Detection of heart murmurs based on radial wavelet neural network with Kalman learning

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance System. Street crime is increasing in recent years, which has demanded more reliable and intelligent public surveillance system. In this paper, the ability and the accuracy of an Adaptive Neuro-Fuzzy Inference System (ANFIS) was investigated for the classification of moving objects for street scene applications. The goal of this paper is to classify the moving objects prior to its communal attributes that emphasize on three major processes which are object detection, discriminative feature extraction, and classification of the target. The intended surveillance application would focus on street scene, therefore the target classes of interest are pedestrian, motorcyclist, and car. The adaptive network based on Neuro-fuzzy was independently developed for three output parameters, each of which constitute of three inputs and 27 Sugeno-rules. Extensive experimentation on significant features has been performed and the evaluation performance analysis has been quantitatively conducted on three street scene dataset, which differ in terms of background complexity. Experimental results over a public dataset and our own dataset demonstrate that the proposed technique achieves the performance of 93.1% correct classification for street scene with moving objects, with compared to the solely approaches of neural network or fuzzy.

Moving Object Detection and Classification Using Neuro-Fuzzy Approach

In this paper, we propose a radial wavelet neural network classifier for heart murmurs (pulmonary insufficiency and tricuspid insufficiency). The extended Kalman filter is used as a learning algorithm. The network inputs are dimensional features, extracted from real cardiac cycles. The proposed model is trained and tested using real heart cycles in order to show the applicability of the proposed scheme.

Intelligent classification of real heart diseases based on radial wavelet neural network

In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting them are presented. In addition, the classical neural network has been trained and tested. The results of these two learning algorithms are also presented in comparison.

Breast cancer classification with wavelet neural network

Artificially intelligent robots have become increasingly important in Industrial Technology in recent years. The key thing that robots do today is to do difficult and time-consuming activities in an efficient manner. Many components or pieces of equipment are installed in any industry. Humans handle this equipment, and they frequently keep track of it by recognizing and classifying it for further action. This process takes a long time to complete because we have to visit each and every component, but it does not require human intervention. As a result, vision systems and Intelligent Robots are now used to perform these duties. This project focuses on finding a solution to this issue. We give a thorough view of these machine learning and image processing techniques in this research, which can be used to improve the intelligence and classification skills of numerous industrial elements. This paper proposes a solution for a hazardous industry that wishes to advance in the automation field in order to make the process safer and easier.

A Review on Classification of Industrial Components using Image Processing and Machine Learning

Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC) using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%.

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Object Classification Using Substance Based Neural Network

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Object Classification Using Substance Based Neural Network