Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect's body despite concealment.

No abstract

No abstract

Pancreatic cancer (PC) in the more extensive sense alludes to in excess of 277 distinct kinds of cancer sickness. Researchers have recognized distinctive phase of pancreatic cancers, showing that few quality transformations are engaged with cancer pathogenesis. These quality transformations lead to unusual cell multiplication. Therefore, in this study we propose a Computer Aided Diagnosis (CAD) system using Synergic Inception ResNet-V2, Deep convoluted neural network architecture for the identification of PC cases from publically Usable CT images that could extract PC graphical functionality to include clinical diagnosis before the pathogenic examination, saving valuable time for disease prevention. Simulation results using MATLAB is shown to illustrate that quite promising results have been obtained in terms of accuracy in detecting patients infected with BC. Accuracy of 99.23 per cent is reached using the proposed deep learning method, which is better than all other state-of-the-art approaches available in the literature. The calculation time was found to be less than the other current 22 second process. The proximity of the suggested approach to the True Positive values in the ROC curve suggests a result that is greater than the other methods. The comparative study with Inception ResNet-V2 is based on separate test and training data at a rate of 90 percent-10 percent, 80 percent-20 percent and 70 percent-30% respectively, which shows the robustness of the proposed research work. Experimental findings show the proposed reliability of the device relative to other detection approaches. The proposed CAD device is fully automated and has thus proved to be a promising additional diagnostic tool for frontline clinical physicians.

The objective of content-based image retrival (CBIR) is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time. All the proposed approaches are different, yet the research goal is to attain better accuracy in a reasonable amount of time. The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features. The second phase of the research extracts features and the association rules are formed by the proposed classification based on highly strong association rules (CHiSAR). Finally, the rule subset classifier is employed to classify between the images. The last pert of our work extracts the features from the kidney images and the association rules are reduced for better performance. The image relevance inference is performed and finally, binary and the best first search classification is employed to classify between the images.