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Accurate localization of brain regions responsible for language and cognitive functions in Epilepsy patients should be carefully determined prior to surgery. Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been shown to be a safer alternative to the electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard. Conventional methods for analyzing...
Essays in different text genres have different ideas and writing method. Prediction the text genres firstly will help get a better accuracy when predicting the success of literary or finding the beautiful words and sentences in the essay. And it will help set a different standard for different text genres when scoring the writing by computer. Words and structure can be effective in discriminating...
In this paper, we propose 3 different machine learning techniques such as Random Forest, Bagging and Support Vector Machine along with time domain feature for classifying sleep stages based on single-channel EEG. Whole-night polysomnograms from 25 subjects were recorded employing R&K standard. The evolved process investigated the EEG signals of (C4-A1) for sleep staging. Automatic and manual scoring...
The aim of this study is to develop a time series classification method based on scale-space theory. Our study has been conducted in three steps: In the first step, scale-space extrema of time series found through using SiZer (SIgnificant ZERo crossings of the derivatives) method and local features set constructed around the determined extreme points, based on interval-widths list entered by the user...
Non-technical losses, especially by cause of electricity theft have been a primary care in power system during long period of time. Huge absorption of electricity in a fraudulent way may mismatch the gap between supply and demandprofoundly. Detection of electricity theft is done in two levels, data processing and analysis scheme. This paper presents the first level of data processing to predict the...
The detection of cells and nuclei is a crucial step for the automatic analysis of digital pathology slides and as such for the quantification of the phenotypic information contained in tissue sections. This task is however challenging because of high variability in size, shape and textural appearance of the objects to be detected and of the high variability of tissue appearance. In this work, we propose...
In this letter, we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests (KRF) and three-dimensional Affine Pose Regression (3D-APR) for face shape initialization. Our face alignment method is based on the Local Binary Feature (LBF) framework, where instead of standard regression forests and pixel difference features used in the original...
Eye movements are well known to express cognitive distraction. Detecting cognitive distraction can help to prevent work-related accidents; thus, it is very useful to detect cognitive distraction using eye movements. Eye movements can be classified into various types. In this paper, we apply an identification-based machine learning algorithm considering eye movement types. We apply Random Forest as...
Automatic extraction of popular music ringtones have become an important and useful area for communications and telecommunications industry. Quick and batch extraction of music ringtones increases the convenience in practical application. In this paper, we propose an automatic technology to extract the ringtones from popular music based on the musical structural analysis. This is a meaning attempt...
Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance...
Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We...
This study introduces a three-stage integrated framework consisting of data envelopment analysis (DEA), random forest, and logistic regression to examine and predict the impact of environmental variables on banks' performance. This framework identified five important environmental variables and their effects on bank performance when applied to 151 banks in Middle East and North African (MENA) countries...
The classification of web pages content is essential to many information retrieval tasks. In this paper, we propose a new methodology for a multilayer soft classification. Our approach is based on the connection between the semi-supervised Latent Dirichlet Allocation (LDA) and the Random Forest classifier. We compute with LDA the distribution of topics in each document and use the results to train...
Random Forest is a well-known ensemble learning method that achieves high recognition accuracies while preserving a fast training procedure. To construct a Random Forest classifier, several decision trees are arranged in a forest while a majority voting leads to the final decision. In order to split each node of a decision tree into two children, several possible variables are randomly selected while...
In this paper, the fusion of hyperspectral and Li-DAR data is taken into account in order to develop a new classification framework for the accurate analysis of urban areas. In this method, an attribute profile is considered in order to model the spatial information of LiDAR and hyper-spectral data. In parallel, in order to reduce the redundancy of the hyperspectral data and address the so-called...
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