Vehicular ad-hoc networks (VANETs) offer a vast number of applications without any support from fixed infrastructure. These applications forward messages in a multi-hop fashion. Designing an efficient routing protocol for all VANET applications is very hard. Hence a survey on routing protocols based on various parameters of VANET is a necessary issue in vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (IVC) communication. This paper gives a brief overview of different routing algorithms in VANET along with major classifications. The protocols are also compared based on their essential characteristics and tabulated.
Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.
Brain tumor image classification is one of the predominant tasks of brain image processing. The three-class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight-layer average-pooling convolutional neural network to address three-class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N-adam optimizer with a sparse-categorical crossentropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state-of-the-art models with 97.42% accuracy and takes lesser computation time than its competitive models.
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