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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...
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...
Recent studies on the geometry of fractals indicate that tumors with irregular shapes can be utilized for the study of the morphology and diagnosis of cancerous cases. In this paper, we deal with the fractal modeling of the mammographic images and their background morphology. It is shown that the use of fractal modeling as applied to a given image can clearly discern cancerous zones from noncancerous...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
Accurate land use/cover (LUC) classifications from satellite imagery are very important for eco-environment monitoring, land use planning and climatic change detection. Traditional statistical classifiers such as minimum distance (MD) have been used to extract LUC classifications in urban areas, but these classifiers rely on assumptions that may limit their utilities for many datasets. On the contrary,...
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
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...
This paper presents a new classification method based on a combination of GIS and BP (back propagation) artificial neural network, taking the TM image of the area of Jinzhou city in 2000 as the testing one. BP neural network can be optimized by means of selecting GIS data aided training sample, improving training algorithm, calculating the number of nodes in the hidden layer and so on. Compared with...
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...
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...
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, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for...
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...
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area...
A new technique for fast detection of power islands in a distribution network, which uses transient signals generated during the islanding event is investigated. Performance comparison of several pattern recognition techniques in classifying the transient generating events as islanding or non-islanding is presented. Features for the classifiers are extracted using the Discrete Wavelet Transform of...
Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of disease. The proposed system concentrates on the two different cognitive tests for the diagnosis of different stages of dementia. Dementia is considered as the fourth most common disorder among the elderly. Early detection of dementia and correct staging of the...
Orientation detection is an important preprocessing step for accurate recognition of text from document images. Many existing orientation detection techniques are based on the fact that in Roman script text ascenders occur more likely than descenders, but this approach is not applicable to document of other scripts like Urdu, Arabic, etc. In this paper, we propose a discriminative learning approach...
A new text categorization method based on singular value decomposition (SVD) and cascade correlation (CC) algorithm is proposed. Most traditional classification systems represent the contents of documents with vector space model (VSM) which represents documents with a set of index terms. However, this model needs a high dimensional space to represent the documents and it does not take into account...
Bioinformatics analysis based on microarray technology is facing serious challenges, due to the extremely high dimensionality of the gene expression data comparing to the typical small number of available samples. Single artificial neural network was unstable and inaccurate for classification. In this paper we introduce classifying gene expression data using artificial neural network ensembles based...
This paper proposes the application of Artificial Neural Network for the classification of Arabic language documents. The automatic classification of Arabic documents using ANN has not been explored in detail so far. In this paper, an Arabic corpus is used to construct and test the ANN model. Methods of document representation, assigning weights that reflect the importance of each term are discussed...
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