| Title |
A deep transfer learning model for green environment security analysis in smart city |
| ID_Doc |
41445 |
| Authors |
Sahu, M; Dash, R; Mishra, SK; Humayun, M; Alfayad, M; Assiri, M |
| Title |
A deep transfer learning model for green environment security analysis in smart city |
| Year |
2024 |
| Published |
Journal Of King Saud University-Computer And Information Sciences, 36, 1 |
| DOI |
10.1016/j.jksuci.2024.101921 |
| Abstract |
Green environmental security refers to the state of human -environment interactions that include reducing resource shortages, pollution, and biological dangers that can cause societal disorder. In IoT-enabled smart cities, due to the advancement of technologies, sensors and actuators collect vast quantities of data that are analyzed to extract potentially useful information. However, due to the noise and diversity of the data generated, only a small portion of the massive data collected from smart cities is used. In sustainable Land Use and Land Cover (LULC) management, environmental deterioration resulting from improper land usage in the digital ecosystem is a global issue that has garnered attention. The deep learning techniques of AI are recognized for their capacity to manage vast amounts of erroneous and unstructured data. In this paper, we propose a morphologically augmented finetuned DenseNet-121(MAFDN) LULC classification model to automate the categorization of high spatial resolution scene images for environmental conservation. This work includes an augmentation process (i.e. erosion, dilation, blurring, and contrast enhancement operations) to extract spatial patterns and enlarge the training size of the dataset. A few state-of-the-art techniques are incorporated for contrasting the efficacy of the proposed approach. This facilitates green resource management and personalized provision of services. |
| Author Keywords |
Deep convolutional neural networks; Digital ecosystem; Environmental green security; Remote sensing data |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001168185000001 |
| WoS Category |
Computer Science, Information Systems |
| Research Area |
Computer Science |
| PDF |
https://doi.org/10.1016/j.jksuci.2024.101921
|