Results for 'Souleymane Bachir Diagne'

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  1. Philippe Capelle-Dumont et Yannick Courtel (dirs), Religion et liberté. [REVIEW]Marguerite El Asmar Bou Aoun - 2017 - Proche-Orient Chrétien 3 (66):425-430.
    The present article is published in Proche-Orient Chrétien, N.66, VOL.3-4, JAN. 2017, USJ: Beirut, pp. 425-430. It is a philosophical review of Philippe Capelle-Dumont and Yannick Courtel book “Religion et Liberté” that fetches the records of the First International Symposium of the Francophone Society of Philosophy of Religion about the two concepts Religion and Freedom. On one hand, religion has always been considered as a pole of practices and references contrary to freedom declining a dependence on a "binding doctrine"; on (...)
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  2. Cinco dificultades para construir la historia de la filosofía africana.Antonio de Diego Gonzalez - 2013 - Contrastes: Revista Internacional de Filosofía 18:211-222.
    RESUMENDesde la teoría postcolonial se han cuestionado los modelos de historia de las ideas impuestos por el africanismo y el orientalismo. Diferentes teóricos africanos –Bachir Diagne, Mundimbe, Wiredu o Kete Asante– han formulado diversas soluciones para superar las dificultades. Este trabajo explora las principales dificultades y las propuestas para elaborar una historia de la Filosofía africana. -/- The postcolonial theory was questioning the patterns of History of Ideas imposed by Orientalism and Africanism. Different African theorists –Bachir (...), Mundimbe, Kete Asante or Wiredu– developed various solutions to overcome the dificulties. This paper explores the principal challenges and proposals so as to build a History of African philosophy. (shrink)
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  3. Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis.Anas Bachir Abu Sultan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):1-9.
    Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...)
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