<span lang="EN-US">Cardiovascular diseases remain the leading cause of death, taking an estimated 17.9 million lives each year and representing 31% of all global deaths. The patient records including blood reports, cardiac echo reports, and physician’s notes can be used to perform feature analysis and to accurately classify heart disease patients. In this paper, an incremental deep learning model was developed and trained with stochastic gradient descent using feedforward neural networks. The chi-square test and the dropout regularization have been incorporated into the model to improve the generalization capabilities and the performance of the heart disease patients' classification model. The impact of the learning rate and the depth of neural networks on the performance were explored. The hyperbolic tangent, the rectifier linear unit, the Maxout, and the exponential rectifier linear unit were used as activation functions for the hidden and the output layer neurons. To avoid over-optimistic results, the performance of the proposed model was evaluated using balanced accuracy and the overall predictive value in addition to the accuracy, sensitivity, and specificity. The obtained results are promising, and the proposed model can be applied to a larger dataset and used by physicians to accurately classify heart disease patients.</span>

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The aim of this paper is to study the problem of control for a fishery fuzzy system model by using an output feedback controller, in order to regulate the stock states around a non-trivial equilibrium. In our case, the continuous fishery model is considered, where the effort is used as a control term, the age classes as a states and the quantity of captured fish per unit of effort as a measured output. The continuous non-linear model is first represented by a Takagi Sugeno model. Next, we develop a technique for designing an output feedback control law which globally stabilises this fuzzy system model. A design method for constructing the output controller is given. A numerical simulation example is given to show the potential of the proposed techniques.978-1-4244-8092-0/10/$26.00 ©2010 IEEE