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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 certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

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Publication Date
Sat Dec 01 2018
Journal Name
Applied Soft Computing
A new evolutionary algorithm with locally assisted heuristic for complex detection in protein interaction networks
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Publication Date
Mon Apr 28 2025
Journal Name
Chemical Papers
New chemiluminometric method for the determination of azithromycin in a continuous flow injection analysis system
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A novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

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Publication Date
Mon Jun 12 2017
Journal Name
Day 3 Wed, June 14, 2017
A New Practical Method for Predicting Equivalent Drainage Area of Well in Tight Gas Reservoirs
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Abstract<p>The tight gas is one of the main types of the unconventional gas. Typically the tight gas reservoirs consist of highly heterogeneous low permeability reservoir. The economic evaluation for the production from tight gas production is very challenging task because of prevailing uncertainties associated with key reservoir properties, such as porosity, permeability as well as drainage boundary. However one of the important parameters requiring in this economic evaluation is the equivalent drainage area of the well, which relates the actual volume of fluids (e.g gas) produced or withdrawn from the reservoir at a certain moment that changes with time. It is difficult to predict this equival</p> ... Show More
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Publication Date
Wed Jun 01 2022
Journal Name
Analytical Methods In Environmental Chemistry Journal
Design a continuous microfluidic flow cell for turbidimetric-flow injection technology: A new approach for routine analysis of active pharmaceutical formulations
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Publication Date
Sun Jun 01 2025
Journal Name
International Journal Of Environmental Sciences
Critical Discourse Analysis Of Ideological Interdiscursivity In Selected Artificial Intelligence English Extracts
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The study employs Critical Discourse Analysis (CDA) to analyze how technological discourses are influenced by AI-generate d English texts. The research marries Fairclough’s three-dimensional discourse analysis, Van Dijk’s socio-cognitive approach, and Corpus-Assisted Discourse Studies (CADS) in the use of mixed-methods research, integrating primarily qualitative analysis with quantitative corpus-based data, to perform a thorough analysis of twenty AI-produced English texts. The findings identify the sophisticated linguistic mechanisms through which AI language employs modality, nominalization, passive voice, and interdiscursive blending to normalize and legitimize dominant contemporary ideologies. These mechanisms serve to legitimize te

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Publication Date
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type
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Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
The Impact of Artificial Intelligence on English Language Learning Challenges and Opportunities
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Publication Date
Wed Aug 31 2022
Journal Name
International Review On Modelling And Simulations (iremos)
Inexpensive DC to AC Inverter Device for Artificial Saline Soils Resistivity Analysis
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In aired and semiarid areas like Iraq, saline soil may be considered one of the major concerns. In addition to environmental effects, they may produce significant geotechnical hazards that could interrupt the structure stability depending on the salt type and its concentration. So, it is crucial to identify the degree of the soil salinity with a proper tool for getting a qualified assessment and consequently offering a suitable treatment. In this paper, the electrical resistivity technique has been employed to detect the degree of soil salinity by considering a new electronic system. The system used a single-phase Direct Current (DC) to Alternating Current (AC) inverter accompanied by a transformer. Natural soils became artificially saline

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Publication Date
Sun Oct 19 2025
Journal Name
Studies In Systems, Decision And Control
The Role of Artificial Intelligence in Achieving Tax Compliance: Evidence from Iraq
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This study focuses on how tax administrations in Iraq use Artificial Intelligence (AI) techniques to monitor tax evasion for individuals and companies to achieve Tax Compliance (TC). AI was measured through four dimensions: Advanced Data Analytics Techniques (ADAT), Explainable AI (EAI), Machine learning (ML), and Robotic Process Automation (RPA). At the same time, TC was measured through registration, accounting, and tax payment stages. We relied on the questionnaire form to measure the variables. A sample of employees in the General Tax Authority in Iraq was selected, and a questionnaire was distributed to 132 people. The results indicated that the dimensions of AI affect achieving TC at all stages. This study provides evidence of using A

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