Cooperative Attention-Based Learning between Diverse Data Sources
Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/5/240 |
| _version_ | 1797601387896373248 |
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| author | Harshit Srivastava Ravi Sankar |
| author_facet | Harshit Srivastava Ravi Sankar |
| author_sort | Harshit Srivastava |
| collection | DOAJ |
| description | Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula> and can dynamically optimize the decision model using a control algorithm with a complexity of order <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo stretchy="false">(</mo><msup><mi>n</mi><mn>2</mn></msup><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>. |
| first_indexed | 2024-03-11T04:00:19Z |
| format | Article |
| id | doaj.art-1da55f1488304fee8d3124b7d5d7783b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T04:00:19Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-1da55f1488304fee8d3124b7d5d7783b2023-11-18T00:08:43ZengMDPI AGAlgorithms1999-48932023-05-0116524010.3390/a16050240Cooperative Attention-Based Learning between Diverse Data SourcesHarshit Srivastava0Ravi Sankar1iCONS Lab, Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USAiCONS Lab, Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USACooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula> and can dynamically optimize the decision model using a control algorithm with a complexity of order <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo stretchy="false">(</mo><msup><mi>n</mi><mn>2</mn></msup><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>.https://www.mdpi.com/1999-4893/16/5/240social data analysiscooperative attentioncooperative learningCOVID-19tracking |
| spellingShingle | Harshit Srivastava Ravi Sankar Cooperative Attention-Based Learning between Diverse Data Sources Algorithms social data analysis cooperative attention cooperative learning COVID-19 tracking |
| title | Cooperative Attention-Based Learning between Diverse Data Sources |
| title_full | Cooperative Attention-Based Learning between Diverse Data Sources |
| title_fullStr | Cooperative Attention-Based Learning between Diverse Data Sources |
| title_full_unstemmed | Cooperative Attention-Based Learning between Diverse Data Sources |
| title_short | Cooperative Attention-Based Learning between Diverse Data Sources |
| title_sort | cooperative attention based learning between diverse data sources |
| topic | social data analysis cooperative attention cooperative learning COVID-19 tracking |
| url | https://www.mdpi.com/1999-4893/16/5/240 |
| work_keys_str_mv | AT harshitsrivastava cooperativeattentionbasedlearningbetweendiversedatasources AT ravisankar cooperativeattentionbasedlearningbetweendiversedatasources |