In recent decades, the world has faced some rapid technical developments, affected on many fields of life, which had the best chance in the information and communication technology, such that it created an entirely new situation, summed up the life and development of business enterprises. This is while these developments have changed a lot the reputation of management institutions and civil society organizations in return. In addition, they have linked the citizen and those institutions in their different specialties within a distinct electronic format called e-management in the contemporary literature.

    This study attempted to highlight the importance of investment in building e-management by linking citiz

The grammar structure transforms from its fundamental field to another field that takes new dimensions, and is used in another way in speech that differs from the first one, because the formula basis of the name(Faael) in speech is a (distorted characteristic), and the distorted characteristic indicates the stability and the necessity of the characterized. as this characteristic has changed from its use as a distorted characteristic which was established by the elders. to a use that differs from the first and cannot be overlooked to achieve a new purpose, to be used as plural

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

Deep Learning Techniques For Skull Stripping of Brain MR Images