The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

يهدف البحث إلى تشخيص أوجه القصور ونقاط الضعف في تطبيق أبعاد جودة الحياة الوظيفية ومدى تأثيرها على اتجاهات وسلوكيات الموظفين، وبالتالي علاقتها بتعزيز مقدراتهم الجوهرية. وتنبع القيمة العلمية للبحث من إبراز أهمية أبعاد جودة حياة العمل في تحسين الكفاءة الإنتاجية للعاملين في القطاع العام ورفع مستوى الأداء التنظيمي. ولأن جودة الحياة العملية تلعب دوراً مهماً في تعزيز المقدرات الأساسية للموظفين في القطاع الع

The research aims to diagnose the shortcomings and weaknesses in applying the dimensions of the quality of work life and the extent of their impact on employees’ attitudes and behaviors, and thus their relationship to enhancing their core competencies. The scientific value of the research stems from highlighting the importance of the dimensions of the quality of professional life in improving the productive efficiency of workers in the public sector and raising the level of organizational performance. Because the quality of working life plays an important role in enhancing the core competencies of employees in the public sector, it can also be an incentive or a disincentive for any employee by adapting to the economic and social condition

The mass collision energy loss (dE/dX), the mass radiative energy loss (Srad/) and the total mass stopping power of electrons in the energy range of 0.01 MeV up to 1000 MeV has been calculated for Lung, Urea and Skin. The results of the present work for the mass collision stopping power of electrons in Lung, Urea and Skin are in excellent agreement with the standard results given by ESTAR program, where the maximum percentage error between the present calculated values and that of ESTAR program in Lung tissue, Urea and Skin tissue is 0.27%, 0.3% and 0.8% respectively. The mass radiative energy loss of electrons in the same energy range is also calculated using a modified equation, and the results are found to be in very good agreem