Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification

Robust Face Recognition Against Soft-errors Using a Cross-layer Approach

Recently, soft-errors, temporary bit toggles in memory systems, have become increasingly important. Although soft-errors are not critical to the stability of recognition systems or multimedia systems, they can significantly degrade the system performance. Considering these facts, in this paper, we propose a novel method for robust face recognition against soft-errors using a cross layer approach. To attenuate the effect of soft-errors in the face recognition system, they are detected in the embedded system layer by using a parity bit checker and compensated in the application layer by using a mean face. We present the soft-error detection module for face recognition and the compensation module based on the mean face of the facial images. Simulation results show that the proposed system effectively compensates for the performance degradation due to soft errors and improves the performance by 2.11 % in case of the Yale database and by 10.43 % in case of the ORL database on average as compared to that with the soft-errors induced

Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

Image processing is a rapidly growing research area of computer science and remains as a challenging problem within the computer vision fields. For the classification of flower images, the problem is mainly due to the huge similarities in terms of colour and texture. The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. The flower image is extracted through Region of Interest (ROI) in which texture and colour are emphasized in this study. In this research, a total of 960 images were extracted from 16 types of flowers. Each ROI was represented by three colour attributes (Hue, Saturation, and Value) and four textures attribute (Contrast, Correlation, Energy and Homogeneity). In training and testing phases, experiments were carried out to observe the classification performance of Neural Networks with duplication of difficult pattern to learn (referred to as DOUBLE) as this could possibly explain as to why some flower images were difficult to learn by classifiers. Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. The findings from this study indicate that Neural Network with DOUBLE data set produces highest performance compared to Logistic Regression and Decision Tree. Therefore, NN has been potential in building Malaysian blooming flower model. Future studies can be focused on increasing the sample size and ROI thus may lead to a higher percentage of accuracy. Nevertheless, the developed flower model can be used as part of the Malaysian Blooming Flower recognition system in the future where the colours and texture are needed in the flower identification process

Digital image classification for Malaysian blooming flower

Digital image processing is a rapidly growing area of computer science since it was introduced and developed in the 1960’s. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification.Colour of the flower plays very important role in image classification since it gives additional information in terms of segmentation and recognition. On the other hand,Texture can be used to facilitate image-based retrieval system normally and it is encoded by a number of descriptors, which represented by a set of statistical measures such as gray-level co-occurrence matrix (GLCM) and Law’s Order approach. This study addresses the application of NN and on image processing particularly for understanding flower image features.For predictive analysis, two techniques have been used namely, Neural Network (NN) and Logistic regression. The study shows that NN obtains the higher percentage of accuracy among two techniques.The MLP is trained by 1800 flower's dataset to classify 30 kinds of flower's type