There is no doubt that teachers are the leaders of positive changing in community where they directed the students and build their brains. In our current generation that characterized by accelerated technological development that communication changes, economic and politics, needs from the teacher an active leadership skills that match with the soul of our generation and contribute in confrontation the current challenges and the future challenges in the form that lead to create a conscious generation where they will be a basic brick for the future community where the listeners looking forward the education where they support the continuity communication of develop process, economy, scientifically and in all life fields. In our study we take

Convolvulus arvensis is a species of bindweed that is rhizomatous and is in the morning glory family (Convolvulaceae) native to Europe and Asia. The plant is naturally grown in Iraq. The plant was reported to be used in traditional medicine from as early as 1730s.

The Aerial parts of Convolvulus arvensis were macerated in 80% ethanol for 6 days. The concentrated extract was partitioned with n-hexane, chloroform, ethyl acetate- and n-butanol successively. The n-hexane and ethyl acetate, fractions were examined for the presence of phytochemicals by thin l

TRIPS agreement was The first to apply protection by patents. However, this type of protection, which grants exclusive and monopoly rights to patent owners, came at the expense of developing countries which are considered rich in biodiversity and also at the expense of traditional and poor knowledge of modern technologies. The release of new plant varieties has led to the emergence of biopiracy and looting of the rights of developing countries without a license

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Melanoma, a highly malignant form of skin cancer, affects individuals of all genders and is associated with high mortality rates, especially in advanced stages. The use of tele-dermatology has emerged as a proficient diagnostic approach for skin lesions and is particularly beneficial in rural areas with limited access to dermatologists. However, accurately, and efficiently segmenting melanoma remains a challenging task due to the significant diversity observed in the morphology, pigmentation, and dimensions of cutaneous nevi. To address this challenge, we propose a novel approach called DenseUNet-169 with a dilated convolution encoder-decoder for automatic segmentation of RGB dermascopic images. By incorporating dilated convolution,

The use of deep learning.

A load-shedding controller suitable for small to medium size loads is designed and implemented based on preprogrammed priorities and power consumption for individual loads. The main controller decides if a particular load can be switched ON or not according to the amount of available power generation, load consumption and loads priorities. When themaximum allowed power consumption is reached and the user want to deliver power to additional load, the controller will decide if this particular load should be denied receiving power if its priority is low. Otherwise, it can be granted to receive power if its priority is high and in this case lower priority loads are automatically switched OFF in order not to overload the power generation. The

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att