Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support and often late and error-prone diagnosis of the disease. For instance, largely used tools like Rapid Diagnosis Test are not fully reliable. This study proposes an extensive study of the efficiency of the most popular machine learning models for the task of Malaria occurrence prediction. We have considered patients from Senegal and have evaluated the overall accuracy of each considered algorithm based on sign and symptom information. Our main result is that machine learning algorithms are promising, in particular Naive Bayesian presents a recall very close to that of a rapid diagnostic test while improving highly its precision by 9%.
In this paper, we propose a numerical method to study phononic and photonic crystal problems. The proposed method is based on topological optimization tools. In fact, after modeling crystal photonics and phononic problems, we use topological optimization tools to build a numerical method for getting optimal design. Here the optimal design is the one in which all frequencies near the reference frequency corresponding to the reference wave length a can pass.
We focus in this paper on the theoretical and numerical aspect os image processing. We consider a non linear boundary value problem the p-Laplacian from which we will derive the asymptotic expansion of the Mumford-Shah functional. We give a theoretical expression of the topological gradient as well as a numerical confirmation of the result in the restoration and segmentation of images.
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