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Trackside data logging hardware is often used in the UK, and increasingly elsewhere in the world, to record and transmit processed condition data from track switching equipment (points) in order to gauge asset health. This paper presents a novel implementation of three tools which can be used together to make the analysis and handling of this data easier. The first of these tools is a statistical...
In this paper, Probabilistic Neural Network with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance...
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...
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...
Karyotyping, manual chromosome classification is a difficult and time consuming process. Many automated classifiers have been developed to overcome this problem. These classifiers either have high classification accuracy or high training speed. This paper proposes a classifier that performs well in both areas based on wavelet neural network (WNN), combining the wavelet into neural network for classification...
Long-term monitoring of health is essential in many chronic conditions, but automatic monitoring is not yet utilized routinely with mental stress. Accelerometers, magnetometers, ECG, respiratory effort, skin temperature and pulse oximetry were used with 12 health volunteers in this study for monitoring 1) heavy mental load, 2) normal mental load, 3) walking, 4) running and 5) lying. Heavy mental load...
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...
Manual bacteria classification is a tedious work which often needs abundant correlative data and also takes a great deal of time and energy. Combining pattern recognition and new neural network, we propose an approach of bacteria classification based on morphometrics using artificial neural network. The neural network is applied to extract the feature. The entropy sequence is taken as the feature...
Generally, IQMs are not able to well predict the image quality for all degradations. Indeed, well performance could be obtained for a given degradation and poor results for others. This is essentially due to the fact that the efficiency of IQMs depends highly on the degradation specificity. To overcome this limitation, we propose to first identify the type of degradation before measuring the quality...
Speech production and speech phonetic features gradually improve in children by obtaining audio feedback after cochlear implantation or using hearing aid. In this study, voice disorders in children with cochlear implantation and hearing aid are classified. 30 Persian children participated in the study, including 6 children in levels 1 to 3 and 12 in level 4. Voice samples of 5 isolated Persian words...
This paper presents S-Transform based Competitive Neural Network (CNN) classifier for recognition of inrush current. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. S-transform is used for feature extraction and CNN is used for classification. Inrush current data and other transients are obtained...
In this study, Electrocardiographic(ECG) Arrythmias were classified by using Artificial Neural Networks (ANN). During the training process of ANN, the ECG recordings from MIT BIH Arrythmia database are used as a reference. 24 recordings out of 48 30 minutes recordings in this database were used for data extraction. In order to have more realistic data, the extractons were made from different recordings,...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
Electroencephalography (EEG) analysis by physicians is intricate, time consuming and needs to experience. Therefore automated systems for EEG analysis and classification are able to help physician. EEG signal in the field of time is raw and complex so it's not suitable for automated system. Therefore appropriate features of EEG signal becomes extraction using signal processing methods (in this paper...
Web advertising has become a major industry and a large part of this market consists of contextual ads. Although it has made a great impact on earnings of many publishers' websites, these advertisements tend to disturb the internet surfing of normal users and to consume a lot of valuable bandwidth. Moreover, they always bring extra burden in indexing to commercial search engines as they mix up with...
In this paper, one fabric defect detection and classification system based on 2D Gabor wavelet transform and Elman neural network is introduced. In the proposed scheme, the texture features of the textile fabric are extracted by using an optimal 2D Gabor filter. A new modified Elman network is proposed to classify the type of fabric defects which have a proportional (P), integral (I) and derivative...
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the...
Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;...
In this study, performance of wavelet transform based features for the speech / music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features used in speech/music...
wireless capsule endoscopy (WCE) is an important device to detect abnormalities in small intestine. Despite emerging technologies, reviewing capsule endoscopic video is a labor intensive task and very time consuming. Computational tools which automatically detect informative frames and tag abnormal conditions such as bleeding, ulcer or tumor will dramatically reduce the clinician's effort. In this...
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