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Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest...
Aerosol optical depth (AOD), one of the key factors affecting the atmosphere visibility, has great influence on the prediction of radiation intensity and photovoltaic power generation. Considering the problem that AOD is difficult to obtain real-timely and conveniently with high accuracy, in this paper, PM2.5 concentration, PM10 concentration and temperature, wind speed grade and relative humidity...
Embedded computer vision applications have been incorporated in industrial automation, improving quality and safety of processes. Such systems involve pattern classifiers for specific functions that, many times, demand high memory footprint and processing time. This work suggests a strategy to choose GLCM (Gray Level Co-occurrence Matrix) features for an SVM classifier that can reduce computer resources...
Gene (microRNA) identification is a key step in understanding the cellular mechanisms. Compared with biological experiments, computational prediction of disease genes is cheaper and more effortless. In this study, we analyzed the properties of tumor-associated microRNA in mouse and found that tumor-associated genes display 8distinguishingfeatures when compared with genes not yet known to be involved...
Wind speed prediction has been used in various fields such as Satellite launch, Air traffic control, Weather forecasting etc. Wind speed can be calculated by various atmospheric variables such as temperature, humidity, pressure, wind direction, etc. A number of methods have been proposed by various researchers to predict the wind speed. During the last few years a lot of research has been carried...
Brain tumor segmentation from magnetic resonance images is a critical step for early tumor diagnosis and treatment. However, accurate and general segmentation of brain tumor is still a challenging task due to complicated characteristics of brain tumor in magnetic resonance images. To solve this problem, we proposed a novel method for brain tumor segmentation based on features of separated local square...
Diabetes is one of the most prevalent diseases worldwide, and hundreds of millions of patients are suffering from diabetes and its serious complications. Early detection and early treatment are urgent needed for clinical diagnosis of diabetics. In this work, we establish a gene coexpression network framework to identify biomarkers of transcripts with highly different gene coexpression patterns in...
With the development of next-generation sequencing technologies, large number of transcripts has been accumulated in public databases. Long non-coding RNAs (lncRNAs), typically above 200 nucleotides in sequence length, have recently attracted increasing interests because of their important roles in various cellular processes. While it is straightforward to distinguishing lncRNAs from most small non-coding...
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...
With the significant increase of the network heterogeneity and the wide use of emerging video applications such as wireless sensor networks, video surveillance systems or remote sensing, the Distributed Scalable Video Coding (DSVC) is a potential solution for efficiently transmitting and storing video data due to its high compression efficiency and low encoding complexity capabilities. In DSVC framework,...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
It is difficult to establish accurate mathematical models to describe the range extender electric vehicles due to the non-stationary, non-linear and interconnection of the monitoring signal sources resulted from the massive moving parts and complex architecture in range-extender. And the support vector machine (SVM) and other algorithms would lead to the destruction of the natural structure and the...
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...
The inherent dependencies between visual elements and aural elements are crucial for affective video content analyses, yet have not been successfully exploited. Therefore, we propose a multimodal deep regression Bayesian network (MMDRBN) to capture the dependencies between visual elements and aural elements for affective video content analyses. The regression Bayesian network (RBN) is a directed graphical...
The authors propose a new tool to extensively analyze and plan the radio resources based on machine learning technique using available data analytics methods like clustering, correlation and regression. This will be done through; Classifying the cells according to their Priority and required quality of service, detecting resources utilization that cause throughput limitation, efficient dimensioning...
The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
This paper describes a new approach to building the query based relevance sets (qrels) or relevance judgments for a test collection automatically without using any human intervention. The methods we describe use supervised machine learning algorithms, namely the Naïve Bayes classifier and the Support Vector Machine (SVM). We achieve better Kendall's tau and Spearman correlation results between the...
In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion...
Some improvements in the classification of masses in the breast are proposed in this paper. First, for the purpose of enriching the information concerning the shape of the mass, a new morphological feature is extracted. Then, the textural features of the region of interest (ROI) are extracted by combining the undecimated wavelet transform (UWT) and the gray level co-occurrence matrix (GLCM). Finally,...
In this paper, we propose an analysis method of drowsiness status of a driver from the opening and closing of eye. For this purpose, eyes, nose and mouth are detected to improve the area of interest using the spatial correlation in the existing Viola-Jones algorithm. Then, histogram equalization is performed for detection at night driving, and drowsiness status of the driver data with the accumulation...
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