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Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
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Publication Date
Wed Jan 01 2020
Journal Name
Plant Archives
Study of bacterial contamination of defected eggshells and egg contents in Baghdad city
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The main aim of the current research is to focus the light on some bacterial contamination on cracked eggshell and egg content plus studying the sensitivity of these bacterial isolates to antibiotics. For this purpose, a total of 50 eggs were collected from the markets in Baghdad city (Iraq) and examined for bacterial isolation from cracked eggshells and from the egg contents. The bacterial isolates were cultured and purified then transferred to a specific media to study its sensitivity against antibiotics. The results revealed that bacteria isolated from both cracked eggshells (46%) and egg contents (44%). The bacteria isolated include E. coli, Staphylococcus, Enterococcus faecalis, Enterobacter and Pseudomonas. The results of antibiotic s

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Publication Date
Sat Jun 01 2024
Journal Name
Ecological Engineering & Environmental Technology
Employing Phytoremediation Methods to Extract Heavy Metals from Polluted Soils
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The phytoremediation technique has become very efficient for treating soil contaminated with heavy metals. In this study, a pot experiment was conducted where the Dodonaea plant (known as hops) was grown, and soil previously contaminated with metals (Zn, Ni, Cd) was added at concentrations 100, 50, 0 mg·kg-1 for Ni and Zn, and at concentrations of 0, 5, 10 mg·kg-1 for cadmium. Irrigation was done within the limits of the field capacity of the soil. Cadmium, nickel and zinc was estimated in the soil to find out the capacity of plants to the absorption of heavy and contaminated metals by using bioconcentration factors (BCFs), bioaccumulation coefficient (BAC) and translocation factor (TF). Additionally, BCF values of both Ni and Zn were l

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Publication Date
Fri Oct 07 2011
Journal Name
Ibn Al- Haitham J. For Pure & Appl. Sci.
Assessment of the Level of Trace Metals in Commonly Edible Vegetables and Fruits of Baghdad City (Iraq)
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A study was carried out to determine the concentrations of trace metals in vegetables and fruits, which are locally available in the markets of Baghdad-samples of fourteen varieties of vegetables and fruits, belonging to Beta vulgaris, Brassica rapa, Daucus carota, Allium cepa, Eurica sativa, Malva silvestris, Coriandrum Sativum, Trigonella Foenum craecum, Anethum graveolens, Barassica oleracea, Phaseolus vulgaris, citrus reticulata, Py rus malus, and Punica granatum. Analysis for Cd,Pb, Mn, Fe, Co, Ni, Cu and Zn were determined by flame atomic absorption sp ectrophotometry. The results indicated that the Malva silvestris recorded the highest concentrations of Cd and Mn while Allium cepa showed the highest concentrations of Pb and Cu. But E

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Using Wild Plant Species Grown in Wadi Al – Tib Region North East of Al – Ammara, Iraq, as Indicators of Heavy Metals Accumulation
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The current study included, studying the ability of eight genera of plants belong to Brassicaceae family, Brassica tournifortii, Cakile Arabica, Capsella bursa – pastoris,Carrichtera annua, Diplotaxis acris, Diplotaxis haru , Eruca sativa and Erucaria hispanica to accumulate ten heavy metals Cadmium, Chromium , Copper, Mercury, Manganese ,Nickel ,Lead ,and Zinc . Plant leaves samples were collected from Al-Tib area during spring of 2021.The data demonstrated that, the highest conc. of Cd was 2.7 mg/kg in Diplotaxis acris leaves and lower value was 0.3 mg/kg in Cakile Arabica leaves. For Co, the highest conc.was 1.3 mg/kg in Capsella bursa – pastoris leaves, whereas the lower value was 0.5 mg/kg in Cakile arabica leaves. As for Cr ele

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Publication Date
Wed Jan 13 2016
Journal Name
University Of Baghdad
Employ Mathematical Model and Neural Networks for Determining Rate Environmental Contamination
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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network
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Abstract<p>Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.</p>
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Publication Date
Fri Aug 27 2021
Journal Name
Human Interaction, Emerging Technologies And Future Systems V: Proceedings Of The 5th International Virtual Conference On Human Interaction And Emerging Technologies, Ihiet 2021, August 27-29, 2021 And The 6th Ihiet: Future Systems (ihiet-fs 2021), October 28-30, 2021, France
Electricity Consumption Forecasting in Iraq with Artificial Neural Network
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Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
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Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Optimum Dimensions of Hydraulic Structures and Foundation Using Genetic Algorithm coupled with Artificial Neural Network
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      A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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