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Prediction of unerupted canines and premolars widths in an Emirati population: development and validation of regression and machine learning models
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Abstract<p> This study aimed to develop a more accurate model for predicting the widths of unerupted canines and premolars in Emirati children, using deep learning and machine learning techniques. Dental models of 380 Emirati individuals aged 15–30 years were collected. The mesiodistal widths of permanent teeth were measured with a standardized orthodontic digital caliper. Regression models were developed using linear regression, Support Vector Regression (SVR; machine learning), and Artificial Neural Networks (ANN; deep learning). The widths of mandibular lateral incisors, central incisors, and the summed width of mandibular incisors were used as predictors. A two-tailed paired t-test was used to assess differences between measured and predicted values. Model performance was evaluated using pass rate (defined as predictions within ± 1 mm of measured values), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R <sup>2</sup> ). The dataset was randomly divided into training (70%), validation (20%), and test (10%) sets. A statistically significant difference ( <italic>P</italic>  < 0.001) was found between the values predicted by the Tanaka–Johnston equations and the measured values. In contrast, no significant differences ( <italic>P</italic>  > 0.05) were observed between the measured values and those predicted by newly derived models. The highest average pass rate (78.5%, MAE 0.66) was achieved with linear regression using one predictor (summed width of the mandibular incisors). The Tanaka–Johnston method showed limited validity in the Emirati population. Population-specific regression equations significantly improved prediction accuracy, while machine-learning approaches enhanced model stability without outperforming well-calibrated linear regression models, supporting the use of simple, interpretable models for clinically reliable mixed-dentition space analysis. </p>
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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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Publication Date
Fri Sep 26 2025
Journal Name
Applied Data Science And Analysis
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes In Networks And Systems
Using Machine Learning to Control Congestion in SDN: A Review
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Publication Date
Tue Jul 09 2024
Journal Name
Diagnostics
A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
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Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Regression shrinkage and selection variables via an adaptive elastic net model
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Abstract<p>In this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in </p> ... Show More
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Publication Date
Fri Oct 09 2026
Journal Name
Journal Of Baghdad College Of Dentistry
CBCT analysis of impacted maxillary canines
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Background: This study designed to shade light on the important role of CBCT in accurate localization of the impacted maxillary canines. Materials and method: Fifty two unilateral and bilateral impacted maxillary canines from 30 patients (24 females and 6 males) were evaluated by a volumetric 3D images obtained from cone beam CT. All samples attended to the specialist health center of dentistry in Al-Sadder City referred to CBCT by oral surgeons or orthodontists to detect the exact position of impacted upper canine in cases when there was no bulging buccally or palatally which aids to detect the exact position. Results: Mesio-palatal angulations had the highest rate (63.5%) followed by mesio-labial (19.2%), vertical (labial) (9.6%), disto-p

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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
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         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

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Publication Date
Wed Apr 30 2025
Journal Name
Open Veterinary Journal
Anatomical differences in the biliary duct system between canines and felines: A review article
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The liver is one of the largest glands in the digestive system and performs 13 various functions, including the secretion of hormones and enzymes. The gallbladder serves as a storage reservoir for secretions before they are released into the digestive system through the duodenum. The bile ducts branch from the liver’s lobes and ultimately connect to the digestive system, making this structure significant and distinct among different animal species. This review focuses on the differences between dogs and cats, highlighting the importance of these differences from both health and pathological perspectives. After conducting a detailed scientific review of the biliary tree in dogs and cats, we concluded that cats are more susceptible to the d

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Publication Date
Wed Jan 01 2025
Journal Name
Open Veterinary Journal
Anatomical differences in the biliary duct system between canines and felines: A review article
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The liver is one of the largest glands in the digestive system and performs 13 various functions, including the secretion of hormones and enzymes. The gallbladder serves as a storage reservoir for secretions before they are released into the digestive system through the duodenum. The bile ducts branch from the liver’s lobes and ultimately connect to the digestive system, making this structure significant and distinct among different animal species. This review focuses on the differences between dogs and cats, highlighting the importance of these differences from both health and pathological perspectives. After conducting a detailed scientific review of the biliary tree in dogs and cats, we concluded that cats are more susceptible

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
The Use of the Regression Tree and the Support Vector Machine in the Classification of the Iraqi Stock Exchange for the Period 2019-2020
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 The financial markets are one of the sectors whose data is characterized by continuous movement in most of the times and it is constantly changing, so it is difficult to predict its trends , and this leads to the need of methods , means and techniques for making decisions, and that pushes investors and analysts in the financial markets to use various and different methods in order to reach at predicting the movement of the direction of the financial markets. In order to reach the goal of making decisions in different investments, where the algorithm of the support vector machine and the CART regression tree algorithm are used to classify the stock data in order to determine

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