In recent years, the rapid development of computer technology has made great progress in the field of smart health-care. Using data mining technology can obtain useful information in medical big data, discover the correlations among diseases and achieve the prevention and control of diseases. However, the traditional mining algorithms can no longer satisfy the demands of medical big data, it is an important direction of future research to improve and optimize the algorithms to make them applicable to the medical field. Based on this, this paper improves Apriori algorithm through parallel processing, matrix compression, and the introduction of lifting rate and interest. The optimized algorithm is called CI-Apriori, and an association rule model of diabetes complications based on CI-Apriori is proposed. The experimental results show that CI-Apriori has a great improvement in time and space efficiency, and can mine the strong association rules among diabetes complications faster and more effectively, so as to find the medical laws for the prevention and treatment of diseases.

Prediction of bulking sludge is a matter of growing importance around the world. In this study, to detect bulking sludge of wastewater treatment process (WWTP), an intelligent detection method, using a self-organizing recurrent radial basis function neural network (SORRBFNN) and a cause variables identification (CVI) algorithm, was developed to detect the fault points and the fault variables of bulking sludge. For this intelligent detection method, first, the structure and parameters of SORRBFNN were updated by an information-oriented algorithm (IOA) and an improvedLevenberg-Marquardt (LM) algorithm to improve the prediction accuracy of the sludge volume index (SVI) from the water qualities. Second, the CVI algorithm was designed to allow a quick revealing of the cause variables of bulking sludge with high accuracy. And the intelligent detection method was tested on the measured data from a real WWTP. Experimental results confirmed the attractiveness and effectiveness of the proposed intelligent detection method. (C) 2018 Elsevier Ltd. All rights reserved.

In this paper, an efficient self-organizing recurrent radial basis function neural network (RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a two-steps learning approach is introduced during the learning process. In the first step, the objective is to find the optimal set of parameters using an improved Levenberg-Marquardt (LM) algorithm. In the second step, an efficient information-oriented algorithm (IOA), without any thresholds, is developed to optimize the structure of RRBFNN. The hidden neurons in this IOA-based RRBFNN (IOA-RRBFNN) are generated or pruned automatically to reduce the computational complexity and improve the generalization power. Meanwhile, a theoretical analysis on the learning convergence of IOA-RRBFNN is given in details. To demonstrate the merits of IOA-RRBFNN for modeling nonlinear systems, several benchmark problems and a real world application are present with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed IOA-RRBFNN performs prediction accuracy in the case of fast learning speed and compact structure. (C) 2017 Elsevier B.V. All rights reserved.

This paper investigates how to construct a recurrent radial basis function neural network (RRBFNN) by an information-oriented algorithm (IOA) and how to adjust the parameters by a gradient algorithm simultaneously. In this IOA-based RRBFNN (IOA-RRBFNN), the proposed IOA is used to calculate the information processing strength (IPS) of hidden neurons, such that the independent component contributions between the hidden neurons and output neurons can be extracted. Then, a novel self-organizing strategy is proposed to optimize the structure of RRBFNN based on the input IPS and output IPS of hidden neurons. Meanwhile, a gradient algorithm is developed to update the parameters of IOA-RRBFNN. The proposed IOA-RRBFNN can be used to organize the network structure and adjust the parameters to improve its performance. Finally, several examples are presented to illustrate the effectiveness of IOA-RRBFNN. The results demonstrate that the proposed IOA-RRBFNN is more competitive in solving the nonlinear system modeling problems compared with some existing methods.

In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values. (C) 2015 Elsevier B.V. All rights reserved.

Due to the diversity of body movements and uncertainty of recording occasion, human action recognition is still a challenging task, especially in real world. This paper provides a new method of representing the video with mid-level vision representation which is extracted from the discriminative supervoxels. In the proposed method, the discriminative supervoxels we extracted through a learning phase frequently occur within class and are distinguishing enough between classes. They contain the meaningful parts of the video, including specific background of an action and the moving human body. The video is first oversegmented to obtain supervoxels, which are described by the dense trajectories and Bag-Of-Words framework. Afterwards, the discriminative supervoxels are extracted by an iterative procedure through training and selecting. Finally the videos are represented with discriminative supervoxels. Experimental results on KTH, YouTube and UT-Interaction datasets demonstrate comparable performance with state-of-the-art models.