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.

Depletion of fossil fuel is one of the main reasons why the bioethanol has become popular. It is a renewable energy source. In order to meet the great demand of bioethanol, it is best that the bioethanol production is from cheap raw materials. Since the golden shower fruit is not being utilized and is considered as waste material, hence, this study was conducted to make use of the large volume of the residue as feedstock to test its potential for bioethanol extraction.The main goal of this study is to obtain the most volume of bioethanol from the golden shower fruit liquid residue by the factors, days of fermentation (3, 5, and 7 days) and sugar concentration (15, 20 and 25 brix) of the liquid residue. Also, part of the study is to compu

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Oily wastewater is one of the most challenging streams to deal with especially if the oil exists in emulsified form. In this study, electrospinning method was used to prepare nanofiberous polyvinylidene fluoride (PVDF) membranes and study their performance in oil removal. Graphene particles were embedded in the electrospun PVDF membrane to enhance the efficiency of the membranes. The prepared membranes were characterized using a scanning electron microscopy (SEM) to verify the graphene stabilization on the surface of the membrane homogeneously; while FTIR was used to detect the functional groups on the membrane surface. The membrane wettability was assessed by measuring the contact angle. The PVDF and PVDF / Graphene membranes efficiency

The performa of evaluation process is a process that should be carried out by all industrial management in order to stand on aspects of development or underdevelopment of the various departments and activities in its industrial project for the purpose of identifying obstacles and find out the causes and then avoid them quickly. And intended to rectify the performance evaluation of the  activities of  industrial project  or economic union by measuring the results achieved within a specific operational process and compare it to what is already targeted, and often the time for comparison of one year.

The process of performance evaluation depends upon several criteria and indicators within the

Social protection meets different aspects of the needs of vulnerable groups, such as the economic, health, education, and family relations and ties in the Iraqi society. This is because vulnerable groups have suffered from social and economic influences that have negative implications on the social reality as a whole. Poverty is a case in point, which paved the way to frequent setbacks that have led to social structure instability. Accordingly, the present study aims to examine the role and effect of the Net of Social Protection Program in equally distributing social protection to curb or mitigate any negative consequnces that might happen to the poor segments and vulnerable people, who are succeptible to shocks, such as: the orphans, un

Smart water flooding (low salinity water flooding) was mainly invested in a sandstone reservoir. The main reasons for using low salinity water flooding are; to improve oil recovery and to give a support for the reservoir pressure.

In this study, two core plugs of sandstone were used with different permeability from south of Iraq to explain the effect of water injection with different ions concentration on the oil recovery. Water types that have been used are formation water, seawater, modified low salinity water, and deionized water.

The effects of water salinity, the flow rate of water injected, and the permeability of core plugs have been studied in order to summarize the best conditions of low salinity

In this study predication of crop coefficient (Kc) values through growing season for cucumber plant was conducted. A field experiment was carried out at AL Yusufiyah Township, in the Governorate of Baghdad, (latitude: 33°09' N, longitude: 44°24' E, and altitude: 34 m) in medium loam soil. The plant was cultivated inside the greenhouse under subsurface trickle irrigation system with soil water retention technology (SWRT) during the growing season 2017. Crop coefficient values were guessed from the direct method of measurements of daily crop evapotranspiration, while reference evapotranspiration was obtained from Agricultural Meteorology Project - Station of Baghdad - Abu-Ghraib.  The obtained results were showed that crop coeffici

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev