A selective forwarding attack is one of the most crucial security problems in MANET. Usually such attacker degrades the network performance in terms of packet loss rate, collision, and overhead. Designing MANET that can work reliably even in the presence of inside packet drop attackers is really challenging. In order to overcome these issues, we have proposed an enhanced Ant based Defense Mechanism for Selective Forwarding Attack in MANET. First, we have implemented S-ACK scheme to transmit the secure acknowledgement. To detect attackers, a trust model is designed. The Forward ant agents transmit back the digitally signed S ACK through the Backward Ant agent to detect any selective forwarding attack against any source node. Also a Challenge and Monitoring packet is transmitted by source node to monitor the neighbor node and the verification table updated by an ant agent.
Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process. The proposed MDP extracts both intensity and edge information and creates the feature vector that increases the classification accuracy during caries detection.
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