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Twitter is a popular microblogging service that allows its users to view and share limited character messages (known as “tweets”) with the public. This paper proposes a tweet sentiment classification framework which pre-processes information from Emoticon and Emoji in such way that their textual representation is included to enrich the tweet. Once the tweets are pre-processed, a hybrid computational...
One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies...
For the sake of improving the precision of speech emotion recognition, this paper proposed a novel speech emotion recognition approach based on Gaussian Kernel Nonlinear Proximal Support Vector Machine (PSVM) to recognize four basic human emotions (angry, joy, sadness, surprise). Firstly, preprocess speech signal containing sampling, quantification, pre-emphasizing, framing, adding window and endpoint...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach,...
Uncertainty based active learning has been well studied for selecting informative samples to improve the performance of the classifier. One of the simplest strategy is that we always select samples with top largest uncertainties for a query. However, the selected samples may be very similar to each other, which results in little information added to update the classifier. In other words, we should...
Brain-computer interface (BCI) is an emerging area of research that aims to improve the quality of human-computer applications. It has enormous scope in biomedical applications, neural rehabilitation, biometric authentication, educational programmes, and entertainment applications. A BCI system has four major components: signal acquisition, signal preprocessing, feature extraction, and classification...
The gene structure is consist of intron, exons, promoter, start codon, stop codon, etc. for the eukaryotic organism. The boundary between intron and exon is splice site. There is the need for accurate algorithms to be used in the splice sites identification and more attention was paid during past few years. This proposed system, Splice Hybrid have three layered architecture — in this layer2nd orderMM...
Representation of data is very important in case of machine learning. Better the representation, the classifiers will give better results. Contractive autoencoders are used to learn the representation of data which are robust to small changes in the input. This paper uses contractive autoencoder and SVM classifier for handwritten Devanagari numerals recognition. The accuracy obtained using CAE+SVM...
Automated facial expression recognition (AFER) is a crucial technology to and a challenging task for human computer interaction. Previous methods of AFER have incorporated different features and classification methods and use basic testing approaches. In this paper, we employ the best feature descriptor for AFER by empirically evaluating the feature descriptors named the Facial Landmarks descriptor...
This paper considers a problem of reconstructing a typeface of Chinese characters. First, Chinese characters are generated by using the so-called dynamic font method as a basic tool. Then, we develop a scheme for reconstructing characters to cursive characters with natural "Renmen" which can depict continuity of the adjacent strokes. For realizing the natural Renmen, we propose a method...
In this paper, we presented an improved vehicle detection algorithm based on object proposals. In the training part, by using Selective Search algorithm, we firstly segment the vehicle areas in the sample set as positive examples, other regions as negative examples. Then PHOG (Pyramid Histogram of Oriented Gradient) features of the positive samples and negative ones after separately being labeled...
In this paper, the incipient fault diagnosis problem is studied for traction motor sensor fault. The data used for the fault diagnosis is from sensors of the closed-loop traction motor system, in which the deviations between normal and bias faulty data are of 1 %∼5 % and between normal and gain faulty data are of 1%∼10%. Considering the non-stationary of the data, the Ensemble Empirical Mode Decomposition...
Nonintrusive monitoring is a powerful tool to increase the quality, reliability, and efficiency of power electronics systems. In recent years, the use of intelligent systems to perform this type of tasks has been increased due to its high capacity to correctly predict the values of the parameters under monitoring. Support Vector Machines (SVM) is a supervised learning method that maps input data into...
A two-layer fuzzy kernel regression (TLFKR) model is proposed for understanding human emotional intention in human-robot interaction, where TLFKR model consists of two layers, including fuzzy c-means (FCM) with kernel ridge regression (Kernel 1) for information analysis layer, fuzzy support vector regressions (FSVR) (Kernel 2) for intention understanding layer. TLFKR model represents the weight impact...
In the distillation process, many important process variables are often difficult to be measured online. For example, the aviation kerosene is an important index of operation quality, but current methods cannot obtain the real-time value of the aviation kerosene efficiently. To solve this problem, a method of selecting the input variable based on partial least squares regression (PLS) is proposed...
One of the most important ways to explore the information in hyperspectral images (HSIs) is accurate classification of targets. Deep learning algorithm has made a great breakthrough in many areas due to its strong ability of data mining. Typical deep learning models such as convolutional neural network (CNN), deep belief network (DBN) and so on, not only combines the advantages of unsupervised and...
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