Tchebichef Moment Based Hilbert Scan for Image Compression

Image compression is now essential for applications such as transmission and storage in data base, so we need to compress a vast amount of information whereas, the compressed ratio and quality of compressed image must be enhanced, for this reason, this paper develop a new algorithm that used a discrete orthogonal Tchebichef moment based Hilbert curve for image compression. The analyzed image was divided into 8×8 image sub-blocks, the Tchebichef moment has been applied to each one, and then the transformed coefficients 8×8 sub-block shall be reordered in Hilbert scan into a linear array, at this step Huffman coding is implemented. Experimental results show that this algorithm improves the coding efficiency on the one hand; and on the other hand the quality of reconstructed image is also not significantly decreased. Keywords: Huffman Coding, Tchebichef Moment Transforms, Orthogonal Moment Functions, Hilbert, zigzag scan

Face Recognition Using Fuzzy Moments Discriminant Analysis

In this work, an enhanced feature extraction method for holistic face recognition approach of gray intensity still image, namely Fuzzy Moment Discriminant Analysis is used. Which is first, based on Pseudo-Zernike Moments to extract dominant and significant features for each image of enrolled person, then the dimensionality of the moments features vectors is further reduced into discriminant moment features vectors using Linear Discriminant Analysis method, for these vectors the membership degrees in each class have been computed using Fuzzy K-Nearest Neighbor, after that, the membership degrees have been incorporated into the redefinition of the between-classes and within-classes scatter matrices to obtain final features vectors of  known persons. The test image is then compared with the faces enrollment images so that the face which has the minimum Euclidean distance with the test image is labeled with the identity of that image. Keyword: Zernike Moments, LDA, Fuzzy K-Nearest Neighbor

Secure Heart Disease Classification System Based on Three Pass Protocol and Machine Learning

Heart disease is one of the worst life-threatening conditions. Correct and early diagnosis of this disease is crucial for saving patients’ life and avoiding other complications. On the other hand, keeping the patient’s data, diagnosis process, and treatment plan secured is equally important to the defactomedical procedure. This research proposes a system that is consisting of two phases: security provision and patients’ condition diagnosis. Typically, the first phase exercises a security protocol, called  three-pass protocol, to ensure that the people who can access the patient's information are authorized. In order to obtain a high accuracy level in the diagnosis process, artificial intelligence with machine learning methods are employed in the later phase. The proposed system relies on a data set which includes a number of vital indicators, by which the patient's status can be classified as having heart disease or not. The KNN algorithm and the random forest tree algorithm are applied to carry out the classification task. The accuracy scale results reveals that the randomforest tree algorithm (99%) gave higher accuracy than KNN (97%)