In this paper, an expert system that performs route planning using dynamic traffic data is introduced. Also an algorithmic approach is introduced to find the shortest path in a three-dimensional. Using both implementations, a comparison is made between the expert system approach and the algorithmic approach. It is concluded that the expert system shows great potential. The expert system indeed finds the best routes, and it outperforms the algorithm approach in computation time, too.
In the marketing area, new trends are emerging, as customers are not only interested in the quality of the products or delivered services, but also in a stimulating shopping experience. Creating and influencing customers' experiences has become a valuable differentiation strategy for retailers. Therefore, understanding and assessing the customers' emotional response in relation to products/services represents an important asset. The purpose of this paper consists of investigating whether the customer's facial expressions shown during product appreciation are positive or negative and also which types of emotions are related to product appreciation. We collected a database of emotional facial expressions, by presenting a set of forty product related pictures to a number of test subjects. Next, we analysed the obtained facial expressions, by extracting both geometric and appearance features. Furthermore, we modeled them both in an unsupervised and supervised manner. Clustering techniques proved to be efficient at differentiating between positive and negative facial expressions in 78\% of the cases. Next, we performed more refined analysis of the different types of emotions, by employing different classification methods and we achieved 84\% accuracy for seven emotional classes and 95\% for the positive vs. negative.
In this paper, we describe the application of a combined neocognitron
type of the neural network classifier in a generic Car License Plate Recognition (CLPR) system. The suggested system contains an image processor, a segment processor and five conpled neocognitron network classifiers that act as a character recognizer. The presented model of the system depends neither on the specific license plate image features nor on the license plates character style and size. Combining neocognitron classifiers were motivated by the fact that manually tuning a training set for a large neocognitron network is tedious. It is shown how the training set tuning for a large neocognitron network can be avoided. By connecting srnall neocognitrons specifically trained on ambiguous character classes, the performance of the recognizer in our CLPR was improved easily. The use of a neocognitron recognizer contributes significantly to the generality of a CLPR systém. Besides, character recognition rates of 94% are realized using the proposed neocognitron.
It has been proved that the emotional state of a car driver has a great impact on his driving performance. Emotions can be triggered by internal stimuli (stress) or external stimuli (aggressive scenes). In this paper we propose a system of classification of certain emotional states by analysis of EEG recordings. We present the results of two experiments. In one experiment we recorded EEG data of car drivers in a simulated environment under conditions with a varying stress level. In the other experiment we presented pictures of emotional situations to car drivers. It proves that we were able to assess the state of respondents under extreme emotions.