Affiliations:
Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
Correspondence:
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Corresponding author. Samar M. Alqhtani, Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. E-mail: smalqhtani@nu.edu.sa.
Abstract: Disasters occur due to naturally stirring events like earthquake, floods, tsunamis, storms hurricanes, wildfire, and other geologic measures. Social media fake image posting influence is increasing day by day regarding the natural disasters. A natural disaster can result in the death or destruction of property, as well as economic damage, the severity of which is determined by the resilience of the affected population and the infrastructure available. Many researchers applied different machine learning approaches to detect and classification of natural disaster types, but these algorithms fail to identify fake labelling occurs on disaster events images. Furthermore, when many natural disaster events occur at a time then these systems couldn’t handle the classification process and fake labelling of images. Therefore, to tackle this problem I have proposed a FLIDND-MCN: Fake Label Image Detection of Natural Disaster types with Multi Model Convolutional Neural Network for multi-phormic natural disastrous events. The main purpose of this model is to provide accurate information regarding the multi-phormic natural disastrous events for emergency response decision making for a particular disaster. The proposed approach consists of multi models’ convolutional neural network (MMCNN) architecture. The dataset used for this purpose is publicly available and consists of 4,428 images of different natural disaster events. The evaluation of proposed model is measured in the terms of different statistical values such as sensitivity, specificity, accuracy, precision, and f1-score. The proposed model shows the accuracy value of 0.93 percent for fake label disastrous images detection which is higher as compared to the already proposed state-of-the-art models.
