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Surveys are used by hospitals to evaluate patient satisfaction and to improve operation. Collected satisfaction data is usually represented to the hospital administration using statistical charts and graphs. Although this statistical data and visualization is helpful, but because of the size and dimension of the dataset, it is very difficult if not impossible, to identify important factors that could...
Restricted Boltzmann Machine (RBM) is an important generative model modeling vectorial data. While applying an RBM in practice to images, the data have to be vectorized. This results in high-dimensional data and valuable spatial information has got lost in vectorization. In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly...
Visual context is fundamental to understand human actions in videos. However, to efficiently employ temporal context information presents an enormous challenge to this area. Two main problems are long-standing: (1) video frames are redundant while discriminative information is sparse; (2) large amount of interference information is mixed in frame sequences. These factors results in redundant computation...
Buildings are responsible for a significant amount of total global energy consumption and as a result account for a substantial portion of overall carbon emissions. Moreover, buildings have a great potential for helping to meet energy efficiency targets. Hence, energy saving goals that target buildings can have a significant contribution in reducing environmental impact. Today's smart buildings achieve...
Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of...
Saliency computational model with active environment perception can substantially facilitate a wide range of applications. Conventional saliency computational models primarily rely on hand-crafted low level image features, such as color or contrast. However, they may face great challenges in low lighting scenario, due to the lack of well-defined feature to represent saliency information in low contrast...
In the fine-grained categories, images have lager diversity in their intra categories. Meanwhile, they have more similarity in their inter categories. Therefore, images are difficultly distinguish during fine-grained visual classification(FGVC). This paper proposes a deep sparse coding framework to implement the fine-grained visual categorization. In our framework, deep layer structures with sparse...
With the evolution of e-commerce and Online Social Networks, the web information has constantly increased, so the relevance to create methods for automatic knowledge extraction and data mining earned notoriety. Information as opinion evaluation is a point studied by Sentiment Analysis area, which is becoming important nowadays. Be aware of the best reviews is a factor that must be taken into account...
Malware classification has become an important task in protection of privacy and sensitive information from being stolen or modified. A number of malware categories and families emerged over last decade targeting Microsoft Windows since it is the most attractive platform for virus developers. Software for this OS is provided in a format of Portable Executable (PE) files. Majority of commercial anti-virus...
Images, text, web documents, videos, real-world data are very often high-dimensional. Many researchers or users may need to construct accurate predictive models for a variety of applications, especially those that involve clustering. Handling high dimensional data is a reality in processing task involving areas such as high-throughput genotyping platforms and human genetic clustering in bioinformatics,...
A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods...
A major issue for bringing brain-computer interface (BCI) based on electroencephalogram (EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying statistical properties of the signals during inter- or intra-session transfers can lead to deteriorated BCI performances over time. These variations may cause the input data distribution to shift when transitioning from the...
The study of proteins and the prediction of their three-dimensional structure is one of the most challenging problem in Structural Bioinformatics. Over the last years, several computational strategies have been proposed as a solution to this problem. As revealed by recent CASP experiments, the best results have been achieved by knowledge-based methods. Despite the advances in the development of computational...
Estimating emotional states in music listening based on electroencephalogram (EEG) has been capturing the attention of researchers in the past decade. Although deep belief network (DBN) has witnessed the success in various domains including early works in emotion recognition based on EEG, it remains unclear whether DBN could improve emotion classification in music domains, especially in dynamic strategy...
In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure)...
This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme...
Auditory emotion recognition has become a very important topic in recent years. However, still after the development of some architectures and frameworks, generalization is a big problem. Our model examines the capability of deep neural networks to learn specific features for different kinds of auditory emotion recognition: speech and music-based recognition. We propose the use of a cross-channel...
This paper presents a simulated memristor crossbar implementation of a deep Convolutional Neural Network (CNN). In the past few years deep neural networks implemented on GPU clusters have become the state of the art in image classification. They provide excellent classification ability at the cost of a more complex data manipulation process. However once these systems are trained, we show that the...
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector)...
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted...
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