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Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm...
This paper provides a survey of different soft computing methods used in epileptic seizure detection from the EEG signal of the patient. The soft computing algorithms discussed here are Neural Network, fuzzy logic and probabilistic reasoning. The methodology, application and accuracy of each method are analyzed.
Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
In this paper, we address the problem of recognizing object categories by proposing a learning model based on evolutionary algorithm that takes unsegmented, complex images which is tolerant to 2D affine transformations such as scaling and translation in the image plane and 3D transformations of an object such as illumination changes and rotation in depth. To achieve this, first object features are...
In order to make recommendations to a user, a recommender mainly uses two approaches: content-based-filtering approach and collaborative filtering approach. However, they both still have some shortcomings technically. The content-based approach is difficult to handle feature extraction as well as user intension prediction. The collaborative approach faces the hard issue of cold start problem and the...
Over the years significant research has been performed for automated, i.e. machine vision based fabric inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated fabric inspection systems mainly involve two challenging problems, one of which is defect classification. The amount of research done to date to solve the defect classification problem...
This paper presents a simple and computationally efficient method for plant species recognition using leaf image. This method works only for the plants with broad flat leaves which are more or less two dimensional in nature. The method consists of five major parts. First, images of leaf are acquired with digital camera or scanners. Then the user selects the base point of the leaf and a few reference...
This paper presents a navigation method that enables an autonomous mobile robot to localize itself and identify its own orientation in order to follow a path in the environment. Both tasks use the same recognition method. The method is based on features data provided through an image captured by a single camera, which is trained by a neural network. No precise and accurate measurement is used in the...
This paper presents a detail review and implementation issue of fetal ECG extraction and enhancement. The focus is also made on proper placement of electrodes for fetal ECG monitoring in twins and multi-fetal prenatal. Various extraction methods like correlation, subtraction, matched filtering, linear regression and independent component analysis are discussed. For enhancement neural networks, fuzzy...
Current search engines are not very effective in filtering out harmful information since the technology they use for filtering is often based on traditional text classification in which texts are often classified according to feature words. To improve the effectiveness of filtering, in this paper, we propose a new filtering scheme in which we combine the neural network and ontology categorization...
Correlation techniques are widely used to locate leaks in buried water pipes. However, a distinct peak in the cross-correlation of two spatially separately collected acoustic signals may result from a non-leak acoustic source outside the pipe. And the peak not related to a real leak will result in a false leak location. So it is necessary to determine whether or not a real leak exists beforehand....
Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output (MIMO) systems is proposed. The suggested algorithm is verified using higher order statistical moments and cumulants of the received signal. A multi-layer neural network trained...
Image feature and similarity measure are important topics in content-based image retrieval. In this paper, we present energy signal sequences of Energy entropy, Entropy, Averagy residual, Standard deviation from Pulse-Coupled Neural Networks (PCNN) as image feature respectively, and Correlation Coefficient (CC) as the similarity metrics in image retrieval system. The pulse image sequence generated...
The following topics are dealt with: exponential change-points model; Fourier transform; forecasting sunspot numbers; distributed temperature measurements; software development; adaptive observer based tracking control; FPGA bases analysis; maximum power point tracking based optimal control; visual indicator component software; dye-sensitized solar cell; feedback delay effect; thresholding algorithm;...
This paper deals with Automatic Speaker Recognition in a binaural context. Such a problematic, not so widely dealt with within the speech processing community, can have potential applications in humanoid robots where speech can be used as the most natural interface between humans and robots. The proposed recognition system is based on parallel Predictive Neural Networks exploiting MFCCs (Mel Frequency...
Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected,...
In the world of technology, human-machine interaction is becoming more common and will perhaps be a part of our life in the future. Human-machine interaction is more natural if machines are able to perceive and respond to human non-verbal communication such as emotions instead of relying only on audio-visual emotion channels. A particle swarm optimization (PSO) of synergetic neural classifier for...
The principal goal of the segmentation process is to partition an image into classes or subsets that are homogeneous with respect to one or more characteristics or features. In medical imaging, segmentation is important for feature extraction, image measurements, and image display. This study presents a new version of complex-valued artificial neural networks (CVANN) for the biomedical image segmentation...
In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor,...
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