Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

Evaluation of artificial intelligence based models for chemical biodegradability prediction.

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability...

Bayesian Prediction for Artificial Intelligence

This paper shows that the common method used for making predictions under uncertainty in AI and science is in error. This method is to use currently available data to select the best model from a given class of models-this process is called abduction-and then to use this model to make predictions about future data. The correct method requires averaging over all the models to make a prediction-w...

Prediction Based Diagnosis Using Artificial Intelligence Methods

Wind power prediction with different artificial intelligence models

In this paper different prediction models based on methods of the artificial intelligence are studied for wind power prediction of single wind farms. The used methods are neural networks, mixture of experts, support vector machines and nearest neighbour search with a superior particle swarm optimization. We build for day-ahead prediction and for short-term prediction with a prediction horizon o...

Comprehensible artificial intelligence-based models for raveling of porous asphalt

Among a large number of existing artificial intelligence (AI)-based techniques, artificial neural network (ANN) is one of the most widely used techniques, which has been applied to many road engineering problems. They are especially useful as predictive models because they do not require prior knowledge of the input data distribution and have shown high prediction performance. However, despite ...