We present a model of web-based learning for teaching Visual Programming in Delphi, by adopting individual learning styles. A software system was developed for this purpose where two learning models (namely; 4MAT system and PBL system) are implemented. The goal of this research is to examine the influence of the two selected models on students of different learning styles enrolled in a web-based programming course.

Students were randomly divided into two groups, one for each learning model, and are given access to a website designed specifically for this purpose. Pre and post-tests were carried out.

A controlled evaluation is currently underway to assess the impacts of the system on students programming skills as well as their attitudes surrounding the use of available web facilities.

This paper describes the design and fabrication of an intelligent gate system that allows only certain vehicles or cars passing through the gate. It is very important to design an intelligent gate to control the flow of entered people or vehicles to the premises. The gate system should have capabilities to control the gate, where only specified vehicles or cars are allowed to enter the premises. The gate will open when the known vehicles are identified. The gate will not open if unauthorized vehicles try to pass. The system is a combination of mechanical, electrical and electronic components. It is one of the mechatronics engineering products. This intelligent gate system is suitable to be installed at universities, companies where only certain vehicles are allowed to enter. The list of certain vehicles must be stored in the database of the system. When the unknown vehicles try to pass through the gate, the gate will show a warning signal to the driver that they are unauthorized.

This paper presents empirical methods for enhancing the accuracy of inductive learning systems. It

addresses the problems of: learning propositional production rules in multi-class classification tasks in

noisy domains, maintaining continuous learning when confronted with new situations after the learning

phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation processes, allows accurate

classifications to be made on real-world data sets. The paper presents the system ARIS which

implements this approach, and it is shown that the resulting classifications are often more accurate than

those made by the non-refined knowledge bases.

The core design decision that lies behind ARIS is that it employs an ordering of the rules according to

their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights for the rule’s

conditions and to combine them. This model focuses the analysis of the knowledge base and assists the

refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments that

appear to be most consistent with the refinement data set. The design framework of ARIS consists of a

tabular model for expressing rule weights, and the relationship between refinement cases and the rules

satisfied for each case to focus the refinement process. The system has been used to refine knowledge

bases created by ARIS itself, as well as to refine knowledge bases created by the RIPPER and C4.5

systems [6,25] in ten selected domains. Two main advantages have been observed. First, the ability to

gradually improve the knowledge base as the refinement proceeds. Second, is the ability to learn strong

rules utilising condition’s weight using minimum covering algorithm. Thus, by taking account of this

information we improve the applicability and quality of refinement