Recently the number of distance education platforms has been increased significantly. These platforms provide isolation between the student and teacher. Thus, there is a need for predicting the students who are possible to fail in a specific course and take the precautions like starting face-to-face on demand lectures for individual cases. Both of artificial intelligence and data mining techniq...

Optimization of machining parameters is very important and the main goal in every machining process. Surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. In this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. This approach has both the learning capability of neural network and li...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

In this paper we present an approach to neuro{fuzzy classiication that is able to learn fuzzy sets and fuzzy rules from data. The fuzzy rules that are created by this approach can be very well interpreted, however, they do not classify as good as the rules derived by sophisticated fuzzy clustering algorithms. They on the other hand supply usually unsatisfying fuzzy sets what makes it hard to in...

This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO2 (tons of carbon dioxide...

In this work a neuro-fuzzy based model of a whey batch fermentation process by a strain Kluyveromyces marxianus var. lactis MC5 is presented. A three-layered neuro-fuzzy network is realized. The simulation results are compared with conventional models (based on mass balance and differential equations). The neuro-fuzzy model provides a better fitness and allows inclusion of linguistic variables ...

The mountain car problem is a well-known task, often used for testing reinforcement learning algorithms. It is a problem with real valued state variables, which means that some kind of function approximation is required. In this paper, three reinforcement learning architectures are compared on the mountain car problem. Comparison results are presented, indicating the potentials of the actor-onl...