In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

This research discusses application Artificial Neural Network (ANN) and Geographical InformationSystem (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameterswere used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium,Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity.These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala provincewhile it is poor to very polluted at the south of Baghdad City. The selected parameters wer

The CdSe pure films and doping with Cu (0.5, 1.5, 2.5, 4.0wt%) of thickness 0.9μm have been prepared by thermal evaporation technique on glass substrate. Annealing for all the prepared films have been achieved at 523K in vacuum to get good properties of the films. The effect of Cu concentration on some of the electrical properties such as D.C conductivity and Hall effect has been studied.
It has been found that the increase in Cu concentration caused increase in d.c conductivity for pure CdSe 3.75×10-4(Ω.cm)-1 at room temperatures to maximum value of 0.769(Ω.cm)-1 for 4wt%Cu.All films have shown two activation energies, where these value decreases with increasing doping ratio. The maximum value of activation energy was (0.319)eV f

In this work, Titanium oxide thin films doped with different concentration of CuO (0,5,10, 15,20) %wt were prepared by pulse laser deposition(PLD) technique on glass substrates at room temperature with constant deposition parameter such as : pulse (Nd:YAG), laser with λ=1064 nm, constant energy 800 mJ , repetition rate 6 Hz and No. of pulse (500). The structure , optical and electrical properties were studied . The results of X-ray diffraction( XRD) confirmed that the film grown by this technique have good crystalline tetragonal mixed anatase and rutile phase structure, The preferred orientation was along (110) direction for Rutile phase. The optical properties of the films were studied by UV-VIS spectrum in the range of (360-1100)

Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill thi