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Fibromyalgia (FM) is a widespread painful disease that has a 2–8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and...
Conventional automatic document classification methods are currently faced with challenges in terms of learning time and computing power, owing to the ever-increasing amount of data on the web. In this paper, we propose an efficient classification method that uses time series-based dataset selection. In the proposed method, the dataset is split based on time series data and the best set of testing...
Time series classification is an important task in data mining that has been traditionally addressed with the use of similarity-based classifiers. The 1-NN DTW is typically considered the most accurate model for temporal data. Nevertheless, some authors have recently proposed ingenious alternatives to the 1-NN DTW by using diversity of time series representation or by using DTW for feature extraction...
The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW)...
The paper presents a new approach for processing of rhinomanometric signals based on F-transform approximation of phase diagrams. Methods of nonlinear dynamics for processing of time series allow us to obtain a significant features of rhinomanometric signals. Research indicated that the results of classification with F-transform approximation is more accurate than results of classification with FFT...
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the...
Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex...
Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion...
Rawal Dam is a strategic asset for the twin cities of Islamabad and Rawalpindi in Pakistan being the main source of drinking as well as agricultural water supplies. The low-lying areas of the reservoir are being affected by reservoir storage and spillways' discharge. For the effective management, modeling techniques would not only be cost effective but also help the water managers in predicting the...
We present a novel data classifier that is based on the regularization of graph signals. Our approach is based on the theory of discrete signal processing on graphs where the graph represents similarities between data and we interpret labels for the dataset elements as a signal indexed by the nodes of the graph. We postulate that true labels form a low-frequency graph signal and the classifier finds...
Multivariate time series (MTS) are used in very broad areas such as finance, medicine, multimedia and speech recognition. Most of existing approaches for MTS classification are not designed for preserving the within-class local structure of the MTS dataset. The within-class local structure is important when a classifier is used for classification. In this paper, a new feature extraction method for...
This paper proposes a system which datamines time series classification knowledge leading by a discovery of feature patterns. In the case of classification, prediction accuracy is an important point, and to build a human understandable model is another essential issue. To satisfy these requests, our system runs in two stages. In the first stage, the system discovers important feature patterns which...
The study investigated the performance of support vector machine (SVM) classifier for regional land cover mapping. First, 8 input features derived from MODIS time series and DEM data were selected by Jeffreys-Matusita distance. Then, all the features were analyzed to generate land cover map of Sanjiang Plain in China, using SVM algorithm. Finally, we evaluated the impact of sample size and its distribution...
This paper compares the performance in financial market prediction of a Neural Network approach and an approach using the regression feature of SVM. The historical values used are those of the Hang Sang Index (HSI) from 2002 to 2007 and data for January 2007 and January 2008. SVM performs well in the short term forecast.
Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles...
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