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The present work includes the temporal modeling of the oviposition activity of the Aedes aegypti mosquito, a vector of viral diseases such as Dengue, Chicungunya and Zika, based on time series of data extracted from earth observation satellite images. Unlike previous works, Machine Learning techniques that are capable of capturing nonlinear relationships between variables, such as artificial neural...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
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...
Affected by the special geographical environment and climate factors, some cities waterlogging occurred frequently, causing serious economic losses and social impacts. Because of certain topographic factors, once the heavy rain coming suddenly in the city, many of the major streets will be flooded by the water, how to better prevent the occurrence of waterlogging, or predict the depth of waterlogging...
On the basis of the information fusion idea, a novel multiple information fusion modeling method is proposed. Several artificial neural networks are used to fuse the information of data. And then the results of information fusion by ANNs will be fused again according to their performance. Using the novel multiple information fusion scheme, a new modeling approach is presented to establish the prediction...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
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