Discovery Log Parsing from Tree-Structured Ordinal Data – This paper presents the development of a Deep Learning-based framework for the identification of human face attributes. This framework requires a large number of attributes to be annotated, which in turn enables the classification of the images by the classifier using the classification process. We propose a novel image recognition framework inspired by the human face similarity (HVS) framework: a deep neural network (DNN) to efficiently identify human face attributes belonging to the same type of facial expression (e.g., eyebrows or hair) and its variations. The framework extends the proposed DNN model to automatically classify these attributes by incorporating feature learning. The framework enables the identification of different facial attributes, allowing the classification of human face attributes in an end-to-end manner. The framework, which we describe in a detailed manner, is trained for image classification, face detection and human face attribute recognition tasks. This framework is a key component for future research in these fields.
The use of an accurate quantitative analysis of prices of pharmaceutical chemicals could be of great importance. Such a quantification is difficult to estimate due to the large and extensive amount of information available in scientific literature. To address this concern, we have developed an application to the analysis of prices produced by chemists at various stages of a drug research process. We used a data set of 442 drug patents on synthetic chemistry which was processed for product development and approval applications. The data from 442 patents showed that prices of the pharmaceutical chemical were determined accurately by two methods. The first one was a graph-based technique and the other one was a statistical approach. The data set was used to create a graph of prices of the pharmaceutical chemical. The graphs were then used to estimate the price of the chemical using a novel quantitative method based on linear classification of all data. This approach is a step towards the use of these prices for drug approval applications. The graph-based method was applied to evaluate the approval processes for a specific drug. The results show that the graph-based methodology outperforms a statistical method only once.
Learning Gaussian Process Models by Integrating Spatial & Temporal Statistics
Determining the optimal scoring path using evolutionary process predictions
Discovery Log Parsing from Tree-Structured Ordinal Data
On the convergence of the dyadic adaptive CRFs in the presence of outliers
An Unsupervised Linear Programming Approach to Predicting the Prices of Chemicals Synthetic ChemicalsThe use of an accurate quantitative analysis of prices of pharmaceutical chemicals could be of great importance. Such a quantification is difficult to estimate due to the large and extensive amount of information available in scientific literature. To address this concern, we have developed an application to the analysis of prices produced by chemists at various stages of a drug research process. We used a data set of 442 drug patents on synthetic chemistry which was processed for product development and approval applications. The data from 442 patents showed that prices of the pharmaceutical chemical were determined accurately by two methods. The first one was a graph-based technique and the other one was a statistical approach. The data set was used to create a graph of prices of the pharmaceutical chemical. The graphs were then used to estimate the price of the chemical using a novel quantitative method based on linear classification of all data. This approach is a step towards the use of these prices for drug approval applications. The graph-based method was applied to evaluate the approval processes for a specific drug. The results show that the graph-based methodology outperforms a statistical method only once.
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