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We propose in this paper a deep learning model based on convolutional neural network (CNN) for biopsy image grading. The model outputs a vector of scores indicating presence or severity of the target histopathological characteristics. Within the model, we first design a 7-layer CNN for feature representation and high level concept extraction. Each biopsy image is expressed as a feature vector through...
A novel biometric recognition system is designed in this paper, using ground reaction force (GRF) measurements of continuous gait. Original GRF signals are combined in three directions Respectively. Waveform interpolation and align ment are performed to meet th e demand of feature extraction ba sed on wavelet packet (WP) decomposition, re-sampling approach is utilized to expand valid training sets...
In the remote sensing data processing the key in extracting thematic information is to compartmentalize the background and the anomaly. Anciently we use the singleness threshold parameter to extract the anomaly information in the same area. Actually different landscape area have different thematic information background, namely it have background spatial differentiation characteristic. So when we...
Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results...
Named entity recognition (NER) is low-level semantics technology. Since it is simple and efficient, it has been widely applied in many systems such as machine translation, information retrieval, information extraction, question answering and summarization. The goal of named entity recognition is to classify names into some particular categories from text, such as the names of people, places, and organizations...
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