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Abstract. The automatic diagnosis of breast cancer is an important medical problem. This paper hybridizes metaphors from cells membranes and intercommunication between compartments with clonal selection principle together with fuzzy logic to produce a fuzzy rule system in order to be used in diagnosis. The fuzzy-membrane-immune algorithm suggested were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem. The developed solution scheme is compared with five previous works based on neural networks and genetic algorithms. The algorithm surpasses all of them. There are two motivations for using fuzzy rules with the membrane-immune algorithm in the underline problem. The first is attaining high classification performance. The second is the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis, beside the simplicity of the diagnosis system, which means that the system is human interpretable.

The automatic diagnosis of breast cancer is an important medical problem. This paper hybridizes metaphors from cells membranes and intercommunication between compartments with clonal selection principle together with fuzzy logic to produce a fuzzy rule system in order to be used in diagnosis. The fuzzy-membrane-immune algorithm suggested were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem. The developed solution scheme is compared with five previous works based on neural networks and genetic algorithms. The algorithm surpasses all of them. There are two motivations for using fuzzy rules with the membrane-immune algorithm in the underline problem. The first is attaining high classification performance. The second is the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis, beside the simplicity of the diagnosis system, which means that the system is human interpretable.

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Crops plantation dates is considered one of the most important factors in the success of crops plantation process. Obviously, each crop needs suitable values of climate variables to be planted in a certain soil. In other words, a crop plantation in different soils needs different suitable values of the climate variables. Consequently, each soil has a range of suitable values of climate variables to ensure a productive and profitable plantation process for a specific crop. Unfortunately, the ongoing changes in climate variables affect mainly the suitability of planting most crops in the same soil at the same traditional dates. So, such changes in climate variables like temperature and humidity make the traditional plantation dates of some crops in a given place became not suitable. Instead, it becomes more suitable to plant at other different dates that were not suitable before. This paper presents a fuzzy set based approach for adjust crops plantation dates reflecting climate changes. The proposed approach has three main phases, one for defining a crop suitability membership functions, the second for calculating the suitability degrees of year days, and the last one for selection and optimization of most perfect plantation dates. It has been applied on the potatoes crop with temperature and humidity as climate variables in Alexandria governorate in Egypt. In consequent, the final results are presented to a set of agriculture researchers in the Agriculture Research Center (ARC) in Egypt and they admired the results.

This paper is focused on the implementation of fuzzy logic controllers and a combination between fuzzy logic control and proportional control to control the speed of a combined cycle electric power plant (CCPP). The system is simulated using Matlab/ Simulink and the fuzzy controller is implemented as a box in the simulation. Different types of the fuzzy controller have been tried in order to obtain the required speed response which achieves certain transient and steady state behavior.