Diagnosing Heart Diseases For Type 2 Diabetic Patients By Cascading The Data Mining Techniques

Motivated by the world-wide increasing mortality of heart disease patients each year, researchers have been using data mining techniques to help health care professionals in the diagnosis of heart disease. Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. To review the primary prevention studies that focused on the development, validation and impact assessment of a heart disease risk model, scores or rules that can be applied to patients with type 2 diabetes. Efficient predictive modeling is required for medical researchers and practitioners. Attribute values measurement using entropy and information gain parameters. This study proposes Hybrid type 2 diabetes Prediction Model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data in which incorrectly classified instances are removed and. pattern extracted from original data. Support Vector Machine (SVM) algorithm is used to build the final classifier model by using the k-fold cross-validation method. The aim of this paper is to highlight all the techniques and risk factors that are considered for diagnosis of heart disease. This paper will provide a roadmap for researchers seeking to understand existing automated diagnosis of heart disease

Constructing PCA baseline algorithms to reevaluate ICA-based face-recognition performance

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Facial expressions recognition based on dimensionality reduction techniques

Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. The traditional text based search and retrieval has its own limitations and hence we move to a facial expressions images are search and retrieval system. In this paper we present a facial expression retrieval system that takes an image as the input query and retrieves images based on image content. Face recognition system is recognizing based on dimensionality reduction derived image features. Facial expressions recognition is the application of computer vision to the image retrieval problem. In this recognition context might refer colours, shapes, textures, or any other information that can be derived from the image itself

A Penalty-projection based Efficient and Accurate Stochastic Collocation Method for Magnetohydrodynamic Flows

We propose, analyze, and test a penalty projection-based efficient and accurate algorithm for the Uncertainty Quantification (UQ) of the time-dependent Magnetohydrodynamic (MHD) flow problems in convection-dominated regimes. The algorithm uses the Els\"asser variables formulation and discrete Hodge decomposition to decouple the stochastic MHD system into four sub-problems (at each time-step for each realization) which are much easier to solve than solving the coupled saddle point problems. Each of the sub-problems is designed in a sophisticated way so that at each time-step the system matrix remains the same for all the realizations but with different right-hand-side vectors which allows saving a huge amount of computer memory and computational time. Moreover, the scheme is equipped with ensemble eddy-viscosity and grad-div stabilization terms. The stability of the algorithm is proven rigorously. We prove that the proposed scheme converges to an equivalent non-projection-based coupled MHD scheme for large grad-div stabilization parameter values. We examine how Stochastic Collocation Methods (SCMs) can be combined with the proposed penalty projection UQ algorithm. Finally, a series of numerical experiments are given which verify the predicted convergence rates, show the algorithm's performance on benchmark channel flow over a rectangular step, and a regularized lid-driven cavity problem with high random Reynolds number and magnetic Reynolds number.Comment: 28 pages, 13 figure

Face Recognition Through Regret Minimization.

Face Recognition is an important problem for Artificial Intelligence Researchers, with applications to law enforcement, medicine and entertainment. Many different approaches to the problem have been suggested most approaches can be categorized as being either Holistic or Local. Recently, local approaches have shown some gains. This thesis presents a system for embedding a holistic algorithm into a local framework. The system proposed builds on the concept of Regional Voting, to create Weighted Regional Voting which divides the face images to be classified into regions, performs classification on each region, and finds the final classification through a weighted majority vote on the regions. Three different weighting schemes taken from the field of Regret Minimization are suggested, and their results compared. Weighted Regional Voting is shown to improve upon unweighted Regional Voting in every case, and to outperform or equal many modern face recognition algorithms. --P. ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b174112

Regional displacement matching scheme for LBP based face recognition.

In face recognition, alignment of the face images has been a known open issue. This thesis proposes a displacement based local aligning scheme to construct a structural descriptive image template for comparison. To conquer the registration difficulties caused by the non-rigidity of human face images, a block displacement strategy is introduced to apply the regional voting scheme to face recognition field. Local Binary Pattern (LBP) is adopted to construct this block LBP displacement-based local matching approach, we name LBP-DLMA. Experiments are performed and have demonstrated the outstanding performances of this LBP-DLMA over the original LBP approach. It is expected and shown by experiments that this approach applies to both large and small sized images, and that it also applies to descriptor approaches other than LBP. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b189084