Logistic regression is a non-linear modification of the linearregression. The purpose of the logistic regression analysis is tomeasure the effects of multiple explanatory variables which can becontinuous and response variable is categorical. In real life there aresituations which we deal with information that is vague innature and there are cases that are not explainedprecisely. In this regard,...

BACKGROUND Syndromic management of vaginal infections is known to have poor diagnostic accuracy. Logic regression is a machine-learning procedure which allows for the identification of combinations of variables to predict an outcome, such as the presence of a vaginal infection. METHODS We used logic regression to develop predictive models for syndromic management of vaginal infection among sy...

We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP faci...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

air conditioning is widely applied for the improvement of standard of living in human life. this present investigation focused on the prediction of indoor air quality characteristics of air conditioned car using fuzzy logic algorithm. the conditioned space was selected and the experiments were planned as per design of experiments to study the effect of human load, fresh air supply and air veloc...