With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.
Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection and a subsequent classification using latent information. Precisely, a typical autoencoder architecture is employed for reconstructing the original input. Then the sparse coding technique is applied to optimize the network structure, with the aim of selecting optimal features and enhancing the generalization capability. In addition, the refined latent information is further cast as alternative features for training a sparse classifier. To evaluate the performance of the proposed autoencoder architecture, we present a comprehensive analysis using a publicly available data repository (i.e., Surveillance, Epidemiology, and End Results, SEER). Experimental study shows that the proposed approach has the ability of extracting important features from high-dimensional inputs and achieves competitive performance than other state-of-the-art classification techniques.
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