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In recent years, deep learning has achieved great success in a series of areas, but there is few published work on deep learning for job recommendation. Most researchers focus on the application of traditional algorithms, most of which still use algorithms like collaborative filtering and content-based filtering. In this paper, we study and improve the existing recommender algorithm based on deep...
Image categorization is the process of categorizing the images into its respective class or bins. It is still challenging problem in computer vision key area. The existing methodologies for image categorization like semantic modelling approaches, neural network approaches does not provides an accurate solution. This is due to inefficient feature extraction and their processing. Deep Learning is a...
To improve software reliability, software defect prediction is utilized to assist developers in finding potential bugs and allocating their testing efforts. Traditional defect prediction studies mainly focus on designing hand-crafted features, which are input into machine learning classifiers to identify defective code. However, these hand-crafted features often fail to capture the semantic and structural...
The “semantic gap” issue which exists the low-level image pixels captured by machines and high-level semantic concepts perceived by human has always been a key challenge for lots of applications, such as computer vision, pattern recognition, Content-based Image Retrieval (CBIR). The recent success of deep learning researches bring a hope for bridging the semantic gap. Many researchers have attempted...
In this paper, a novel multi-label classification model using convolutional neural networks (CNNs) is proposed. As one of the deep learning architectures, CNNs lead breakthrough in many fields of image processing especially the image classification. Since the applications of CNNs are more concentrating in the background of single-label samples, our model introduce the hidden semantic between different...
With the development of the Internet, it is vital for the security of the Internet to detect web-based anomalies. Clustering based on feature extraction by manually has been verified as a significant way to detect new anomalies. But the presentations of these features can't express semantic information of the URLs. In addition, few studies try to cluster the anomalies into specific types like SQL-injection...
Aiming at the problem that the traditional single neural network method is limited in feature dimension extraction, a new deep-fusion convolutional neural network is proposed. It uses two kinds of different representations (i.e., word vector and shortest dependency path) as different inputs of convolutional neural network, therefore, it is capable to learn more dimension text features automatically...
Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound...
Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
Land cover classification is a task that requires methods capable of learning high-level features while dealing with high volume of data. Overcoming these challenges, Convolutional Networks (ConvNets) can learn specific and adaptable features depending on the data while, at the same time, learn classifiers. In this work, we propose a novel technique to automatically perform pixel-wise land cover classification...
The two main problems of biomedical event extraction are trigger identification and argument detection which can both be considered as classification problems. In this paper, we propose a distributed representation method, which combines context, consisted by dependency-based word embedding, and task-based features represented in a distributed way on deep learning models to realize biomedical event...
The amount of electronic medical documents is growing rapidly every day. While they carry much information, it becomes more and more difficult to manually process it. Our work represents small steps towards automatic knowledge extraction from medical documents using deep learning and similarity based methods. Our goal here is to identify in an unsupervised manner relations between known medical concepts...
Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human...
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning...
With rapid development of E-commerce platforms, automated review sentiment analysis for commodities becomes a research focus, with main purpose to extract potential information within reviews for decision making of consumers. Traditional methods have made some progress on document level sentiment analysis, but with tremendous increasing of data scale, how to process high dimension of data fast and...
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