Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.     

Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images

Heart is an important and hardest working muscular organ of the human body. Inability of the heart to restore normal perfusion to the entire body refers to cardiac failure, which then with symptoms results in manifestation of congestive heart failure (CHF). Impairment in systolic function associated with chronic dilation of left ventricle is referred as dilated cardiomyopathy (DCM). The clinical examination, surface electrocardiogram (ECG), chest X-ray, blood markers and echocardiography play major role in the diagnosis of CHF. Though the ECG manifests chamber enlargement changes, it does not possess sensitive marker for the diagnosis of DCM, whereas echocardiographic assessment can effectively reveal the presence of asymptomatic DCM. This work proposes an automated screening method for classifying normal and CHF echocardiographic images affected due to DCM using variational mode decomposition technique. The texture features are extracted from variational mode decomposed image. These features are selected using particle swarm optimization and classified using support vector machine classifier with different kernel functions. We have validated our experiment using 300 four-chamber echocardiography images (150: normal, 150: CHF) obtained from 50 normal and 50 CHF patients. Our proposed approach yielded maximum average accuracy, sensitivity and specificity of 99.33%, 98.66% and 100%, respectively, using ten features. Thus, the developed diagnosis system can effectively detect CHF in its early stage using ultrasound images and aid the clinicians in their diagnosis.