In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

In this paper, previous studies about Fuzzy regression had been presented. The fuzzy regression is a generalization of the traditional regression model that formulates a fuzzy environment's relationship to independent and dependent variables. All this can be introduced by non-parametric model, as well as a semi-parametric model. Moreover, results obtained from the previous studies and their conclusions were put forward in this context. So, we suggest a novel method of estimation via new weights instead of the old weights and introduce

Paper Type: Review article.

another suggestion based on artificial neural networks.

Statistical methods and statistical decisions making were used to arrange and analyze the primary data to get norms which are used with Geographic Information Systems (GIS) and spatial analysis programs to identify the animals production and poultry units in strategic nutrition channels, also the priorities of food insecurity through the local production and import when there is no capacity for production. The poultry production is one of the most important commodities that satisfy human body protein requirements, also the most important criteria to measure the development and prosperity of nations. The poultry fields of Babylon Governorate are located in Abi Ghareg and Al_Kifil centers according to many criteria or factors such as the popu

Reliability is an essential measure and important component of all power system planning and operation procedures. It is one of the key design factors when designing complex, critical and expensive systems. This paper presents a fuzzy logic approach for reliability improvement planning purposes. Evaluating the reliability of the complex and large planned Iraqi super grid ;as Al- Khairat generating station with its tie set is intended to be compact to that grid; and determination of the given reliability improvement project are the major goals of the paper. Results show that the Iraqi super grid reliability is improved by 9.64%. In the proposed technique, fuzzy set theory is used to include imprecise indices of different components in normal

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic

In this research, the region in the south-west of Iraq is classified using a fuzzy inference system to estimate its desertification degree. Three land cover indices are used which are the Normalized Difference Vegetation Index, Normalized Multi-Band Drought Index and the top of atmosphere surface temperature to build a fuzzy decision about the desertification degree using eight decision roles. The study covers a temporal period of 38 years, where about every 10 years a sample is elected to verify the desertification status of the region, starting from 1990 to 2018. The results show that the desertification status varied every 10 years, wherein 2000 encountered the highest desertification in the south-west of Iraq.

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t