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Background: From the epidemiological point of view, certain factors involved in the appearance of varicose veins are preponderant such as multiple pregnancies, age, and also certain races. Physiologically, venous valve dysfunction can also be a factor. Here, radiologists intervene to determine venous insufficiency. Doppler ultrasound (US) and tomography are often the most used in this detection. Certain other factors contribute to their recidivism. Aims: Some factors that occur in the recurrence of varicose veins are extrinsic such as age, sex, or genetic factor. On the other hand, certain factors are linked to an inadequate surgical procedure that can be partly explained by a poor radiological or methodological reading. The aim of this study is to prevent recurring complications that may occur the analysis of the factors of these is necessary. Materials and Methods: In our study, 62 patients were operated in our general surgery department during the period from January 2016 to September 2017. The pre-operative clinical examination included, among others, the radiological examination using a Doppler US. Patients who have had a recurrence are classified from the identification of the possible causes. Since the causes are complex and vary from one person to another, this makes them very difficult to analyze by conventional methods. We proposed an intelligent system based on artificial neural networks. Results: Once the system is established, this will identify the most important factor in the recurrence of varicose veins. By randomly changing the parameters at the input one by one and we record the effect that each produces on the recurrence rate at the output. Conclusion: The proposed system with its very strong inters connectivity, and the support of all possible combinations with the weight of each factor makes it possible to extract the predominant cause. With its learning from the real values recorded, and the optimal function created between the two input-output spaces, it becomes very easy to identify the main cause that leads to recidivism.

Aim: Nowadays, functional MRI is widely used in the study of brain functions. If it has the advantage of being non-invasive and allows delimiting the zones that are activated at each stimulus in 3D, it presents several deficiencies. On the psychotic studies, the zones which activate induce the analyst in error in view of the complexity and the differences between individuals and their states of anxiety. Even minimal movements of the head influence the result; the response of the vascular system signal is delayed after the stimulus...etc. A heavy numerical processing in particular in statistical analyzes is necessary to refine the images. Despite this, difficulties persist. Method:In this study, a fuzzy logic system in this analysis is proposed. Viewing the complexity of the system, the variables that define the constructed image are considered as inaccurate variables and therefore fuzzy variable. The motor and emotional stimulus, the parasites movements, the anxiety state are considerate as inputs system. The quality of image constructed is the output system. The data base constructed permits adjusting input variables for optimal image. Conclusion:The proposed system allows defining the optimal interaction of the different factors for an optimal image. Considering the variables of input and output as fuzzy variables thus imprecise, this makes it possible to overcome the deficiencies of the system

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The radiological aspect of brain tumors is most often suggestive of the diagnosis. However, the radiological presentation can be very variable and sometimes misleading. Moreover, other pathologies, tumor or otherwise, may have a similar radiological presentation and which are essentially abscesses or inflammatory lesions. The problem is posed in the interpretation of the magnetic resonance imaging (MRI). In this context, the nature of tissues, which have a non-homogeneous structure, without apparent regularity, whose scheduling varies according to whether it is healthy or not. Particularly statistical methods are used due to the random nature of the tissues. This allows extracting the characteristic parameters that will make it possible to diagnose the nature and gravity of the tumor. These models are structural models (adapted repetition of macrostructures), models by probabilistic laws (for the analysis of microstructures) and since it is difficult to delimit the boundary between the zones of regularity and non-regularity, sometimes we call upon to analysis techniques by treating the data as a multi-fractal signal (turbulence signal analysis techniques). In view of this complexity, an artificial intelligence technique is proposed in this analysis of the texture resulting from MRI imaging. A fuzzy inference system is established. As fuzzy logic deals with uncertainty, its application in this area is adequate. The proposed system consists of four input variables (Texture nature, age, gender, genetic factor) and an output variable that expresses the degree of tumor confirmation. A rule base is established from the recorded values encompassing all possible combinations. The established algorithm permits to introduce randomly values on input factors of the system to read predict the degree of cerebral tumor confirmation.