Machine learning is an area where both symbolic and neural approaches have been heavily investigated. However, there has been little research into the synergies achievable by combining these two learning paradigms. A hybrid approach that combines the symbolically-oriented explanation-based learning paradigm with the neural back-propagation algorithm is described. Most realistic problems can nev...

The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., subpixel targets), making extractin...

Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffi...

In this paper, we propose a Wavelet Neural Network with Hybrid Learning Approach (WNN-HLA). A novel hybrid learning approach, which combines the on-line partition method (OLPM) and the gradient descent method, is proposed to identify a parsimonious internal structure and adjust the parameters of WNN-HLA model. First, the proposed OLPM is an online method and is a distance-based connectionist cl...

Evolutionary Algorithms are powerful optimization techniques which have been applied to many different problems, from complex mathematical functions to real-world applications. Some studies report performance improvements through the combination of different evolutionary approaches within the same hybrid algorithm. However, the mechanisms used to control this combination of evolutionary approac...

This paper proposes a hybrid approach based on neuro fuzzy model and emotional learning for prediction of stock exchange market. Neuro fuzzy models are powerful in modeling and forecasting highly nonlinear and complex time series. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the pred...

Relation extraction is a very useful task for several natural language processing applications, such as automatic summarization and question answering. In this paper, we present our hybrid approach to extracting relations between Arabic named entities. Given that Arabic is a rich morphological language, we build a linguistic and learning model to predict the positions of the words that express ...

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT–SVM) and an ensemble approach combining the base ...

guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of intelligent transportation systems (its). nowadays, many complex systems can be controlled by using multi agent systems. adaptation with the current condition is an important feature of the agents. in this research, formulation of dynamic guidance for vehicles has been investigated based...

3D virtual worlds are recognized as collaborative learning environments. However, the underlying technology is not sufficiently mature and the virtual worlds look cartoonish, unlinked to reality. Thus, it is important to enrich them with elements from the real world to enhance student engagement in learning activities. Our approach is to build learning environments where participants can either...