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Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS...
A novel fMRI classification method designed for rapid event related fMRI experiments is described and applied to the classification of loud reading of isolated words in Hebrew. Three comparisons of different grammatical complexity were performed: (i) words versus asterisks (ii) “with diacritics versus without diacritics” and (iii) “with root versus no root”. We discuss the most difficult task and,...
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...
With the development of deep learning, word vectors (i.e., word embeddings) have been extensively explored and applied to many Natural Language Processing tasks (e.g., parsing, Named Entity Recognition, etc). However, the semantic word vectors learned from context have insufficient sentiment information for performing sentiment analysis at different text levels. In this work, we present three Convolutional...
This paper studies Aspect-based Opinion Summarization (AOS) of reviews on particular products. In practice, an AOS system needs to address two core subtasks, aspect extraction and sentiment classification. Most existing approaches to aspect extraction, using linguistic analysis or topic modeling, are general across different products but not precise enough or suitable for particular products. Instead...
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detection of rail defects can help to save time and costs,...
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...
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...
Recently, rectified linear units (ReLUs) have been used to solve the vanishing gradient problem. Their use has led to state-of-the-art results in various problems such as image classification. In this paper, we propose the hyperbolic linear units (HLUs) which not only speed up learning process in deep convolutional neural networks but also obtain better performance in image classification tasks. Unlike...
In this paper, we focused on designing a semi-rotation invariant feature descriptors for classification problem. We proposed hierarchical Zernike moments architecture which is combination of original Zernike moments, the local receptive field concept and shared weights concept from convolution neural network. The descriptors which are output of the architecture have improved classification performance...
Human activity recognition involves classifying times series data, measured at inertial sensors such as accelerometers or gyroscopes, into one of pre-defined actions. Recently, convolutional neural network (CNN) has established itself as a powerful technique for human activity recognition, where convolution and pooling operations are applied along the temporal dimension of sensor signals. In most...
Visual features trained from large scale image data by the deep convolutional neural network can be used for the other visual tasks. This paper investigates the effects of the learning of multiple tasks for such transfer learning from the source domains to the target domain. Two methods of the learning of multiple tasks are considered. Also we investigate which hidden layers should be re-trained for...
Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-the-art approach for many tasks. One advantage is their implicit feature learning, opposite to an explicit feature extraction of the input signal. In this work, we analyzed whether more discriminative features can be learned...
In this paper, a novel electrocardiogram (ECG) signal classification and patient screening method is developed. The focus is on identifying patients with paroxysmal atrial fibrillation (PAF) which is a life threatening cardiac arrhythmia. The proposed approach uses the raw ECG signal as the input and automatically learns the representative features for PAF to be used by a classification mechanism...
Naïve Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each...
This paper presents a fast algorithmic method to train convolutional neural network (CNN) classifiers through extreme learning which has been verified on popular datasets on classification and pedestrian detection. CNN has been one of the best classifiers for images and object recognition. However, the Backpropagation (BP) algorithm, mostly used for training CNN, suffers from slow learning, local...
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