 x 3 and pool size of 2 x 2 achieved an accuracy of 96%.
{"title":"Malaria Parasite Classification using Deep Convolutional Neural Network","authors":"Abhik Paul, R. Bania","doi":"10.1109/iccica52458.2021.9697307","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697307","url":null,"abstract":"Malaria is one of the life-threatening diseases which spread by the Plasmodium parasites. Traditionally, microscopists analyze the microscopic blood smear images but it is time consuming and may leads to false negatives. Automated detection of malaria from the thin blood smear slide images is a challenging task. However, in the domain of medical and healthcare applications, classification accuracy plays a vital role. The higher level of false negatives in medical diagnosis systems may raise the risk of the patients by not employing the required treatment they exactly need. In this article, we have developed three Convolution Neural Network (CNN) models for the prediction of malaria from the red blood cell images into infected parasite red blood cells and uninfected parasite red blood cells. Finally, out of the three setups, proposed CNN setup-1 with kernel size 3 x 3 and pool size of 2 x 2 achieved an accuracy of 96%.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","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":"129882139","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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