An architectural framework for information integration using machine learning approaches for smart city security profiling
In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2020-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720965473 |
| _version_ | 1797765390678360064 |
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| author | Adnan Abid Ansar Abbas Adel Khelifi Muhammad Shoaib Farooq Razi Iqbal Uzma Farooq |
| author_facet | Adnan Abid Ansar Abbas Adel Khelifi Muhammad Shoaib Farooq Razi Iqbal Uzma Farooq |
| author_sort | Adnan Abid |
| collection | DOAJ |
| description | In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system. |
| first_indexed | 2024-03-12T20:09:29Z |
| format | Article |
| id | doaj.art-32d7aed0a309477c8d20848e59c444aa |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T20:09:29Z |
| publishDate | 2020-10-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-32d7aed0a309477c8d20848e59c444aa2023-08-02T01:46:50ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772020-10-011610.1177/1550147720965473An architectural framework for information integration using machine learning approaches for smart city security profilingAdnan Abid0Ansar Abbas1Adel Khelifi2Muhammad Shoaib Farooq3Razi Iqbal4Uzma Farooq5Department of Computer Science, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, University of Management and Technology, Lahore, PakistanAbu Dhabi University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, University of Management and Technology, Lahore, PakistanDundas Data Visualization, Toronto, ON, CanadaDepartment of Computer Science, University of Management and Technology, Lahore, PakistanIn the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.https://doi.org/10.1177/1550147720965473 |
| spellingShingle | Adnan Abid Ansar Abbas Adel Khelifi Muhammad Shoaib Farooq Razi Iqbal Uzma Farooq An architectural framework for information integration using machine learning approaches for smart city security profiling International Journal of Distributed Sensor Networks |
| title | An architectural framework for information integration using machine learning approaches for smart city security profiling |
| title_full | An architectural framework for information integration using machine learning approaches for smart city security profiling |
| title_fullStr | An architectural framework for information integration using machine learning approaches for smart city security profiling |
| title_full_unstemmed | An architectural framework for information integration using machine learning approaches for smart city security profiling |
| title_short | An architectural framework for information integration using machine learning approaches for smart city security profiling |
| title_sort | architectural framework for information integration using machine learning approaches for smart city security profiling |
| url | https://doi.org/10.1177/1550147720965473 |
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