The Artificial Neural Network (ANN) is a powerful data-driven model that can capture and represent both linear and non-linear relationships between input and output data. Hence, ANNs have been widely used for the prediction and forecasting of water quality variables, to treat the uncertainty of contaminant source, and nonlinearity of water quality data. However, the initial weight parameter pro...

Since seafood is highly susceptible to corruption, it is important to check their storage and shelf-life time. In this research, image processing technology was used to recognize the freshness (time lasted of catching) of shrimps. Shrimp samples were randomly selected from shrimp farming pools and stored in three storage conditions: freezer, refrigerator, and cool environments. Images were take...

Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor model, we proposed a novel soft sensor model based on artificial neural network...

this study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ann) in broiler chicken. artificial neural networks (anns) are powerful tools for modeling systems in a wide range of applications. the ann model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...

Introduction: Hypothyroidism is one of the frequent side effects of radiotherapy of head and neck cancers, breast cancer, and Hodgkin's lymphoma. It is recommended to estimate the normal tissue complication probability of thyroid gland using radiobiological modeling during treatment planning. Moreover, the use of artificial neural network is also proposed as a new method for t...

in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...

background : in medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. due to the limitations of usual statistical models, other methods such as artificial neural network (ann) and hybrid models could be used. in this paper, we propose a hybrid artificial neural network-genetic algorithm (ann-ga) model to predictio...

Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the incre...

Characterizing and modeling the intrinsic light response of rat ganglion-cell photoreceptors 1 2 Olivia J. Walch, L. Samantha Zhang, Aaron N. Reifler, Michael E. Dolikian, Daniel B. Forger and 3 Kwoon Y. Wong 4 5 Department of Mathematics, University of Michigan, Ann Arbor MI 48109. 6 Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor MI 48105. 7 Department of Comp...

load modeling is widely used in power system studies. two types of modeling, namely, static and dynamic, are employed. the current industrial practice is the static modeling. static modelss are algebraic equations of active and reactive power changes in terms of voltage and frequency deviations. in this paper, a component based on static modeling is employed in which the aggregate model is deri...