Support vector machines (SVMs) have proved to be promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. The critical SVMs problem is to select appropriate kernels as the performance of SVMs depends on this choice. In this paper, a set of new Hermite kernel functions is ... Read more

In this paper, a novel quantum classification algorithm that is based on competitive learning is presented to classify an input pattern that results from the failures of some sensors. As long as an incomplete pattern is presented to our model, the proposed algorithm performs the competitions between the neurons by applying some unitary transformations then measures the degree of entanglement ... Read more

In this paper, we proposed an improved high efficient Haar wavelets (HEHW) algorithm to improve the quality of image compression ratio (CR) rate and peak signal to noise ratio for medical imaging. The proposed algorithm starts by partitioning the original image into 2 * 2 submatrices. Then the wavelets transform coefficients obtained by working on the submatrices instead of the ... Read more

Medical image compressions allow large volume data to be kept in an exceedingly quantity of disk and to move towards complete film-less imaging. Also, the image compression process minimizes the time while download or sent the image from the internet. For that, several wavelets are used for image compression, which composed of multi-resolution based function. The choice of the suitable ... Read more