This research paper tries to show the significance of the narrative structure in the television advertisement and its connotations. The researchers chose the annual advertisement of Zain Mobile Telecommunication Company for the year 2020, which shed light on the global Corona pandemic crisis. The idea of the advertisement won wide approval as it focused on the suffering that everyone is witnessing like medical and security personnel in particular, and family relationships consequences.
In addition to the positive global interaction with the message presented by the Company in these exceptional circumstances. The advertisement, which lasted for 2.35 minutes, exceeded 13 million views in a short period of time. This prompted us to choos

During the last two decades at least, increased interest in Social attraction  variable by scientists  in psychology and sociology, to multi-dimensional and its association with psychological  and emotional variables , and what are of importance in the process of human communication , leadership, and social relations.

Social psychologists interested   in social attraction also  so its relation with  social influence , political and occupational  .
"Social attraction defined as "individual ability  functioned his skills in at

The study aims to measure life management (in its various approaches) among   university academics and to identify the differences among life management strategies (in its various approaches) in regard of gender (male, female), field of education (scientific, humanitarian) and title (assistant lecturer, lecturer, assistant professor, and professor). The study sample consisted of (246) male and female academics from four colleges (Arts, Science, Science education, and Humanitarian Science Education). To achieve the goals of the study, the researchers used the Freund & Baltes (2002) life-management strategies scale after translating it and determining its parameters of reliability and validity. The statistical analysis of the

Deep Learning Techniques For Skull Stripping of Brain MR Images

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere