P. Aruna

An investigation of neuro-fuzzy systems in psychosomatic disorders

A neuro-fuzzy model for diagnosis of psychosomatic disorders is proposed in this paper. The symptoms and signs are collected from the patients through oral interview. For the linguistic nature of patients inputs, an artificial domain is created and fuzzy membership values are defined. The fuzzy values are fed as inputs to feedforward multilayer neural network. The network is trained using Backpropagation training algorithm. The trained model is tested with new patients symptoms and signs. Further, the performance of the diagnosing capability is compared with medical expert. The performance of the model is also compared with probability model based on Bayesian Belief Network and statistical model using Linear Discriminant analysis

SVM and Neural Network based Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, two models like Probabilistic Neural network (PNN) and Support vector machine (SVM) are described and their performances are compared. Experimental results show that PNN has an accuracy of 89.60% and SVM has an accuracy of 97.608 %. This infers that the SVM model outperforms the other model.

Automated diagnosis of Age-related macular degeneration from color retinal fundus images

Automated image processing has the potential to assist in the early detection of Age-related macular degeneration, by detecting changes in blood vessel and patterns in the retina. Age-related macular degeneration (ARMD) is gradual loss of vision by oxidation of macula and most common cause of irreversible vision loss. The ARMD can be classified into 1. Dry macular degeneration 2. Wet macular degeneration. The purpose of this paper is to diagnose the retinal disease ARMD and to classify the two types. The extent of the disease spread in the retina can be identified by extracting the features of the retina. Detection of ARMD disease is done using Probabilistic Neural Network (PNN) method and the two types are classified and diagnosed successfully. The results showed a sensitivity of 94.00% for the classifier and specificity of 95.00%.

Probability based document clustering and image clustering using content-based image retrieval

Clustering of related or similar objects has long been regarded as a potentially useful contribution of helping users to navigate an information space such as a document collection. Many clustering algorithms and techniques have been developed and implemented but as the sizes of document collections have grown these techniques have not been scaled to large collections because of their computational overhead. To solve this problem, the proposed system concentrates on an interactive text clustering methodology, probability based topic oriented and semi-supervised document clustering. Recently, as web and various documents contain both text and large number of images, the proposed system concentrates on content-based image retrieval (CBIR) for image clustering to give additional effect to the document clustering approach. It suggests two kinds of indexing keys, major colour sets (MCS) and distribution block signature (DBS) to prune away the irrelevant images to given query image. Major colour sets are related with colour information while distribution block signatures are related with spatial information. After successively applying these filters to a large database, only small amount of high potential candidates that are somewhat similar to that of query image are identified. Then, the system uses quad modelling method (QM) to set the initial weight of two-dimensional cells in query image according to each major colour and retrieve more similar images through similarity association function associated with the weights. The proposed system evaluates the system efficiency by implementing and testing the clustering results with Dbscan and K-means clustering algorithms. Experiment shows that the proposed document clustering algorithm performs with an average efficiency of 94.4% for various document categories.

Diagnosis of gastrointestinal disorders using DIAGNET

Abstract A new neural network model called DIAGNET is proposed in this paper for diagnosing gastrointestinal disorders. DIAGNET is a combination of Backpropagation neural network (BPNN) and radial basis functions neural network (RBFNN). The symptoms and signs are collected from the patients through oral interview. For the linguistic nature of patient’s inputs, an artificial domain is created and fuzzy membership values are defined. The fuzzy values are fed as inputs to the DIAGNET and trained for diagnosing the diseases related to gastrointestinal disorders. The trained model is tested with new patient’s symptoms and signs. The performance of the DIAGNET is compared with the existing Backpropagation neural network and Radial basis functions neural network models. Sensitivity, Specificity and Receiver-Operating Characteristics (ROC) are used as the indicators for testing the accuracy of the models which predict the gastrointestinal disorder diseases. The results suggest that the DIAGNET can be better solution for complex, nonlinear medical decision support systems.

