The volume and amount of data in cancerology is continuously increasing, yet the vast majority of this data is not being used to uncover useful and hidden insights. As a result, one of the key goals of physicians for therapeutic decision-making during multidisciplinary consultation meetings is to combine prediction tools based on data and best practices (MCM). The current study looked into using CRISP-DM machine learning algorithms to predict metastatic recurrence in patients with early-stage (non-metastatic) breast cancer so that treatmentappropriate medicine may be given to lower the likelihood of metastatic relapse. From 2014 to 2021, data from patients with localized breast cancer were collected at the Regional Oncology Center in Meknes, Morocco. There were 449 records in the dataset, 13 predictor variables and one outcome variable. To create predictive models, we used machine learning techniques such as Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbors (KNN) and Logistic Regression (LR). The main objective of this article is to compare the performance of these four algorithms on our data in terms of sensitivity, specificity and precision. According to our results, the accuracies of SVM, kNN, LR and NB are 0.906, 0.861, 0.806 and 0.517 respectively. With the fewest errors and maximum accuracy, the SVM classification model predicts metastatic breast cancer relapse. The unbiased prediction accuracy of each model is assessed using a 10-fold cross-validation method.

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The dictionary resources are very important for Natural Language Processing (NLP). Generating high quality dictionary resources is a crucial step for the success and effectiveness of NLP application. Linguistic information about lexical database is complex, large size and various (ie, phonological, morphological, syntactic, semantic and pragmatic

Abstract-The preservation of the water quality in the distribution network requires maintaining permanently minimum residual chlorine at any point of the network. This is possible only if we plan chlore's injections in various points of the network for intermediate rechlorination, or when increasing the initial level of chlorine in the tank outlet. In the latter case, there is a risk of disruption of the taste and smell of water for consumers near the tanks. Therefore, to avoid an excessive increase in the chlorine concentration in the tanks and to avoid affecting the taste of the distributed water, intermediate rechlorination stations should be implemented. These stations will proceed with the chlorine regulation.Given the high cost of the implementation of such stations, the optimization of the number and the choice of location of these stations are needed. This paper is focused on the implementation of an algorithm for such optimization. We used dynamic programming in this algorithm. Performance tests of our decision support system were done on real sites of the Wilaya Rabat-Sale (network of Morocco's capital).

The need for Automatic Natural Language Processing ( NLP ) in large dictionary resources continues to grow. The management of these linguistic knowledge should be taken into account because it is a fundamental element in the success and effectiveness of applications of NLP. Also, there is increasing interest for the development of reusable and independent lexical databases of a particular language application. Knowledge about a lexical database are complex, large sizes and various (ie, phonological, morphological, syntactic, semantic and pragmatic), which has negatively influenced many national and international projects for the development of lexical databases (monolingual or multilingual ). Among such lexical database entries, we find conjugated verbs. To this end, we present in this paper, our open source conjugator application of arabic verbs that we have developed in Java under the Android platform. This conjugator developed within the MISC laboratory is structured into several modules whose core is a morphological generator Root-Pattern.