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Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM,...
In order to solve the classification prediction of dust pollution at different altitudes, the least square support vector machine(LS-SVM) and BP neural network is used to construct the distribution model. Built by LS-SVM, the accuracy of the model was verified by BP neural network with the realtime dust pollution data of different high monitored by Unmanned aerial vehicles. The data analysis shows...
The CNN-RNN design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its transform as the image embedding that provides the interface between the CNN and RNN. This leaves the RNN overstretched with two jobs: predicting the visual concepts and modelling their...
predicting drug side effects is a critical task in the drug discovery, which attracts great attentions in both academy and industry. Although lots of machine learning methods have been proposed, great challenges arise with boom of precision medicine. On one hand, many methods are based on the assumption that similar drugs may share same side effects, but measuring the drug-drug similarity appropriately...
The prediction of the track quality (TQI) is paid much more attention than ever before since the steel track is directly related to the safety and the comfort of the railway vehicles. In this paper, a method so called Markov-Grey GM(1,1) model is applied for predicting the TQI. The result of the comparison between this new method and the traditional grey prediction theory shows that the precision...
Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel radial basis function neural networks (RBFNN). The proposed implementation of RBFNN allows parallel computation therefore expedites the convergence of training process. The proposed model also employs a new hybrid chaotic genetic...
With the development of social media websites, more and more users start to show their attitudes and emotions to each other. Some of these interactions can be represented as links with sign values(positive or negative). In this paper, a unified method is proposed for link prediction and feature analysis. This paper focuses on the data from social media websites and tries to find the features that...
According to Guangxi agriculture Industrialization, modern agricultural Industrialization competitiveness model is established based on the Grey systems theory, which evaluates and predicts the modern agricultural industrialization competitiveness in Guangxi Beibu Bay Economic Zone. Moreover, modern agricultural Industrialization development process in Yulin City is effectively analyzed referring...
It plays a crucial role in autonomic logistics or maintenance decision-making on condition to forecast equipment health status. However it was influenced by many various factors with complexity as variable, strong coupling, nonlinear and dynamic. The difficulty to forecast equipment health status lies in treating time sequence characteristic of health status index and complexity characteristic of...
In this study, a comprehensive system was developed to meet the demand of the Security Guarding during 2008 Beijing Olympic Games. In the system, meteorological models, namely, MM5 and RAMS6.0, and a poisonous clouds diffusion model over complex terrain (CDM) were configured in a one-way off-line nested way. In the system, MM5 runs were performed in an real-time operational way with a horizontal resolution...
This paper presents a minimum generation error (MGE) training method for hidden Markov model (HMM) based prediction of articulatory movements when both text and audio inputs are given. In this method, MGE criterion is adopted to replace the maximum likelihood (ML) criterion to estimate model parameters for the unified acoustic-articulatory HMMs. Different from the MGE training for HMM-based acoustic...
Data assimilation is an advanced and innovative set of techniques for variable estimation, especially in agriculture research in recent years, which integrates not only remote sensing data products, other measurements, but also land dynamic models. It can provide more abundant and precise data set, and extend data's spatiotemporal scale. In this paper, firstly composition and assimilation methods...
This paper presents a charge-based compact model for the arbitrary doped long-channel cylindrical surrounding-gate (SRG) MOSFETs. Starting from Poissonpsilas equation with fixed charge and inversion charge terms, an accurate equation of inversion charge is obtained with the full-depletion approximation. Substituting this inversion charge expression into Pao-Sahpsilas dual integral, a drain current...
A complete surface potential-based current-voltage and capacitance-voltage core model for cylindrical undoped surrounding-gate (SRG) MOSFETs is presented in this paper. This model allows the current-voltage (IV) and capacitance-voltage (CV) characteristics to be adequately described by a single set of the equations in terms of the surface potential. The model is valid for all operation regions and...
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