مطالب

Contrairement à l’idée répandue qui réduit le silence à une absence ou à un simple vide, il importe d’envisager celui-ci comme unélément constitutif du langage et du discours. Dans le Masnavi de Jalâl od-Din Rûmî, figure majeure du soufisme et de la littérature persane, le silence apparaît non pas comme une négation de la parole, mais comme une composante essentielle de la production du sens. L’objectif de cette recherche est d’analyser les fonctions sémiotiques du silence dans le Masnavi et de montrer son rôle fondamental dans la dynamique discursive et spirituelle de l’œuvre. La présente étude relève de la recherche théorique et descriptive, fondée sur une analyse textuelle et sémiotique. Elle mobilise la grille conceptuelle de la sémiotique peircienne, articulée avec la sémiotique discursive, pour examiner le fonctionnement du silence dans le discours mystique de Rûmî. L’approche repose principalement sur l’étude des passages du Masnavi où le silence est explicitement évoqué ou se manifeste implicitement à travers la rétention de l’énonciation, les interruptions narratives ou les injonctions au mutisme. Les résultats montrent que le silence, loin d’être neutre, constitue un signe pluriel et dynamique. Il assume différentes valeurs sémiotiques (icône, indice, symbole) et s’articule aux autres signes matériels et discursifs pour former une chaîne interprétative continue. Sur le plan plus spécifique, il apparaît comme une présence signifiante, à la fois linguistique et spirituelle.

Moral identity refers to those characteristics and attributes that play an important role in the formation of an individual’s identity. Indeed, the role performed by moral attributes in the evolution of a person’s identity is such that quite a few of today’s scholars maintain the oneness of the “self/psyche” and “moral character”. Adopting this approach, researchers have set out to produce a wide range of research works in the field of moral philosophy. The present study is an attempt to measure the effect of three components of ‘exercise’, ‘nutrition’, and ‘sleep’ on shaping moral character from Avicenna’s viewpoint. The findings of the present research illuminate the fact that the latter three elements have a direct influence on an individual’s temperament, whereby suitable grounds are created in which moral character develops. Avicenna holds that this statement may not challenge man’s will and authority. The reason is that every individual–by identifying his own physical coordinates and applying the most apt instructions–can approximate his temperament to moderation. As a result, the necessary ground for developing the most desirable moral character is created.

This study aims to apply automatic machine-learning approaches using genetic algorithms to enhance heart disease prediction. Heart disease has remained the major cause of mortality in the world, necessitating an effective and timely diagnosis. Most current diagnostic and assessment processes are lengthy and expensive, relying heavily on clinical expert knowledge. To help address these issues, machine learning approaches, which derive their utility from examining substantial datasets for the recognition of patterns, have emerged as a potential solution, providing solutions beyond those achievable by human recognition alone. Genetic algorithms are also suited to addressing these issues as they mimic natural evolution to perfect high-caliber machine-learning models, feature selection, and parameter selection in machine-learning applications. This study examines the utilization of genetic algorithms working alongside AutoML frameworks to improve accuracy in heart disease predictions. Reducing to the best combination of attributes and the optimum parameters for each attribute is a time-consuming task, so automating this aspect of the process allows for more accurate and prompt predictions, consequently reducing the manual work. The AutoML approach followed in this research is TPOT, which uses genetic algorithms to ascertain optimally designed machine-learning pipelines. The application of AutoML, together with genetic algorithms, is the most prominent finding that yields a significant improvement in the quality of the predictions for heart disease compared to the traditional assessment approaches, with an accuracy of 93.8%. This approach will enhance diagnostic accuracy and enable early diagnosis, thereby reducing the likelihood of misdiagnoses or ineffective treatments and ultimately lowering associated costs.