Performance Comparison of Network Intrusion Detection System Based on Different Pre-processing Methods and Deep Neural Network

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

“…These features are specified by the classes and dimensions of the histogram ½1 × 96 by the presence of histogram features. Features can be set as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 0 ; 1 1 6 ; 2 5 4 H ¼ fh 1 ; h 2 ; h 3 ; : : : : : : : : : : : : : : : h 96 g; (10) where h includes the features of the histogram. R e t r a c t e d…”

“…9 Deeper neural networks (DNNs) can be trained with a process that follows this, as they are fed into adjacent layers (DBN-DNN). 10,11 It delves into the classification of brain tumors, the two stages of which are known as progressive multifocal leukoencephalopathy and diffuse astrocytoma. The tumor present in a complete image is passed to the segmentation module for processing, which segments a tumor to reduce processing time and make the process simpler.…”

“…Brain tumor detection can be further divided into supervised and unsupervised approaches. 10 Classification refers to the process of assigning distinct classes to individual observations. Dataset classification uses classifiers, such as random forests and SVMs.…”

The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison

“…Figure 1 The public structure of a convolutional neural network with an example of inserting images containing an object (bird) and how the type of object in the object is determined. A convolutional neural network has two main parts: the first is a convolution/pooling mechanism that divides the image into features and analyses them, and the second is a fully connected layer that takes the convolution/pooling output and guesses the best tag to describe the image [60][61][62][63][64][65][66][67]. Figure 2 illustrates how deep learning is utilised to classify images of COVID-19 patients [68].…”