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Ultrafiltration and Reverse Osmosis Membranes for Treating Wastewater Effluent from Gas Turbine Power Plants using the Statistical Method of Taguchi
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A study on the treatment and reuse of oily wastewater generated from the process of fuel oil treatment of gas turbine power plant was performed. The feasibility of using hollow fiber ultrafiltration (UF) membrane and reverse osmosis (RO) membrane type polyamide thin-film composite in a pilot plant was investigated. Three different variables: pressure (0.5, 1, 1.5 and 2 bars), oil content (10, 20, 30 and 40 ppm), and temperature (15, 20, 30 and 40 ᵒC) were employed in the UF process while TDS was kept constant at 150 ppm. Four different variables: pressure (5, 6, 7 and 8 bar), oil content (2.5, 5, 7.5 and 10 ppm), total dissolved solids (TDS) (100, 200,300 and 400 ppm), and temperature (15, 20, 30 and 40 ᵒC) were manipulated with the help of statistical method of Taguchi in the RO process. Analysis of variable (ANOVA) and optimum condition was investigated. The study shows that pressure has the greatest impact on the flux of UF process, while it was temperature for RO process. It was noticed that more than 99% oil removal can be achieved and flux of 580 L/m2.hr by UF process and that the fouling mechanism of UF process follows the cake/gel layer filtration model. It was concluded that 100% removal of oil content can be achieved along with 99% for the TDS rejection and flux of 76 L/m2.hr by RO process. The result shows fouling in RO process follows the standard pore blocking model. Process optimization was conducted with confirmation test. It was concluded that the observed values are within ±5% of that the predicted which reflects a strong representative model. The treated wastewater has the characteristics of that used as fresh water and it can be reused to the process to reduce the operation cost.

 

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Publication Date
Mon Aug 31 2015
Journal Name
Journal Of Theoretical And Applied Information Technology
EXAM QUESTIONS CLASSIFICATION BASED ON BLOOM’S TAXONOMY COGNITIVE LEVEL USING CLASSIFIERS COMBINATION
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Publication Date
Sat Aug 31 2024
Journal Name
International Journal Of Intelligent Engineering And Systems
Credit Card Fraud Detection Using an Autoencoder Model with New Loss Function
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Publication Date
Sun Nov 01 2020
Journal Name
International Journal Of Engineering
Pavement Maintenance Management Using Multi-objective Optimization: (Case Study: Wasit Governorate-Iraq)
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Sentiment Analysis on Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique
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Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which perfo

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Engineering
Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm
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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Sentiment analysis in arabic language using machine learning: Iraqi dialect case study
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Publication Date
Wed Mar 01 2017
Journal Name
2017 Annual Conference On New Trends In Information & Communications Technology Applications (ntict)
Automatic Iraqi license plate recognition system using back propagation neural network (BPNN)
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Publication Date
Mon Jul 06 2026
Journal Name
Iraqi Journal For Computer Science And Mathematics
Enhanced Intrusion Detection Using Recurrent Neural Networks with Amino Acid Codon Features
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Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification

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Publication Date
Thu Oct 31 2019
Journal Name
Al-kindy College Medical Journal
Contraception as a Risk Factor of Trichomonas vaginalis Infection Among Women Attending Outpatient of Al-Batool Teaching Hospital for Maternity and Children-Baqubah-Iraq
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Background:The effects of contraception on Trichomonas vaginalis have important implications for women who suffer from infections associated with disruptions in the vaginal ecology, such as bacterial vaginitis and urinary tract infections.

Objective: To find the association of the common types of contraceptions with the Trichomonasvaginalis infection in women admitted to the Al-BatoolTeaching Hospital for Maternity and Children in Baqubah city.

Type of study: Cross-sectional study

Methods: This study consist of 75 women with contraception use and71 apparently healthy non contraception user women  admitted to outpatient in Al

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