Clustering is one of the essential machine learning algorithms. Data is not labeled in clustering. The most fundamental challenge in clustering algorithms is to choose the correct number of clusters at the beginning of the algorithm. The proper performance of the clustering algorithm depends on selecting the appropriate number of clusters and selecting the optimal right centers. The quality and an optimal number of clusters are essential in algorithm analysis. This article has tried to distinguish our work from other writings by carefully analyzing and comparing existing algorithms and a clear and accurate understanding of all aspects. Also, by comparing other methods using three criteria, the minimum internal distance between points of a cluster and the maximum external distance between clusters and the location of a cluster, we have presented an intelligent method for selecting the optimal number of clusters. In this method, clusters with the lowest error and the lowest internal variance are chosen based on the results obtained from the research. تفاصيل المقالة

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weighted average are not able to produce safe outputs when obtaining a correct output is impossible and also both of them are not able to perform appropriately in small error limit. In the present paper, delivering a voter for safety system, Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The above mentioned model is trained through Hybrid learning algorithm that is effective and using basic Fuzzy inference system, subtractive clustering and fuzzy C-means method. Results show that delivered voter produced more safety outputs especially for small error amplitude.Keywords: ANFIS, Adaptive Neuro-Fuzzy Inference System, Voting Algorithm, Fault Tolerant Systems, Safety-Critical Systems. تفاصيل المقالة

Liver cancer is one of the most common cancers that causes many deaths every year. In recent years, the risk of men and women getting liver cancer has increased by 40% and 23%, respectively. In order to identify a tumor in the liver, segmentation is performed on CT images. The use of data fusion methods in data mining techniques is one of the most practical methods to improve accuracy, which also has many applications in the field of medical image processing. Correct and efficient diagnosis of liver abnormalities leads to a significant reduction in human error and a more accurate diagnosis by physicians. This requires the use of methods based on automatic and semi-automatic detection. Combining clustering methods and considering cluster uncertainty is an appropriate tool in solving clustering problems in medical image processing, especially cancer diagnosis. The proposed method, in addition to having high accuracy, has a high convergence speed. تفاصيل المقالة