The increasing scale and sophistication of network attacks have become a major concern for organizations around the world. As result, there is an demand effective accurate classification to enhance cyber security measures. Most existing schemes assume that available training data labeled; is, based on supervised learning. However, this not always case since real expected be unlabeled. In paper,...

We suggest to apply the hybrid neural network based on multi layer perceptron (MLP) and adaptive resonance theory (ART-2) for solving of navigation task of mobile robots. This approach provides semi supervised learning in unknown environment with incremental learning inherent to ART and capability of adaptation to transformation of images inherent to MLP. Proposed approach is evaluated in exper...

Background and Aim: With the advent of problem-based learning (PBL), pioneer universities across the world started using this method to improve learning quality. One of its modifications, hybrid-PBL, incorporates a combination of PBL and traditional teaching skills. This study compared the efficacy of hybrid PBL and the conventional instruction in terms of preclinical test scores and clinical p...

This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniques. One of the main feature of this algorithm is the application of automata theory to formalize the problem of decision tree induction and the use of a hybrid approach, which integrates both syntactical and statistical strategies. Some experimental results are also presented indicating that the...

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology induction-motor fault detection. Some the parameters such as stator currents and vibration signals provide great deal information about motor’s conditions. Therefore, these were selected to test proposed model. The was assessed in laboratory under he...

In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Cl...