The necessity of the use of several mobile services increases every day. This requires the consumption of energy rates. Hence, minimizing the energy and interferences of mobile users are major concerns of Heterogeneous Networks (HetNet). This paper aims to improve the energy of mobile users by minimizing a weighted function. To consider significant weights values, we have exploited the multicriteria AHP method. The cost function represents the cost of users toward available BS. It includes the essential criteria influencing energy consumption. Moreover, we introduce an optimal user association based on their minimal cost values. Simulation results prove that the optimal method has saved more energy and reduced network interferences.

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Covid19 is a horrible disease, which upset our life everywhere. The main complexity of this disease lies in its rapid evolution and through people’s contact and gaps in our understanding. Moreover, it represents critical cases when the immune system has not presented any symptoms. Hence, the design of an effective classifier is necessary. This paper aims to hybrid the multi-criteria Analytic Hierarchy Pprocess (AHP) tool and the process of the convolutional neural network (CNN), for making the classification of a category of patients. Our novel method is divided into two main phases: the first one focuses on the generation of the priorities of the essential criteria using the AHP model, while the second phase aims to classify the patients using the neural network classifier. In the present study, we considered three important criteria: fever, patient, localiztion, and the age of the patient. From the obtained results, the proposed model has proved its efficiency even if we consider different cases.