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A deep neural network (DNN) is called as a deep rectified network (DRN), if using Rectified Linear Units (ReLUs) as its activation function. In this paper, we show its parameters can be seen to play two important roles simultaneously: one for determining the subnetworks corresponding to the inputs and the other for the parameters of those subnetworks. This observation leads our paper to proposing...
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure “black-box” models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not...
With the growth of video content produced by mobile cameras and surveillance systems, an increasing amount of data is becoming available and can be used for a variety of applications such as video surveillance, smart homes, smart cities, and in-home elder monitoring. Such applications focus in recognizing human activities in order to perform different tasks allowing the opportunity to support people...
Deep neural networks have achieved significant success for image recognition problems. Despite the wide success, recent experiments demonstrated that neural networks are sensitive to small input perturbations, or adversarial noise. The lack of robustness is intuitively undesirable and limits neural networks applications in adversarial settings, and for image search and retrieval problems. Current...
A reinforcement learning (RL) agent needs a fair amount of experience to find a near-optimal policy. Transfer learning has been investigated as a means to reduce the amount of experience required. Transfer learning, however, requires another similar reinforcement learning task as a transfer source, which can also be costly in the amount of experience required. In this research, we examine the possible...
Recent years have seen a growing interest in neural networks whose hidden-layer weights are randomly selected, such as Extreme Learning Machines (ELMs). These models are motivated by their ease of development, high computational learning speed and relatively good results. Alternatively, constructive models that select the hidden-layer weights as a subset of the data have shown superior performance...
In this work, we have invesigated the action recognition problem using the Charades Action Recognition Dataset with 157 action classes. We have compared the results of different techniques such as extreme learning machines, support vector machines, and decision trees, applied on the features extracted with deep neural networks and the scene-action conditional probabilities.
Video concept classification is a very important task for several applications such as content based video indexing and searching. In this study, we propose a multi-modal video classification method based on the feature-level fusion of audio-visual signals. In the proposed method, we extract Mel Frequency Cepstral Coefficient (MFCC) and convolutional neural network (CNN) features from the audio and...
Melanoma is a serious cancer that causes many people to lose their lives. This disease can be diagnosed by a dermatologist as a result of interpretation of the dermoscopy images by the ABCD rule. In this study, a deep neural network (DNN) is used as a new method for diagnosis of melanoma skin cancer. This method is compared with the-state-art-methods in literature. According to the obtained results,...
AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional...
The prediction of the hand movement based on Electromyography (EMG) signals has been extensively studied over the past three decades. However, recent EGM applications pose an emerging need of efficient classification of EMG signals. Toward this goal, we propose a deep neural network (DNN) classifier in this study to classify 6 different hand movement from EMG signals. DNN classifier has ability to...
Growing concerns about increasing world population and limited food resources have been leading researchers to utilize advanced computing technology to improve the efficiency of agricultural fields. Computing technology is expected to increase the productivity, contribute to a better understanding of the relationship between environmental factors and healthy crops, reduce the labor costs for farmers...
Classification systems of retail products have recently been gaining more importance. There are many classes of retail products and the resemblance of these products makes the design of product recognition systems, which have many application areas, more challenging. In this paper, we present a comparison of different classification techniques that are widely used in computer vision for image classification...
The recognition and classification of the remote sensing image is a key technology in the application of remote sensing image, which has been one of the research hotspots. Extreme learning machine (EML), is a kind of machine learning method, which has advantages of fast learning speed and good generalization capability, and has been getting more and more attention from the researchers. Based on the...
Food recognition in still images is a problem that has been recently introduced in computer vision. The benchmark data sets used in training and evaluation of food recognition methods contain sample images of popular foods from the globe. However, when they are examined thoroughly, it can be observed that very few of them are Turkish dishes. In this study, we first carry out a data collection process...
One of the challenging issues in high-resolution remote sensing images is classifying land-use scenes with high quality and accuracy. An effective feature extractor and classifier can boost classification accuracy in scene classification. This letter proposes a deep-learning-based classification method, which combines convolutional neural networks (CNNs) and extreme learning machine (ELM) to improve...
The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter...
To collect various data, millions of probes and sensors mounted on a Google and Baidu street view vehicles, 360-degree view and GPS location. From millions of street sensors, image data of GB level is transmitted back per second. It is an important research topic to classify and identify images quickly. The experimental results yielded a 23% error rate of recognition on images collected by probes...
Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages...
Diabet is one of the metabolic trouble which is generally occurs genetic and environmental components. It happens increasing of blood level. In this study, diabet illness has been diagnosed with its features by classification with support vector machines (SVM) and artificial neural networks (multi layer perceptron). The method used for diagnosis is aritificial neural networks multi layer perceptron...
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