Brain cancer importance emanates from the importance of the brain as an organ and its functions. It has a great effect on the whole human body. Identification brain cancer according to its type, it refers to a multiclass classification problem in the machine learning world. In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances. In this research, a Haar Discrete Wavelet transforms hybrid with the Histogram of Oriented Gradients (HDWT-HOG) features descriptors are proposed by the local gradients in MR image as shape information. The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features. This reduction doesn't affect the system performance but it saves time in the classification phase. The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.

Feature extraction is a very important and crucial stage in recognition system. It has been widely used in object recognition, image content analysis and many other applications. Feature extraction is the best way/method to recognize images in the field of medical images. However, the selection of proper feature extraction method is equally important because the classifier output depends on the input features.This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed method consists of four main stages: preprocessing, feature extraction, feature reduction and classification, followed by evaluation. The first stage starts with noise reduction in MR images. In the second stage, the features related to MR images are obtained using Gabor filter. In the third stage, the features of MR images are reduced to the more essential features using kernel linear discriminator analysis (KLDA). In the last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. Whereas the first classifier is based on Support Vector Machine (SVM), the second classifier is based on K-Nearest Neighbor (KNN) on Euclidean distance. Classification accuracies are 100% and 96.3% for SVM and KNN classifiers respectively. The result shows that the proposed methodologies are robust and effective compared with other existing technologies in classification of MRI tumor brain.

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Separating a reentry vehicle into warhead and body is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the body, whose radar cross section is larger, and ignore the warhead which is the most important part of the reentry vehicle. The aerodynamic Coefficients models play an essential part in the simulation and the analysis of the supersonic and hypersonic of the ballistic missiles, especially in the dynamic trajectory planning. This paper builds the aerodynamic coefficients models by the nonlinear least square method based on the separable warhead re-entry vehicle using lebedev aerodynamic calculations. The lift and drag coefficients models can be expressed with the polynomial and the exponential function. So the models fit the aero characteristic well and can be used in practical design and simulation as a reference.