The performance of any machine learning algorithm heavily depends on the quality and quantity of the training data. Machine learning algorithms, driven by training data can accurately predict and produce the right outcome when trained through enough amount of quality data. In the medical applications, being more critical, the accuracy is of utmost importance. Obtaining medical imaging data, enough to train machine learning algorithm is difficult due to a variety of reasons. An effort has been made to produce an augmented dental radiography dataset to train machine learning algorithms. 116 panoramic dental radiographs have been manually segmented for each tooth producing 32 classes of teeth. Out of 3712 images of individual tooth, 2910 were used for machine learning through general augmentation methods that include rotation, intensity transformation and flipping of the images, creating a massive dataset of 5.12 million unique images. The dataset is labeled and classified into 32 classes. This dataset can be used to train deep convolutional neural networks to perform classification and segmentation of teeth in x-rays, Cone-Beam CT scans and other radiographs. We retrained AlexNet on a subset of 80,000 images of the entire dataset and obtained classification accuracy of 98.88% on 10 classes. The retraining on original dataset yielded 88.31%. The result is evident of nearly a 10% increase in the performance of the classifier trained on the augmented dataset. The training and validation datasets include teeth affected with metal objects. The manually segmented dataset can be used as a benchmark to evaluate the performance of machine learning algorithms for performing tooth segmentation and tooth classification.
-AODV and DSR are normally taken as a standard in reactive routing protocols for Mobile Ad-hoc Network (MANETs). Both of these protocols are widely used in different applications of MANET because of their simple design and better performance. AODV does not provide optimal results in the scenarios where we have heavy traffic with large number of connections and higher routing load. In this paper, we have introduced a novel idea of "Reliability Factor" to determine reliable links between the intermediate nodes; based on this factor a reactive routing protocol is proposed, the simulation results of Reliability Factor Based Routing Protocol (RFBRP) show that it outperforms AODV and SP-AODV in terms of better packet delivery fraction, routing load and end-to-end delay.
Over the past decade, there has been a rapidly rising trend of malware (ransomware) that limits user access by encrypting the data and demanding the ransom against the decryption key. In most cases, such encryption may lead to a permanent data loss. In order to prevent this unwanted encryption, we propose a method based on Moving Target Defense (MTD) approach. Our method is based on the alteration of the attack surface to reduce the attack success ratio. We have used multiple layers of MTD. The first layer generates random extensions that hide the existing known file extensions. This will protect user files against those ransomware variants which encrypt files having some specific extensions. Our second layer of protection uses event-based MTD in which tasks are scheduled to change file extensions at the occurrence of specific events which mostly occur due to the execution of ransomware in the system. As a result of our proposed method, we have successfully protected user files against well-known ransomware variants such as WannaCry,
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