nasim nourafza

Alzheimer’s disease is one of the most common diseases in the 21st century. Alzheimer's patients lose their brain cells gradually and eventually die. It is often diagnosed when the symptoms appear and little work can be done for the patient. Using of learning algorithms is useful for diagnosing of Alzheimer. Previous studies used Support Vector Machine, K-Nearest Neighbor, and Linear Discriminant Analysis in order to diagnose the disease. These methods have some problems such as low accuracy, high computation complexity or high execute time. Therefore in this research, a method based on brain emotional learning and wavelet feature is used. First, the white and gray matters of the brain were separated by a threshold selection method. Second, the texture properties of the images were extracted by wavelet transform algorithm. Third, the dimensional reduction is done on the properties extracted by principal component analysis. Finally, the features were classified using Brain Emotional Learning Algorithm and Brain Emotional Learning Based Pattern Recognizer. Results showed that run time of brain emotional learning algorithm is 0.22 second and Brain Emotional Learning algorithm with 95% accuracy and Brain Emotional Learning Based Pattern Recognizer with 97% accuracy are better than Support Vector Machine with 83% accuracy.

Sugarscape model is a multi-agent environment used for modeling and organizing processes such as social, political and economic processes. The purpose of this study is to assess the learning ability of a learner system in sugarscape and for that reason the Boltzmann machine learning algorithm is evaluated. During the first experience, model number 1 which is a learned multi-agent model and is based on Boltzmann machine algorithm was considered. In this model each agent, was assigned with a parameter which indicates the agent’s knowledge. Once the knowledge of agents reached the maximum, the model is converged. The criterion was time of convergence. After that, the second model which is a multi-agent, cellular, and learner based on Boltzmann machine learning algorithm was considered, in which the Boltzmann machine’s learning algorithm is implemented in a multi-agent cellular environment. And finally the third model which is a multi-agent, cellular, Boltzmann machine learner model was assessed in sugarscape. And measure the time needed to reach convergence over specific number of agents for each model. After investigating the resulting diagrams it was concluded that the convergence speed of third model is more than the convergence speed of second model. Also, the convergence speed of second model is more than convergence speed of first model. Utilization of cellular automata results in a more speedy convergence for the model and this is due to rules and local interactions and transformation of generalized convergence to localized convergences. Application of sugarscape accelerates convergence speed relative to the case when it is not used. Additionally the maximum number of executable agents in third model is more than second model and also the maximum number of executable agents in second model is much greater than the first model. At last, it was deduced that the learning process of a learned multi-agent system and in sugarscape speeds up this process and does not have the limitations in the number of agents during execution.