Pub Date : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039612
S. Jain
A SMARRT government is a simple, moral, accountable, responsive, responsible, and transparent government. This is what e-Government is supposed to give. Any nation's governance can now be done in a moral, straight forward, accountable, responsible, responsive, and transparent manner thanks to the emergence of e-Governance. This paper investigates the e-Government development indicators (EGDI) and their sub-indices in India and policy implications for India for the sake of improving their EGDI. This study and analysis are based on surveys of the United Nations conducted on e-Government between 2003 and 2020. To achieve the objective of the National e-Governance Plan (NeGP) and Digital India Program, this study also provides suggestions and prioritizes the United Nations e-Government development indicators in India.
{"title":"Comparative Study of United Nations E-Government Indicators Between World Leaders and India (Measuring Digital India)","authors":"S. Jain","doi":"10.1109/ICAST55766.2022.10039612","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039612","url":null,"abstract":"A SMARRT government is a simple, moral, accountable, responsive, responsible, and transparent government. This is what e-Government is supposed to give. Any nation's governance can now be done in a moral, straight forward, accountable, responsible, responsive, and transparent manner thanks to the emergence of e-Governance. This paper investigates the e-Government development indicators (EGDI) and their sub-indices in India and policy implications for India for the sake of improving their EGDI. This study and analysis are based on surveys of the United Nations conducted on e-Government between 2003 and 2020. To achieve the objective of the National e-Governance Plan (NeGP) and Digital India Program, this study also provides suggestions and prioritizes the United Nations e-Government development indicators in India.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128718357","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039614
Harshil Shah, Harsh V Thakkar, S. Dharmadhikari
Potatoes are the third most important food source in the world after wheat and rice. Moreover, India, in the fiscal year 2021, produced over 50 million metric tons of potatoes out of which _ percent was wasted due to late blight and early blight diseases. The traditional methods to detect diseases in the plants involve manual inspection. This method is very expensive, time-consuming and does not provide satisfactory results. So as to identify the infected leaves at the beginning of their growth cycle which will help to increase the yield and thereby decrease the losses incurred by the farmers, we propose a web application to do the same with the help of deep learning models like InceptionNet, ResNet50, MobileNet and CNN trained on the PlantVillage dataset available on kaggle. We achieved an accuracy of 93.97 percent, 88.79 percent, 96.12 percent and 94.83 percent respectively.
{"title":"Potato Leaf Disease Detection using Sequencial Models","authors":"Harshil Shah, Harsh V Thakkar, S. Dharmadhikari","doi":"10.1109/ICAST55766.2022.10039614","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039614","url":null,"abstract":"Potatoes are the third most important food source in the world after wheat and rice. Moreover, India, in the fiscal year 2021, produced over 50 million metric tons of potatoes out of which _ percent was wasted due to late blight and early blight diseases. The traditional methods to detect diseases in the plants involve manual inspection. This method is very expensive, time-consuming and does not provide satisfactory results. So as to identify the infected leaves at the beginning of their growth cycle which will help to increase the yield and thereby decrease the losses incurred by the farmers, we propose a web application to do the same with the help of deep learning models like InceptionNet, ResNet50, MobileNet and CNN trained on the PlantVillage dataset available on kaggle. We achieved an accuracy of 93.97 percent, 88.79 percent, 96.12 percent and 94.83 percent respectively.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125939921","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039569
Venkata A. P. Chavali, T. Tirodkar, A. Ambekar, A. Deshmukh
This article presents detailed analysis of a broadband single layer gap-coupled shorted stub loaded rectangular microstrip antenna with filtering Characteristics. With support of surface current distributions, resonance modes yielding wider bandwidth are recognized. Pair of rectangular stubs connected on both sides of the fed patch along with shorting pins tunes spacing between TM20 and TM01 modes of the main patch, The incorporation of parasitic patches above and below the fed patch tunes the impedance and brings impedance locus within the voltage standing wave ratio = 2 circle realizing a wider bandwidth of about 21% due to close spacing and impedance matching of three resonant modes. In addition to the wideband response incorporation of narrow strips and pair of stubs around the fed patch introduces radiation nulls at higher and lower frequencies respectively realizing a band pass filter response. This can be further improved with the addition of a pair of parasitic patches above and below the fed patch. With this modal understanding the antenna is optimized at 1500 MHz on Arlon substrate which resulted in an impedance BW of 3.35% and above 5 dBi peak gain. Resulting polar patterns are in broadside direction near band edge frequencies of bandwidth.