Automated diagnosis of age-related macular degeneration using machine learning techniques

Age-related macular ARM degeneration is an eye disease, that gradually degrades the macula, a part of the retina, which is responsible for central vision. It occurs in one of the two types, dry and wet age-related macular degeneration. The purpose of this paper is to diagnose the retinal disease age-related macular degeneration. An automated approach is proposed to help in the early detection of age-related macular degeneration using three models and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. Detection of age-related macular degeneration disease has been done using probabilistic neural network PNN, Bayesian classification and support vector machine SVM and the two types of age-related macular degeneration are classified and diagnosed successfully. The results show that SVM achieves a higher performance measure than probabilistic neural network and Bayes classification.

Layered Model to Estimate Effort, Performance and Cost of the Software Projects

The Authors have proposed a model that first captures the fundamentals of software metrics in the phase 1 consisting of three primitive primary software engineering metrics; they are personmonths (PM), function-points (FP), and lines of code (LOC). The phase 2 consists of the proposed function point which is obtained by grouping the adjustment factors to simplify the process of adjustment and to ensure more consistency in the adjustments. In the proposed method fuzzy logic is used for quantifying the quality of requirements and is added as one of the adjustment factor, thus a fuzzy based approach for the Enhanced General System Characteristics to Estimate Effort of the Software Projects using productivity has been obtained. The phase 3 takes the calculated function point from our work and is given as input to the static single variable model (i.e. to the Intermediate COCOMO and COCOMO II) for cost estimation. The Authors have tailored the cost factors in intermediate COCOMO and both; cost and scale factors are tailored in COCOMO II to suite to the individual development environment, which is very important for the accuracy of the cost estimates. The software performance indicators are project duration, schedule predictability, requirements completion ratio and post-release defect density, are also measured for the software projects in my work. A comparative study for effort, performance measurement and cost estimation of the software project is done between the existing model and the authors proposed work. Thus our work analyzes the interactional process through which the estimation tasks were collectively accomplished.

Analysis of Sparse Representation and PCA fusion methods for Iris and Palmprint biometric features

Multimodal Biometric System using multiple sources of information is proposed. In this paper multimodal biometric images such as palmprint, and iris are extracted individually and fused together using sparse fusion mechanism and PCA fusion mechanism. The images are pre-processed for feature extraction. CASIA database is chosen for the biometric images. All the images are 8 bit gray-level JPEG image. In this process QABF, VIF, MI metrics are applied to the fused template to record the performance variation of fusion mechanism.

PSO Watermarking Model for Multimodal Biometric System

Multimodal Biometric Watermarking System using multiple sources of information for establishing the individuality has been widely recognized, computational models for multimodal biometrics recognition have only recently got attention. In this paper multimodal biometric images such as fingerprint, palmprint, and iris are extracted individually and are fused together using Average, Minimum and Maximum fusion mechanism. The fused template is then watermarked using the PSO watermarking system. The biometric features used here are fingerprint, iris and palmprint. The image quality is measured by using various metrics such as Peak Signal Noise ratio(PSNR), Normalized Absolute Error(NAE) and Normalized Cross Correlation(NCC). CASIA database is chosen for the biometric images. All the images are 8 bit gray-level JPEG image with the resolution of 320*280.

Color Image Compression Based on Feature Extraction

This paper proposes an efficient compression scheme for compressing RGB color images based on feature extraction with the combination of DCT transform and the Peano-Hilbert Scan. The RGB color image is converted into YCbCr in order to extract the color and the texture features. The DCT transform is applied to the extracted luma and the chroma component to reduce the redundancy. Peano-Hilbert scanning is performed over the DCT matrix which increases the PSNR of the reconstructed image. The proposed bi-mode quantization is applied to preserve the image quality. The quantized coefficients are encoded using the lossless Huffman encoding. The efficiency of the proposed compression scheme has been implemented and compared with other existing compression techniques. The proposed compression method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods. Thus, Compression based on feature extraction contributes to better performance.