Photosynthesis is a process that indicates the productivity of crops. The estimation this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it essential to know: input/output variables their units; modeling used its classification (empirical, or hybrid);...

This work describes a first population pharmacokinetic (PK) model for free and total cefazolin during pregnancy, which can be used for dose regimen optimization. Secondly, analysis of PK studies in pregnant patients is challenging due to study design limitations. We therefore developed a semiphysiological modeling approach, which leveraged gestation-induced changes in creatinine clearance (CrCL...

Stock market prediction has been a challenging task due to the nature of the data which is very noisy and time varying. However, this theory has been faced by many empirical studies and a number of researchers have successfully applied machine learning approaches to predict stock market. The problem studied here is about stock prediction for the use of investors. It is true investors usually ge...

In this paper, we propose a hybrid decision model for supporting the ranking financial status of corporations using case-based reasoning augmented with genetic algorithms and the fuzzy nearest neighbor method. An empirical experimentation on 746 cases was conducted that shows that the average accuracy of the ranking is about 92% and 80% for the first order and the second order, respectively. Th...

Reinforcement is an increase in premating reproductive isolation between taxa resulting from selection against hybrids. We present a model of reinforcement with a novel type of selection on female mating behavior. Previous models of reinforcement have focused on the divergence of female mating preferences between nascent species. We suggest that an increase in the level of female mating discrim...

in a large scale cyclic storage system ,as the number of rule parameters and/or number of operating period increase, general purpose gradient-based nlp solvers and/or genetic algorithms may loose their merits in finding optimally feasible solution to the problem. in these cases hybrid ga which decomposes the main problem into two manageable sub-problems with an iterative scheme between ga and l...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

This paper describes an original strategy for using a datadriven probabilistic motion model into particle filter-based target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The hybrid discrete-continuous probabilist...

In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on th...

This paper describes a method for storing, modeling, and processing an individual’s travel history collected with a GPS enabled device. The technique uses a general framework called Hybrid Dynamic Mixed Networks (HDMNs), which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We use this framework to model a person’s...