Keywords : Artificial intelligence


The Linguistic Connotations of the Word Light in the Holy Quran (An analytical study of Quranic verses using Artificial intelligent techniques)

Nima A. Al-Fakhry

AL-Rafidain Journal of Computer Sciences and Mathematics, 2020, Volume 14, Issue 2, Pages 13-24
DOI: 10.33899/csmj.2020.167343

The Holy Quran is a sea of words, articulations, phrases, regulations, laws, and judgments. Therefore, when we dive in the Quran verses we need a large amount of information in various aspects to achieve the required knowledge. The word (Al-Noor) is one of the Quran's vocabularies, which enjoys a special place, and this privacy came from the specificity of the Quran and its sanctity. The word (Al-Noor) has one pronunciation and many meanings and vocabulary.
The research has sought to know God's lights: “the science, the guidance, the kernels, and faith “the closest and most intense and congregated of the verse (35/Al-Noor).  Furthermore, this verse was chosen due to it speaks about the Sultan of Allah Almighty and god's light. Finally, the research has used the algorithm of “subtractive clustering and weighted subtractive clustering” measured and Matlab language (2013) to achieve the practical aspect of the study.
 

Rule Based Planning for Solving Hanoi Problem

safwan hasoon

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 51-59
DOI: 10.33899/csmj.2013.163546

In this research, the planning intelligence technique has been developed and applied to solve the tower  of Hanoi puzzle through the construction of the rule based included a set of facts and rules under certain conditions to describe this problem. The tower problem of Hanoi consists of different size disks and three pegs. The proposed system is to transfer the disks from the initial state to the goal state by using some rules. The rule based is used from planning to get the goal by applying different operations. The intelligence techniques used are contributed to reduce time and memory (state space) compared with traditional planning depended on human aid which spends more time and memory, since this technique solved a problem in the depended are planning the approach without human aid. The prolog language is used to implement the computer simulation program for the proposed system.
 

Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images

Maha Abdul Rahman Hasso; Aseel Ismail Ali

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 63-79
DOI: 10.33899/csmj.2012.163719

Ant Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails of real ant with additional capabilities to make it more effective such as a memory to save the past events. Every ant build solutions to the problem, and uses the information grouped about the features and performance of the private problem, to change the look to the ant problem.
In this work, an edge detection technique based on Ant Colony Optimization is used by selecting pheromone matrix which represents the information about edges in each pixel based on the guidelines set up by the ant on the image. Multiple values for different sizes of neighbor pixels are applied and a heuristic information function to test results is proposed. The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. A comparative study is also given illustrating the superiority of the proposed algorithm.
 

Detection of network anomaly based on hybrid intelligence techniques

Shahbaa I. Khaleel; Karam mohammed mahdi saleh

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 81-98
DOI: 10.33899/csmj.2012.163720

Artificial Intelligence could make the use of Intrusion Detection Systems a lot easier than it is today. As always, the hardest thing with learning Artificial Intelligence systems is to make them learn the right things. This research focuses on finding out how to make an Intrusion Detection Systems environment learn the preferences and work practices of a security officer, In this research hybrid intelligence system is designed and developed for network intrusion detection, where the research was presented four methods for network anomaly detection using clustering technology and dependence on artificial intelligence techniques, which include a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to develop and improve the performance of intrusion detection system. The first method implemented by applying traditional clustering algorithm of KM in a way Kmeans on KDDcup99 data to detect attacks, in the way the second hybrid clustering algorithm HCA method was used where the Kmeans been hybridized with GA. In the third method PSO has been used. Depending on the third method the fourth method Modified PSO (MPSO) has been developed, This was the best method among the four methods used in this research.
 

Design a Fuzzy Expert System for Liver and Pancreas Diseases Diagnosis

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 2, Pages 129-141
DOI: 10.33899/csmj.2010.163891

Fuzzy logic is a branch of artificial intelligence techniques, it deals with uncertainty in knowledge that simulates human reasoning in incomplete or fuzzy data. Fuzzy relational inference that has applied in medical diagnosis was used within the medical knowledge base system to deals with diagnostic activity, treatment recommendation and patient's administration.
            In this research, a medical fuzzy expert system named (Liv&PanFES) has been developed for diagnosis and decision making of general Liver and Pancreas diseases.
       The (Liv&PanFES) is a rule based fuzzy expert system, results of laboratory analysis are inserted into the system. This system can define the probable diagnosis on these data, and later on it can pick out the most probable one for disease.