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Grasping objects is one of the most important hand utilisation in everyday life. Due to neuromuscular ailments or injury, some people are unable to move their hands. Though myoelectrically controlled prostheses are widely available in the market, they require some muscle based control points which are hardly available for many. Motor Imagination (MI) controlled prostheses will surpass this shortcomings...
The change of the vibration signal can reflect the mechanical state of the HV circuit breaker. An efficient method of feature extraction of the vibration signal usually pays a key role in the validity of the fault diagnosis and also lays the foundation for the fault classification in the subsequent stage. The paper presented a feature extraction method which is based on average empirical mode decomposition...
We propose a novel computer recognition system with magnifying narrow-band imaging (NBI) colonoscopy to predict pathologic diagnosis of colorectal neoplasms. In this work, we propose a system based on the sparse representation of features derived from the Bank of Binarized Statistical Image Features (B-BSIF). The BSIF code of a pixel is considered as a local descriptor of the image intensity pattern...
In order to fully utilize the local geometric information of the given training set consisting of the normal data, locality correlation preserving (LCP) is introduced into the traditional one-class support vector machine (OCSVM). The proposed method, named as locality correlation preserving based one-class support vector machine (LCP-OCSVM), inherits the merits of LCP and OCSVM. It can keep locality...
The performance of a defect classification modeldepends on the features that are used to train it. Feature redundancy, correlation, and irrelevance can hinder the performance of a classification model. To mitigate this risk, researchers often use feature selection techniques, which transform or select a subset of the features in order to improve the performance of a classification model. Recent studies...
Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature...
With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining...
With the development of smart phones, more and more mobile phone malwares have came out in the market especially on the popular platforms such as Android, which can potentially cause harm to users' information. But how to effectively detect the new malwares and malicious software variants has been a difficult problem. In view of the traditional feature extraction method based on binary program, this...
In order to solve the problem of low intrusion detection rate and weak generalization ability of Intrusion Detection System (IDS), it proposes a new hybrid method based on the relationship of feature and spatial correlation for IDS. The proposed IDS reduces the dimension of network data flow by spatial correlation-based dimension reduction method (SCDR). It improves the effectiveness of intrusion...
The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
The aim of this paper is to propose a new method of discrimination when the dependant variable is categorical and when a large number of categorical explanatory variables is retained. This method, Categorical Multiblock Linear Discriminant Analysis (CMLDA), computes components which take into account both relationships between explanatory categorical variables and canonical correlation between each...
This work presented two prediction models for the estimation of student's performance in final examination. The work made use of the popular dataset provided by the University of Minho in Portugal, which relate to the performance in math subject and it consists of 395 data samples. Forecasting the performance of students can be useful in taking early precautions, instant actions, or selecting a student...
Text classification, a simple and effective method, is considered as the key technology to deal with and organize a large amount of text data. At present, the simple text classification is unable to meet the increasing of user's demand, hierarchical text classification has received extensive attention and has broad application prospects. Hierarchical feature selection algorithm is the key technology...
Speech enhancement using adaptive filtering methods are known to give good signal recovery from the noisy speech signal. Among these, Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms are more popular. These algorithms have a constraint that correlating noise should be given as the reference signal for denoising. Therefore in all the adaptive algorithms, two microphones are used,...
A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale...
We present a novel Cyber Security analytics framework. Wedemonstrate a comprehensive cyber security monitoring system toconstruct cyber security correlated events with feature selection toanticipate behaviour based on various sensors.
Complex industrial processes are often non-linear and non-Gaussian, while the traditional principal component analysis (PCA) method assumes that the data are Gaussian and linear. In this paper, a novel process monitoring method based on maximum information coefficient-PCA (MIC-PCA) and support vector data description (SVDD) is proposed. First, the covariance matrix is replaced by the MIC matrix which...
Considering the characteristics of communication signal from the whole and local all together, it can improve the classification accuracy. A new feature extraction algorithm of communication signals based on the fractal and wavelet theories is proposed. Employing preprocessing the received signal, the correlation dimension of empirical mode decomposition(EMD) is researched to extract feature and they...
Functional connectivity is the stochastic association or the dependency of two or more distinct brain regions. It is primarily used for finding patterns that are validated through statistical methods, in the context of brain connectivity. Quantification of functional connectivity is usually performed using Pearson's correlation coefficient (PCC). Many Functional magnetic resonance imaging (fMRI) studies...
Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (MKL) attempt to learn the parameters for combining the kernels through sophisticated optimization procedures. In this paper, we propose an alternative approach that creates dense embeddings for data using the kernel similarities...
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