The current research was conducted to report the synthesis of alumina powder from Iraqi kaolin. The kaolin was transformed to metakaolin by calcinations at temperature 800 °C for three hours. Then the calcined kaolin was treated with (1.5 M) from H2SO4 for 6 hours to form Al2(SO4)3.12H2O solution. The precipitate was dried at 80oC for 10 hours and calcinations at different temperatures for two hours. The samples which result was characterized by X–Ray diffraction (XRD) and X–Ray fluorescence (XRF). The results indicate to the crystalline hydrate aluminum sulfate for the sample that be as – synthesis and when calcinations at 600 oC transformed into aluminum sulfate phase. The phases of alumina which we obtain consisted of a gamma a

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The optical detectors which had been used in medical applications, and especially in radioactive treatments, need to be modified studied for the effects of radiations on them. This study included preparation of the MnS thin films in a way that vacuum thermal evaporation process at room temperature 27°C with thickness (400+-10nm) nm and a sedimentation rate of 0.39nm/sec on glass floors. The thin films prepared as a detector and had to be treated with neutron irradiation to examine the results gained from this process. The results decay X-ray (XRD) showed that all the prepared thin films have a multi-crystalline structure with the dominance of the direction (111), the two samples were irradiated with a neutron irradiation source (241Am-9Be)

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod