In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that

   The removal of Ibuprofen antibiotics (IBU) by photo-degradation UV/H₂O₂/Fe⁺² system was investigated in a batch reactor under different initial concentrations of H₂O₂ (100-500) mg/L, Fe⁺² (10-40) mg/L, pH (3-9) and initial concentrations of IBU (10-80) mg/L, and their relationship with the degradation efficiency were studied. The result demonstrated that the maximum elimination of IBU was 85.54% achieved at 300 mg/L of H₂O₂, 30 mg/L of Fe⁺², pH=3, and irradiation time of 150 min, for 10 mg/L of IBU. The results have shown that the oxidation reagent H₂O₂ plays a very important role in IBU degradation.

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.

Increased diseases and obesity currently due to increased production and excessive consumption of foods manufactured from non-food sweeteners without attention to the risk of consuming those additional high calories due to consuming these refreshing products such as juices and other various drinks, especially in the summer season by most segments of Iraqi society, especially workers, children and school students the aim of this study. Therefore, the study designed to replace sucrose with 0.03, 0.04 and 0.05% of each of the white stevia crystals and milled dry stevia leaves in the laboratory manufacture of juices and its effect on the general and sensory characteristics and the extent of their acceptability among the specialized r

A microbial study conducted for a number of flour samples (30 samples) Uses in the bakery ovens in various areas of the city of Baghdad, by used the conventional methods used in laboratories in microbial tests and compared with the modern techniqueby usedof BacTrac Device 3400 equipped from SY-LAB Impedance analysersAustrian company.The results of two ways showed (The conventional way and BacTrac Device test)that the total counts of aerobic bacteria, coliform bacteria, StaphylococcusSpp. bacteria, Bacillus cereus bacteria and yeasts and molds,Most of them were within the permissible borders in the Iraqi standard for grain and its products With free samples from SalmonellaSpp. bacteria, and that the screening by BacTrac device are shorten