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 situation after the initial 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 process 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 combining them. This model focuses the analyses 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 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 in ten selected domains.

In this paper we study the use of the Brill tagger [5,6,7] for tagging Modern Standard Arabic (henceforth MSA) text. The Brill tagger is a famous public domain part of speech tagger, originally designed for tagging English text by implementing machine learning approach through the method of transformation rules. IT had been adopted for other languages by many researchers [17,19,22]. Some modifications are needed on the learner and tagger that are written partly in perl and partly in C programming languages, and are run under the unix/linux operating system. The main change is done on the initial state tagger, which is used by both learner and tagger. A program is written using the lexical analyzer Lex to capture Arabic morphological structures, and then interfaced with both learner and tagger. The tagset used in this work is a revised version of that introduced by Khoja [11]. The revision included changing some of the tags for linguistic considerations and introducing some new tags to make the set more powerful, or to make up for limitations in the original tagset that hinder tagging some words. The corpus is obtained from two Jordanian magazines, and has to go through a series of editing steps. A collection of lexical rules and contextual rules are obtained and applied to Arabic text. The tagging accuracy of the resulting tagged text is measured now to be an average of up to about 84% for both known and unknown words, A rate, which is very promising for such a complex language and rich tagset. We still hope for better performance.

This paper describes the design and fabrication of an intelligent Irrigation control system that allows intelligent control of the water applied to the field at right amounts and times. The system should have the capabilities to measure the water content in the soil so water can be applied as needed. The WATERMARK soil moisture sensor based on the measurement of the soil tension will provide the measurement of the water content. The system measures the soil tension, soil temperature and rain status and records the data in a file for future reference. It will apply water to field if a certain level of soil water tension is reached. This intelligent Irrigation control system is suitable for universities, research centers and farms where a control of the soil water content is required. The system can be used to study the water requirements for crops so irrigation can be scheduled efficiently.

An article published in The New York Times on March 20, 2009 gives a very clear message of Libyan citizens: “We don’t need money, we need roads, we need health care, education, and we need an economy”. There are only 13 doctors, 48 nurses, 34 hospital beds per 10,000 people in Libya. Similar to this, ample evidences are available to prove that Libya has a poor healthcare system

The rapid growth of advanced robots has given researchers unprecedented opportunities to explore and discover new fields of research where robots can be used to assist humans in their daily life. There are many applications that use robots and automation in different aspects of life such as industry, medical, domestic machines and etc. In this paper, the work has been devoted for the use of robotics and robots in cleaning process. The window cleaning robot is one of the robots that have emerged in recent decay. This robot can be used in homes, offices and large buildings. The main target is to design a robot that can clean glass windows efficiently and rapidly even in dangerous and hazardous places. The robot will be controlled using the Programmable Logic Controller PLC. The motion will generated by three servo motors.

Adaptive control systems have been studied for many years. There are many types of adaptive control algorithms, one of which is the reference model technique. In this paper, the work has been devoted for the use of model reference adaptive control system (MRAC). The MRAC algorithm with parameters adaptation was developed and applied for control of the DC motor drive. The algorithm is based on the gradient technique with parameter adaptation. The controller can significantly improve system behavior and force the system to follow the reference model and minimize the error between the model and plant output.

In this paper a data compression technique using double transforms is proposed. The compression ability of any transform or a combination of transforms is evaluated by the error in the reconstructed data and/or its fast implementation. This investigation concerns the reduction in the computational requirements for data compression without increasing, significantly, the error in reconstruction. The double transform approach is based on the transformation of input data using WHT, rejecting some of the spectral components, converting the spectrum from WHT domain into DCT domain, and then inverse transform using DCT. In the experimental part of this paper, several contours extracted from 2D images, using the FCCE method for contour extraction, ware used. It is shown that the proposed technique reduces the number of multiplication operations, comparing with DCT. In addition, the compression process does not require any multiplications at the transmitting end, thus can be used for real time compression applications.