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Artificial intelligence in cancer diagnosis: Opportunities and challenges
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Publication Date
Wed Dec 01 2021
Journal Name
International Journal Bioautomation
Model for Prediction of the Weight and Height Measurements of Patients with Disabilities for Diagnosis and Therapy
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Background: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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Publication Date
Thu Aug 10 2023
Journal Name
Journal For Re Attach Therapy And Developmental Diversities
Challenges of Developing Communicative Competence among Iraqi EFL Undergraduate Student
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Research in the field of English language as a foreign language (EFL) has been consistently highlighted the need for communicative competence skills among students. Accompanied by the validated positive impact of technologies on students’ skills’, this study aims to explore the strategies used by EFL students in enhancing their communicative competence using digital platforms and identify the factors of developing communicative competence using digital platforms (linguistic factors, environmental factors, psychological factors, and university-related factors). The mixed-method research design was utilized to obtain data from Iraqi undergraduate EFL students. The study was conducted in the Iraqi University in Baghdad Iraq. EFL undergradu

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Publication Date
Fri Nov 03 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences( P-issn 1683 - 3597 E-issn 2521 - 3512)
Professional Challenges Facing Pharmacists Working at Public Hospitals in an Iraqi Province: A Qualitative Study
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In Iraq, because of the dramatic turnovers facing the country for three decades, pharmacists continue to experience significant professional challenges in both the public and private sectors. The present study aimed to explore the professional challenges and obstacles facing Iraqi pharmacists working in public hospitals. This qualitative study included face-to-face semi-structured interviews with open-ended questions with hospital pharmacists. The participants were selected purposefully (with ≥ 3 years of experience) to work at governmental hospitals in Karbala province between December 2022 and April 2023. The audio-recording interviews were scripted. Thematic analyses were used to generate themes and subthemes from the interview

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Publication Date
Wed Sep 01 2021
Journal Name
Euphrates Journal Of Agriculture Science
A survey study on artificial fraud and thickeners in local and imported cream and yogurt that available in Baghdad city
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The current study aimed to detect artificial fraud in chemical additives (formalin, hydrogen peroxide) and to detect thickeners (starch) in imported and local dairy products and to determine their suitability for human consumption due to the high demand for their consumption by members of society, 20 samples of products were collected The local and imported milk in the Iraqi markets of the city of Baghdad consisted of 10 samples of yoghurt and 10 samples of cream. The tests were carried out, which were the detection of chemicals and additive thickeners such as formalin, hydrogen peroxide and starch, respectively, as well as the examination of the microbial load, which included the detection of colon bacteria, yeasts and molds, and the resul

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Publication Date
Mon Jul 18 2022
Journal Name
Journal Of Genetic And Environmental Resources Conservation
Estimation of immunological markers (IL1Alpha, IL-6 and CRP) in colorectal cancer in Iraqi patients
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Cytokines are A type of protein that is made by certain immune and non-immune cells and has an effect on the immune system. Some cytokines stimulate the immune system and others slow it down. Interleukins (ILs) can be divided into several families with more than 40 subfamily members. They can interact with a variety of cells that alter the immune system and act on a wide range of cancers. In the past several years, ILs have attracted substantial attention because of their distinct roles in CRC that provide a new and promising strategy for CRC. In general, ILs facilitate CRC by promoting tumorigenesis, tumour growth, angiogenesis, and cancer cell invasion and metastasis and inhibit CRC via complex pathways. The Bioassay Technology Human Inte

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Publication Date
Fri Jun 01 2018
Journal Name
Kerbala Journal Of Medicine
Polymerase Chain Reaction Testing in Comparison to Culture of Cerebrospinal Fluid for Diagnosis of Bacterial Meningitis in Children
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Publication Date
Thu Aug 01 2019
Journal Name
The Journal Of Solid Waste Technology And Management
Recycling of Waste Compact Discs in Concrete Mix: Lab Investigations and Artificial Neural Networks Modeling
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This study aimed to investigate the incorporation of recycled waste compact discs (WCDs) powder in concrete mixes to replace the fine aggregate by 5%, 10%, 15% and 20%. Compared to the reference concrete mix, results revealed that using WCDs powder in concrete mixes improved the workability and the dry density. The results demonstrated that the compressive, flexural, and split tensile strengths values for the WCDs-modified concrete mixes showed tendency to increase above the reference mix. However, at 28 days curing age, the strengths values for WCDs-modified concrete mixes were comparable to those for the reference mix. The leaching test revealed that none of the WCDs constituents was detected in the leachant after 180 days. The

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Publication Date
Wed Jun 01 2016
Journal Name
Journal Of Engineering
Seismic Effects and Static Analysis for the Artificial Damped Outrigger Systems in Tall R.C Buildings
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This paper studies the combination fluid viscous dampers in the outrigger system to add supplementary damping into the structure, which purpose to remove the dependability of the structure to lower variable intrinsic damping. It works by connecting the central core, comprising either shear walls or braced frames, to the outer perimeter columns.

     The modal considered is a 36 storey square high rise reinforced concrete building. By constructing a discrete lumped mass model, and using frequency-based response function, two systems of dampers, parallel and series systems are studied. The maximum lateral load at the top of the building is calculated, and  this load  w

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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