A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services
A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate res...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10075673/ |
| _version_ | 1797861366788259840 |
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| author | Nafees Zaman Ahmad Abu Saiid Md Arafatur Rahman Shavan Askar Jasni Mohamad Zain |
| author_facet | Nafees Zaman Ahmad Abu Saiid Md Arafatur Rahman Shavan Askar Jasni Mohamad Zain |
| author_sort | Nafees Zaman |
| collection | DOAJ |
| description | A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today’s technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in pre- and post-flood situations. Labeling data from the proposed framework generates a support vector machine-based classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority. |
| first_indexed | 2024-04-09T22:01:11Z |
| format | Article |
| id | doaj.art-9fdccfe1dd6a4b0d8b5edc5394ae6176 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T22:01:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9fdccfe1dd6a4b0d8b5edc5394ae61762023-03-23T23:00:12ZengIEEEIEEE Access2169-35362023-01-0111270862709810.1109/ACCESS.2023.325742910075673A Data-Intelligent Scheme Toward Smart Rescue and Micro-ServicesNafees Zaman0https://orcid.org/0000-0002-3203-5684Ahmad Abu Saiid1Md Arafatur Rahman2https://orcid.org/0000-0002-8221-6168Shavan Askar3https://orcid.org/0000-0002-9279-8181Jasni Mohamad Zain4Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaÉcole Polytechnique de Bruxelles, Université Libre de Bruxelles, Brussels, BelgiumSchool of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, U.K.Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, IraqInstitute for Big Data Analytics and Artificial Intelligence (IBDAAI), Komplek Al-Khawarizmi, Universiti Teknologi MARA, Shah Alam, Selangor, MalaysiaA considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today’s technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in pre- and post-flood situations. Labeling data from the proposed framework generates a support vector machine-based classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority.https://ieeexplore.ieee.org/document/10075673/Data-intelligentmicro-servicesgeographic information systemrisk mapK-means clusteringPCA |
| spellingShingle | Nafees Zaman Ahmad Abu Saiid Md Arafatur Rahman Shavan Askar Jasni Mohamad Zain A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services IEEE Access Data-intelligent micro-services geographic information system risk map K-means clustering PCA |
| title | A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services |
| title_full | A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services |
| title_fullStr | A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services |
| title_full_unstemmed | A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services |
| title_short | A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services |
| title_sort | data intelligent scheme toward smart rescue and micro services |
| topic | Data-intelligent micro-services geographic information system risk map K-means clustering PCA |
| url | https://ieeexplore.ieee.org/document/10075673/ |
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