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1D-local binary pattern based feature extraction for classification of epileptic EEG signals

In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.

A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease

Abstract Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications.

Evaluation of texture features for automatic detecting butterfly species using extreme learning machine

In this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.

Two novel local binary pattern descriptors for texture analysis

In this study, two novel local binary patterns were proposed.First one is based on spatial relations between neighbors with a distance parameter.The second is based on relations between a reference pixel and its neighbor on the same orientation.Two approaches are improved to detect special patterns in images.The results show that the proposed approaches can be used in image processing areas. The recent developments in the image quality, storage and data transmission capabilities increase the importance of texture analysis, which plays an important role in computer vision and image processing. Local binary pattern (LBP) is an effective statistical texture descriptor, which has successful applications in texture classification. In this paper, two novel descriptors were proposed to search different patterns in images built on LBP. One of them is based on the relations between the sequential neighbors with a specified distance and the other one is based on determining the neighbors in the same orientation through central pixel parameter. These descriptors are tested with the Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets to show the applicability of the proposed nLBPd and dLBPα descriptors. The proposed methods are also compared with classical LBP. The average accuracies obtained by ANN with 10 fold cross validation, which are 99.26% (LBPu2 and nLBPd), 94.44% (dLBPα), 95.71% ( n L B P d u 2 ) and %99.64 (nLBPd), for Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets, respectively, show that the proposed methods outperform significant accuracies.

Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait

This study showed that the PD can be diagnosed by using sensors attached at underfoot from gait.Feature extracted by Shifted 1D-LBP, which is sensitive to local changes in time signals.Shifted 1D-LBP has a simple algorithm. It can be used in real time applications.Obtained detection accuracy is 88.8889%.The accuracy results were compared with the results of previous studies in literature. The Parkinsons disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinsons Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.

A novel approach for SEMG signal classification with adaptive local binary patterns

AbstractFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network

Abstract Pollen grains vary in colour and shape and can be detected in honey used as a way of identifying nectar sources. Accurate differentiation between pollen grains record is hampered by the combination of poor taxonomic resolution in pollen identification and the high species diversity of many families. Pollen identification determines the origin and the quality of the honey product, but this indefiniteness is also a big challenge for the beekeepers. This study aimed to develop effective, accurate, rapid and non-destructive analysis methods for pollen classification in honey. Ten different pollen grains of plant species were used for the estimation. GLCM (grey level co-occurrence matrix) texture features and ANN (artificial neural network) were used for the identification of pollen grains in honey by the reference of plant species pollen. GLCM has been calculated in four different angles and offsets for the pollen of the plant and the honey samples. Each angle and offset pair includes five features. At the final step, features were classified using the ANN method; the success of estimation with ANN was 88.00%. These findings suggest that the texture parameters can be useful in identification of the pollen types in honey products.

Automatic identification of butterfly species based on local binary patterns and artificial neural network

A computer vision method was proposed for automatically identifying butterfly species.To our knowledge, it was the first study in identifying the butterfly species with computer vision.The method is based on local binary patterns and artificial neural network.Results demonstrated that the proposed method has achieved well recognition accuracy rates. Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification.

EMG signal classification by extreme learning machine

From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.

A novel approach for spam email detection based on shifted binary patterns

Advances in communication allow people flexibility to communicate in various ways. Electronic mail (email) is one of the most used communication methods for personal or business purposes. However, it brings one of the most tackling issues, called spam email, which also raises concerns about data safety. Thus, the requirement of detecting spams is crucial for keeping the users safe and saving them from the waste of time while tackling those issues. In this study, an effective approach based on the probability of the usage of the characters that has similar orders with respect to their UTF-8 value by employing shifted one-dimensional local binary pattern (shifted-1D-LBP) was used to extract quantitative features from emails for spam email detection. Shifted-1D-LBP, which can be described as an ordered set of binary comparisons of the center value with its neighboring values, is a content-based approach to spam detection with low-level information. To validate the performance of the proposed approach, three benchmark corpora, Spamassasian, Ling-Spam, and TREC email corpuses, were used. The average classification accuracies of the proposed approach were 92.34%, 92.57%, and 95.15%, respectively. Analysis and promising experimental results indicated that the proposed approach was a very competitive feature extraction method in spam email filtering. Copyright