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The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
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 proposes a novel method for fire and smoke detection using video images. The ViBe method is used to extract a background from the whole video and to update the exact motion areas using frame-by-frame differences. Dynamic and static features extraction are combined to recognize the fire and smoke areas. For static features, we use deep learning to detect most of fire and smoke areas based...
This study proposes a system-on-a-chip, field-programmable gate array (FPGA)-based real-time video processing platform for human action recognition. We provide the details of a hardware implementation for real-time human activity recognition in 3D scenes, including capture, processing, and display. The proposed platform is implemented by adding a two-stage preprocessing step to improve the results...
The main goal of the work is to present some aspects in using time-frequency transforms, applied to the vibration signals and for change detection purposes, from signal processing point of view. The processed object is the time-frequency image (TFI), as result of the time-frequency transform. Two distributions are considered: Wigner-Ville and Choi-Williams, each with specific properties and matched...
In the paper, we propose an effective long-term real-time tracking method to address the problem of robustness and tracking failure in visual tracking with UAVs. Most existing trackers only consider short-term tracking, therefore are unable to cope with partial and complete occlusion, which finally leads to object drifting or loss. Our method still follows the tracking-by-detection framework. However,...
Vulnerable code reuse in open source software is a serious threat to software security. However, the existing high-efficiency methods for vulnerable code clone detection have a large number of false-negatives when the code is modified, which results in limited application scenarios. In this paper, we present an innovative fingerprint model to describe the vulnerability code and propose VFDETECT, an...
Person re-identification is important and challenging parts in a non-overlapping camera network. In this paper, we propose the person re-identification framework which consists of kernel size into convolutional layers considering the person ratio and relationship matrix that train the relationship information related to neighborhoods. Our framework deals with global feature extracted from the whole...
In process monitoring of batch process, Fisher discriminant analysis is a very popular method and has be widely applied. In this paper, a new kernel local Fisher discriminant analysis (KLFDA) algorithm is proposed for fault diagnosis. The main contributions of the presented approach are as follows: 1) the proposed algorithm can simultaneously extract the global European distribution of data and local...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
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...
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output...
Although the existing correlation filter based on trackers has appeared to be more excellent in the visual tracking problem, there is still tremendous space for the improvement of the tracking performance, especially in the occlusion situation which is often ignored due to the difficulty in detection and processing. In this paper, a scale-adaptive tracker is proposed to handle the case of occlusion...
A major challenge in matching between vision and language is that they typically have completely different features and representations. In this work, we introduce a novel bridge between the modality-specific representations by creating a co-embedding space based on a recurrent residual fusion (RRF) block. Specifically, RRF adapts the recurrent mechanism to residual learning, so that it can recursively...
The core of computational identification of transcription factor binding sites (TFBSs) is to deal with high dimensional and small sample size data and to handle the complex nonlinear relationships between features. Partial least squares (PLS) performs well in reducing dimensionality as well as explaining relations between multiple variables. Besides, kernel methods are widely applied to non-linear...
This paper presents a winning solution to the AAIA'17 Data Mining Challenge. The challenge focused on creating an efficient prediction model for digital card game Hearthstone. Our final solution is an ensemble of various neural network models, including convolutional neural networks.
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