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Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five attributes of the training process. The results of the second experiment showed improvement in the performance of the KNN and the Multilayer Perceptron. The results of the second experiment showed a slight decrease in the performance of the Random Forest with 97.5 % accuracy.

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Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology & Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

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Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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This paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
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Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

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Publication Date
Sun Oct 01 2023
Journal Name
Journal Of Applied Hematology
D-dimer and Ferritin Levels in Prediction of COVID-19 Severity
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Abstract<sec> <title>BACKGROUND:

The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.

AIM OF THE STUDY:

The aim of the study is to demonstrate the role of COVID-19 on coagulation parameters D-dimer and ferritin with their association with COVID-19 severity and disease progression in a single-center study.

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Applied Engineering Science
Rutting prediction of hot mix asphalt mixtures reinforced by ceramic fibers
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One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Ghrelin and Leptin and Their Relations with Insulin Resistance in Diabetes Mellitus Type 2 Patients
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Ghrelin and leptin are hunger hormones related to type 2 diabetes mellitus (T2DM), and the pathogenesis of T2DM is the abnormality in insulin secretion and insulin resistance (IR). The aim of this study is to evaluate ghrelin and leptin concentrations in blood and to specify the relationship of these hormones as dependent variables with some biochemical and clinical measurements in T2DM patients. In this study, forty one T2DM and forty three non-diabetes mellitus (non-DM) subjects, aged between 40-60 years and with normal weight, were enrolled. Fasting serum ghrelin and leptin were estimated by enzyme-linked immunosorbent assay (ELISA). In our results ghrelin was significantly increased, and leptin was significantly decreased, in T2DM pa

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Relationship between serum Nesfatin-1, Adiponectin, Resistin Concentration, and Obesity with Type 2 Diabetes Mellitus
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            Diabetes mellitus caused by insulin resistance is prompted by obesity. Neuropeptide Nesfatin-1 was identified in several organs, including the central nervous system and pancreatic islet cells. Nesfatin-1 peptide appears to be involved in hypothalamic circuits that energy homeostasis and control food intake. Adiponectin is a plasma collagen-like protein produced by adipocytes that have been linked to the development of insulin resistance (IR), diabetes mellitus type 2 (DMT2), and cardiovascular disease (CVD). Resistin was first identified as an adipose tissue–specific hormone that was linked to obesity and diabetes.  The aim of this study was to estimate the relationship between human serum nesfatin-1, adiponect

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Publication Date
Thu May 01 2025
Journal Name
2025 3rd International Conference On Business Analytics For Technology And Security (icbats)
Comparison of Deep Neural Network Models (LSTM, Bi-LSTM, GRU and Bi-GRU) for Gold Price Prediction
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This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

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Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Main Difficulties Faced by EFL Students in Language Learning
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Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers.  This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning.  A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study wh

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Publication Date
Sat Jun 06 2020
Journal Name
Journal Of College Of Education For Women
Main Difficulties Faced by EFL Students in Language Learning
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Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers. This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning. A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study which consists of 90 (male and female) stud

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