{"title":"Analysis of Broadband Single Layer Gap-Coupled Shorted Rectangular Microstrip Antenna","authors":"Venkata A. P. Chavali, T. Tirodkar, A. Ambekar, A. Deshmukh","doi":"10.1109/ICAST55766.2022.10039569","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039569","url":null,"abstract":"This article presents detailed analysis of a broadband single layer gap-coupled shorted stub loaded rectangular microstrip antenna with filtering Characteristics. With support of surface current distributions, resonance modes yielding wider bandwidth are recognized. Pair of rectangular stubs connected on both sides of the fed patch along with shorting pins tunes spacing between TM20 and TM01 modes of the main patch, The incorporation of parasitic patches above and below the fed patch tunes the impedance and brings impedance locus within the voltage standing wave ratio = 2 circle realizing a wider bandwidth of about 21% due to close spacing and impedance matching of three resonant modes. In addition to the wideband response incorporation of narrow strips and pair of stubs around the fed patch introduces radiation nulls at higher and lower frequencies respectively realizing a band pass filter response. This can be further improved with the addition of a pair of parasitic patches above and below the fed patch. With this modal understanding the antenna is optimized at 1500 MHz on Arlon substrate which resulted in an impedance BW of 3.35% and above 5 dBi peak gain. Resulting polar patterns are in broadside direction near band edge frequencies of bandwidth.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"38 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017319","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039513
Darshan Patil, Reena Lokare, Sunita Patil
Increased digitization in nearly every sector demands huge data storage requirements. Every person upload tons of information related to themselves on Internet through some mobile or web application, knowingly or sometimes unknowingly. Such increasing personal data storage requirement has created data privacy issues. There is no law which prohibits someone from using personal information of an individual. India is still in the process of preparing personal data protection law, whereas European Union's data protection regulation has already took place in the year 2018. Some organizations are in the process of developing applications which can check whether a document is personal or non-personal. Such applications can be developed with the help of deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Term Short Memory (LSTM), etc. This research focuses on different text representation techniques required to represent text in text classification problems such as private data classification, sentiment analysis, language detection, online abuse detection, recommendations systems, to name a few. Having represented text in different formats, helps in increasing accuracy of classification algorithms.
{"title":"Privacy Preserving Document Classification using Convolution Neural Network- A Deep Learning Approach","authors":"Darshan Patil, Reena Lokare, Sunita Patil","doi":"10.1109/ICAST55766.2022.10039513","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039513","url":null,"abstract":"Increased digitization in nearly every sector demands huge data storage requirements. Every person upload tons of information related to themselves on Internet through some mobile or web application, knowingly or sometimes unknowingly. Such increasing personal data storage requirement has created data privacy issues. There is no law which prohibits someone from using personal information of an individual. India is still in the process of preparing personal data protection law, whereas European Union's data protection regulation has already took place in the year 2018. Some organizations are in the process of developing applications which can check whether a document is personal or non-personal. Such applications can be developed with the help of deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Term Short Memory (LSTM), etc. This research focuses on different text representation techniques required to represent text in text classification problems such as private data classification, sentiment analysis, language detection, online abuse detection, recommendations systems, to name a few. Having represented text in different formats, helps in increasing accuracy of classification algorithms.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999462","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039570
N. Srivastava, Jameel Ahmad
Nowadays, Massive Open Online Courses are in demand owing to their informative value, easy access and low costs. The Covid-19 pandemic era saw a lot of teaching and learning through the online resources. One of the developing fields is Educational Data Mining in which the data derived from the educational environments is collected in databases, which is further analyzed to extract some interesting patterns of information. The findings can aid in supporting the educational staff in designing a cohort that may produce better results in terms of increasing the learner's performance, identifying at-risk students, placement prediction and dropout prediction, whatever the current motive may be. In this paper, we emphasize on the techniques focusing on the performance prediction that have been applied during the years 2012 to 2022 and the attributes affecting the performance have been determined.
{"title":"A Review on the Learner's Performance Prediction Techniques in MOOC Courses through Data Mining","authors":"N. Srivastava, Jameel Ahmad","doi":"10.1109/ICAST55766.2022.10039570","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039570","url":null,"abstract":"Nowadays, Massive Open Online Courses are in demand owing to their informative value, easy access and low costs. The Covid-19 pandemic era saw a lot of teaching and learning through the online resources. One of the developing fields is Educational Data Mining in which the data derived from the educational environments is collected in databases, which is further analyzed to extract some interesting patterns of information. The findings can aid in supporting the educational staff in designing a cohort that may produce better results in terms of increasing the learner's performance, identifying at-risk students, placement prediction and dropout prediction, whatever the current motive may be. In this paper, we emphasize on the techniques focusing on the performance prediction that have been applied during the years 2012 to 2022 and the attributes affecting the performance have been determined.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126553814","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039665
A. Bhonsle
Most of the work done in text recognition focuses on English, more specifically, Latin script languages. Unlike Latin, Indic scripts like Devanagari, Gujarati, Malayalam, Tamil etc. are a family of abugida writing systems. In these scripts each unit is made up of a consonant and an optional vowel notion. This makes the separation of different characters a non-trivial task as each visible letter may represent more than one character. Letters derived from the same base consonant can be visually similar to each other and make distinguishing between them quite difficult. This paper focuses on Devanagari script and a technique to generate synthetic text recognition datasets with rich glyph-level annotations.
