Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data
<p dir="ltr">The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are acc...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2020
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| _version_ | 1864513561205669888 |
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| author | Rana M. Amir Latif (16864170) |
| author2 | Samir Brahim Brahim (16864173) Saqib Saeed (16864176) Laiqa Binte Imran (16864179) Mazhar Sadiq (16864182) Muhammad Farhan (4454434) |
| author2_role | author author author author author |
| author_facet | Rana M. Amir Latif (16864170) Samir Brahim Brahim (16864173) Saqib Saeed (16864176) Laiqa Binte Imran (16864179) Mazhar Sadiq (16864182) Muhammad Farhan (4454434) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rana M. Amir Latif (16864170) Samir Brahim Brahim (16864173) Saqib Saeed (16864176) Laiqa Binte Imran (16864179) Mazhar Sadiq (16864182) Muhammad Farhan (4454434) |
| dc.date.none.fl_str_mv | 2020-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2019.2963590 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Integration_of_Google_Play_Content_and_Frost_Prediction_Using_CNN_Scalable_IoT_Framework_for_Big_Data/24006741 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Earth sciences Atmospheric sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Machine learning Agriculture Production Temperature sensors Meteorology Temperature distribution CNN Frost event Ensemble Prediction Google Play store Scraping Measurement Analysis |
| dc.title.none.fl_str_mv | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2019.2963590" target="_blank">https://dx.doi.org/10.1109/access.2019.2963590</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d9fc408c470797b354d9bbc44d7f7e62 |
| identifier_str_mv | 10.1109/access.2019.2963590 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24006741 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big DataRana M. Amir Latif (16864170)Samir Brahim Brahim (16864173)Saqib Saeed (16864176)Laiqa Binte Imran (16864179)Mazhar Sadiq (16864182)Muhammad Farhan (4454434)Earth sciencesAtmospheric sciencesEngineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceData management and data scienceDistributed computing and systems softwareMachine learningAgricultureProductionTemperature sensorsGoogleMeteorologyTemperature distributionCNNFrost eventEnsemblePredictionGoogle Play storeScrapingMeasurementAnalysis<p dir="ltr">The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2019.2963590" target="_blank">https://dx.doi.org/10.1109/access.2019.2963590</a></p>2020-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2019.2963590https://figshare.com/articles/journal_contribution/Integration_of_Google_Play_Content_and_Frost_Prediction_Using_CNN_Scalable_IoT_Framework_for_Big_Data/24006741CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240067412020-01-01T00:00:00Z |
| spellingShingle | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data Rana M. Amir Latif (16864170) Earth sciences Atmospheric sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Machine learning Agriculture Production Temperature sensors Meteorology Temperature distribution CNN Frost event Ensemble Prediction Google Play store Scraping Measurement Analysis |
| status_str | publishedVersion |
| title | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| title_full | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| title_fullStr | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| title_full_unstemmed | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| title_short | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| title_sort | Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data |
| topic | Earth sciences Atmospheric sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Machine learning Agriculture Production Temperature sensors Meteorology Temperature distribution CNN Frost event Ensemble Prediction Google Play store Scraping Measurement Analysis |