The magnetic properties of a pure Nickel metal and Nickel-Zinc-Manganese ferrites having the chemical formula Ni_0.1(Zn_0.4Mn_0.6)_0.9Fe₂O₄ were studied. The phase formation and crystal structure was studied by using x-ray diffraction which confirmed the formation of pure single spinel cubic phase with space group (Fd3m) in the ferrite. The samples microstructure was studied with scanning electron microstructure and EDX. The magnetic properties of the ferrite and nickel metal were characterized by using a laboratory setup with a magnetic field in the range from 0-500 G. The ferrite showed perfect soft spinel phase behavior while the nickel sample showed higher magnetic loss an

Cadmium sulfide (CdS) thin films with n-type semiconductor characteristics were prepared by flash evaporating method on glass substrates. Some films were annealed at 250 ^oC for 1hr in air. The thicknesses of the films was estimated to be 0.5µ by the spectrometer measurement. Structural, morphological, electrical, optical and photoconductivity properties of CdS films have been investigated by X-ray diffraction, AFM, the Hall effect, optical transmittance spectra and photoconductivity analysis, respectively. X-ray diffraction (XRD) pattern shows that CdS films are in the stable hexagonal crystalline structure. Using Debye Scherrerś formula, the average grain size for the samples was found to be 26 nm. The transmittance of the

Tetragonal compound CuAl0.4Ti0.6Se2 semiconductor has been prepared by
melting the elementary elements of high purity in evacuated quartz tube under low
pressure 10-2 mbar and temperature 1100 oC about 24 hr. Single crystal has been
growth from this compound using slowly cooled average between (1-2) C/hr , also
thin films have been prepared using thermal evaporation technique and vacuum 10-6
mbar at room temperature .The structural properties have been studied for the powder
of compound of CuAl0.4Ti0.6Se2u using X-ray diffraction (XRD) . The structure of the
compound showed chalcopyrite structure with unite cell of right tetragonal and
dimensions of a=11.1776 Ao ,c=5.5888 Ao .The structure of thin films showed

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

The State company for vegetable oils industry one of the most dynamic

companies in the Iraqi economy and is one of the companies manufacturing(food) that takes astrategic dimension and production within the concept of food security, this as well as to reduce dependence on imports and operation of national manpower.This study aims to describe the performance of the State company for vegetable oils industry for the period (2003-2007) which was characterized by economic and security instability of the country and give an accurate picture of their efficiency and their capacity to produce during this Period.    

     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