{"title":"Generating Datasets with Glyph-level Annotations for Devanagari Text Recognition","authors":"A. Bhonsle","doi":"10.1109/ICAST55766.2022.10039665","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039665","url":null,"abstract":"Most of the work done in text recognition focuses on English, more specifically, Latin script languages. Unlike Latin, Indic scripts like Devanagari, Gujarati, Malayalam, Tamil etc. are a family of abugida writing systems. In these scripts each unit is made up of a consonant and an optional vowel notion. This makes the separation of different characters a non-trivial task as each visible letter may represent more than one character. Letters derived from the same base consonant can be visually similar to each other and make distinguishing between them quite difficult. This paper focuses on Devanagari script and a technique to generate synthetic text recognition datasets with rich glyph-level annotations.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132430626","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}
Smart Surveillance Systems are becoming an important aspect of our lives, reducing man labour and additionally increasing the accuracy of detection by reducing false positives. Specifically for an ATM, Surveillance system is very crucial because of the transactions happening being sensitive along with that drop-box containing confidential documents like cheques and bank forms. Hence, there is a need to develop a fool-proof system which can handle a lot of load and perform various surveillance tasks. Moreover, the systems also need to have network security to protect the data from being illegally traced and changed. In this paper, we will be reviewing and comparing various smart surveillance system methods which involve various technologies.
{"title":"Review: Video Analytics Technologies Available for Surveillance Systems","authors":"Utkarsha Mokashi, Aarush Dimri, Hardee Khambhla, Pradnya Bhangale","doi":"10.1109/ICAST55766.2022.10039583","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039583","url":null,"abstract":"Smart Surveillance Systems are becoming an important aspect of our lives, reducing man labour and additionally increasing the accuracy of detection by reducing false positives. Specifically for an ATM, Surveillance system is very crucial because of the transactions happening being sensitive along with that drop-box containing confidential documents like cheques and bank forms. Hence, there is a need to develop a fool-proof system which can handle a lot of load and perform various surveillance tasks. Moreover, the systems also need to have network security to protect the data from being illegally traced and changed. In this paper, we will be reviewing and comparing various smart surveillance system methods which involve various technologies.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047703","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039524
Tejashree S. Phatak, Jayalekshmi Nair, S. Ram, B. Roy, G. Mohanto
Accurate quantification of uncertainty present in the HPGe detector efficiency is very essential as uncertainty in the detector efficiency affects the measured cross-section value of neutron reaction. Error propagation has been active area of research interest in the nuclear field, since cross-section plays a major role in the nuclear reactor calculations. Therefore, Uncertainty quantification in the detector efficiency has been performed using non linear Unscented Transformation techniques such as Extended Unscented Transformation, Spherical Unscented Transformation, and Simplex Unscented Transformation. To evaluate the performance of these techniques in error propagation, a comparative study has been carried out in comparison with the well-known Monte Carlo method using the chi-square test.
{"title":"Non-Linear Unscented Transformation Techniques for Error Estimation of HPGe Detector Efficiency","authors":"Tejashree S. Phatak, Jayalekshmi Nair, S. Ram, B. Roy, G. Mohanto","doi":"10.1109/ICAST55766.2022.10039524","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039524","url":null,"abstract":"Accurate quantification of uncertainty present in the HPGe detector efficiency is very essential as uncertainty in the detector efficiency affects the measured cross-section value of neutron reaction. Error propagation has been active area of research interest in the nuclear field, since cross-section plays a major role in the nuclear reactor calculations. Therefore, Uncertainty quantification in the detector efficiency has been performed using non linear Unscented Transformation techniques such as Extended Unscented Transformation, Spherical Unscented Transformation, and Simplex Unscented Transformation. To evaluate the performance of these techniques in error propagation, a comparative study has been carried out in comparison with the well-known Monte Carlo method using the chi-square test.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930856","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039572
Rishabh Patil, Leena Kirtikar, Parth Shroff, Aakash Kapadia, M. Tolani, M. Edinburgh
We live in a society where competition is the driving force behind people getting into prestigious institutions and selecting courses that are most relevant to their current circumstances. It is getting harder for students to get accepted to their dream institution due to the rise in the number of graduates wanting to continue their education. This method maybe prejudiced and inaccurate given the limited number of colleges that a human consultant may evaluate. There is no way to measure the trustworthiness of advice given nowadays because there are so many different avenues to acquire information. Thus, we have provided a way to give unbiased advice with the help of our research. In this research work, we have compared multiple algorithms to give us the best results regarding which college fits perfect for the user according to the fees bracket provided. In this paper, Linear Regression, Decision Tree and Random Forest will be compared. Furthermore, this paper will display the use of K-means Clustering to help us rate the Statement of Purpose (SOPs) and Letter of Recommendation (LORs) provided. The results might help the students in solving their dilemma about choosing the suitable university based on their academic performance.
{"title":"Post Graduate College Prediction with SOP and LOR Analyser","authors":"Rishabh Patil, Leena Kirtikar, Parth Shroff, Aakash Kapadia, M. Tolani, M. Edinburgh","doi":"10.1109/ICAST55766.2022.10039572","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039572","url":null,"abstract":"We live in a society where competition is the driving force behind people getting into prestigious institutions and selecting courses that are most relevant to their current circumstances. It is getting harder for students to get accepted to their dream institution due to the rise in the number of graduates wanting to continue their education. This method maybe prejudiced and inaccurate given the limited number of colleges that a human consultant may evaluate. There is no way to measure the trustworthiness of advice given nowadays because there are so many different avenues to acquire information. Thus, we have provided a way to give unbiased advice with the help of our research. In this research work, we have compared multiple algorithms to give us the best results regarding which college fits perfect for the user according to the fees bracket provided. In this paper, Linear Regression, Decision Tree and Random Forest will be compared. Furthermore, this paper will display the use of K-means Clustering to help us rate the Statement of Purpose (SOPs) and Letter of Recommendation (LORs) provided. The results might help the students in solving their dilemma about choosing the suitable university based on their academic performance.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126725765","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039594
Soham Shinde, S. Yadav, Ashelesha Somvanshi
The intelligent models is used for prediction of diseases as well as creation of model that helps doctor to prevent spreading of disease globally is increased day by day. When a disease spreads rapidly in a short period of time in a specific area, it is called an epidemic outbreak. An outbreak might start in a single community or spread across multiple countries. It might last anywhere from a few days to several years. PHO (Public health organizations) are taking preventative efforts to stop the disease from spreading besides that they are highly benefited from accurate prediction of infectious disease. The emergence of big data in the sectors of health and biomedicine, precise data analysis aids early disease identification and better patient treatment. It is now increasingly viable to use massive computing power to predict and manage outbreaks. Our goal is to investigate and determine how outbreaks spread in villages and suburbs where medical care may be limited. A machine learning model is required to forecast epidemic dynamics and identify where the next outbreak is most likely to occur. Because these are important features that contribute subtly to the dynamics of the disease epidemic, our method considers the climate, geography, and distribution of population in impacted region. Our approach will assist health authorities in taking the necessary steps to guarantee that there are sufficient resources to fulfil demand and, if feasible, to prevent epidemics from arising.
{"title":"Epidemic Outbreak Prediction Using Machine Learning Model","authors":"Soham Shinde, S. Yadav, Ashelesha Somvanshi","doi":"10.1109/ICAST55766.2022.10039594","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039594","url":null,"abstract":"The intelligent models is used for prediction of diseases as well as creation of model that helps doctor to prevent spreading of disease globally is increased day by day. When a disease spreads rapidly in a short period of time in a specific area, it is called an epidemic outbreak. An outbreak might start in a single community or spread across multiple countries. It might last anywhere from a few days to several years. PHO (Public health organizations) are taking preventative efforts to stop the disease from spreading besides that they are highly benefited from accurate prediction of infectious disease. The emergence of big data in the sectors of health and biomedicine, precise data analysis aids early disease identification and better patient treatment. It is now increasingly viable to use massive computing power to predict and manage outbreaks. Our goal is to investigate and determine how outbreaks spread in villages and suburbs where medical care may be limited. A machine learning model is required to forecast epidemic dynamics and identify where the next outbreak is most likely to occur. Because these are important features that contribute subtly to the dynamics of the disease epidemic, our method considers the climate, geography, and distribution of population in impacted region. Our approach will assist health authorities in taking the necessary steps to guarantee that there are sufficient resources to fulfil demand and, if feasible, to prevent epidemics from arising.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742660","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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