Issues and Innovation for Setting and Infrastructure Management in the Islamic University of Lebanon in the Time of Pandemic

The Islamic University of Lebanon (IUL) is committed to providing faith and knowledge as a source of inspiration for the Lebanese nation, citizen, state and society as a whole. IUL has paid special attention to the environment and to the green metrics when establishing the new campus in Wardanieh, the rules related to green buildings and the preservation of the environment were taken into account. The COVID-19 pandemic has created the largest disruption of education systems in human history, affecting nearly 1.6 billion learners in more than 200 countries. In this paper we will describe how the setting and infrastructure of Wardanieh campus has helped in managing the Covid-19 crisis at the university through different levels: the physical distancing, the effect of the large campus buildings area according to the campus population, moreover the large forest spaces, the natural ventilation of buildings, the sanitization and sterilization procedures with setting guidelines for Covid-19 such as the obligation of wearing masks. Furthermore, we will describe the influence of ICT technologies into education in many different ways for distance learning, and how the university has evolved its ICT infrastructure to support the increase in demand on the internet capacity and on university servers.Keyword: IUL university, Green, SDGs, Covid-19 prevention, education, settings and infrastructur

Application des réseaux de neurones à la classification automatisée des grades placentaires

Le placenta est un organe provisoire joignant la mère et le fœtus qui transfère l oxygène et des nutriments de la mère au fœtus et permet l évacuation de l anhydride carbonique et des produits du métabolisme du fœtus. Le but de notre travail était d étudier la fonction de transfert des tissus placentaires selon son développement en se basant sur les images ultrasonores. Nous avons développé au cours de ce travail une nouvelle approche de la classification du développement placentaire en ultrasons par des techniques de traitement d images avancées basée sur une représentation par réseau neuronal. Le modèle réalisé par la transformée en ondelettes basé sur le réseau neuronal MLP représente donc un outil efficace et rapide répondant à nos critères et bien adapté à nos applications concernant l étude de la maturation placentaire. L application du modèle réalisée en cas de traitement d images placentaires ouvre des portes intéressantes en terme de classification des grades placentaires afin d identifier des stades de maturation autorisant la définition d une maturation normale et de classes à risque.The placenta is a temporary organ joins the mother and the fœtus, which transfers oxygen from the mother to the foetus, allows the evacuation of the carbon dioxide and the products of foetus metabolism. The goal of our work is to study the transfer function of placental development using ultrasound images. A new approach is developed during this work to classify the placental development by image processing techniques based on supervised neural network. The realized model by the wavelet transform based on MLP neural network, represents an effective tool answering our criteria and adapted to our applications concerning the study of placental maturation. The realized model application in the event of placental image processing opens interesting doors in terms of placental grades classification in order to identify the stages of maturation, authorizing the definition of a normal maturation and an abnormal maturation.TOURS-BU Médecine (372612103) / SudocSudocFranceF

Multilevel graphical modeling for system of systems: Bondgraph model for a wireless communication link with redundancy

International audienc

Bondgraph model for system of systems wireless communication link

International audienc

HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHM

Abstract—Information decision support systems are becomingmore in use as we are living in the era of digital data andrise of artificial intelligence. Heart disease as one of the mostknown and dangerous is getting very important attention, thisattention is translated into digital and prediction system thatdetects the presence of disease according to the available dataand information. Such systems faced a lot of problems since thefirst rise, but now with the deveolopment of machine learnigfield we are using them in developing new models to detect thepresence of this disease, in addition to algorithms data is veryimportant which also form the heart of the predicton systems,as we know prediction algorithms take decisions and thesedecisions must be based on facts, and these facts are extractedfrom data, as a result data is the starting point of every system.In this paper we propose a Heart Disease Prediction Systemusing Machine Learning Algorithms, in terms of data we usedCleveland dataset, this dataset is normalized then divided intothree scnearios in terms of traning and testing respectively,80%-20%, 50%-50%, 30%-70%. In each case of dataset ifit is normalized or not we will have these three scenarios.We used three machine learning algorithms for every scenarioof the mentioned before which are SVM, SMO and MLP, inthese algorithms we’ve used two different kernels to test theresults upon that. These two types of simulation are added tothe collection of scenarios mentioned above to become as thefollowing we have at the main level two types normalized andunnormalized dataset, then for each one we have three typesaccording to the amount of training and testing dataset, thenfor each of these scenarios we have two scenarios according tothe type of kernel to become 30 scenarios in total, our proposedsystem have shown a dominance in terms of accuracy over theother previous works.</jats:p