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Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. A systematic literature review (SLR) strategy is used in this study to give a comprehensive overview of the existing literature on forecasting data on thyroid disease diagnosed using ML. This study includes 168 articles published between 2013 and 2022, gathered from high-quality journals and applied meta-analysis. The thyroid disease diagnoses (TDD) category, techniques, applications, and solutions were among the many elements considered and researched when reviewing the 41 articles of cited literature used in this research. According to our SLR, the current technique's actual application and efficacy are constrained by several outstanding issues associated with imbalance. In TDD, the technique of ML increases data-driven decision-making. In the Meta-analysis, 168 documents have been processed, and 41 documents on TDD are included for observation analysis. The limits of ML that are discussed in the discussion sections may guide the direction of future research. Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future

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Publication Date
Sun Mar 19 2023
Journal Name
Journal Of Educational And Psychological Researches
The Effect of Using the Generative Learning Model on the Achievement of First-Grade Intermediate Students of Chemical Concepts in Science
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Abstract

The current research aims to identify the effect of using a model of generative learning in the achievement of first-middle students of chemical concepts in science. The researcher adopted the null hypothesis, which is there is no statistically significant difference at the level (0.05) between the mean scores of the experimental group who study using the generative learning model and the average scores of the control group who study using the traditional method in the chemical concepts achievement test. The research consisted of (200) students of the first intermediate at Al-Farqadin Intermediate School for Boys affiliated with the Directorate of General Education in Baghdad Governorate / Al-Karkh 3 wit

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Publication Date
Fri Feb 28 2025
Journal Name
Energies
Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility
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Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Publication Date
Mon Apr 01 2019
Journal Name
Journal Of Educational And Psychological Researches
The Teaching Practices of Faculty Members in Northern Border University According to the Brain-Based Learning Theory
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The present study aims to identify the most and the least common teaching practices among faculty members in Northern Border University according to brain-based learning theory, as well as to identify the effect of sex, qualifications, faculty type, and years of experiences in teaching practices. The study sample consisted of (199) participants divided into 100 males and 99 females. The study results revealed that the most teaching practice among the study sample was ‘I am trying to create an Environment of encouragement and support within the classroom which found to be (4.4623). As for the least teaching practice was ‘I use a natural musical sounds to create student's mood to learn’ found to be (2.2965). The study results also in

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Publication Date
Tue Jun 01 2021
Journal Name
Baghdad Science Journal
A New Methodology to Find Private Key of RSA Based on Euler Totient Function
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          The aim of this paper is to present a new methodology to find the private key of RSA. A new initial value which is generated from a new equation is selected to speed up the process. In fact, after this value is found, brute force attack is chosen to discover the private key. In addition, for a proposed equation, the multiplier of Euler totient function to find both of the public key and the private key is assigned as 1. Then, it implies that an equation that estimates a new initial value is suitable for the small multiplier. The experimental results show that if all prime factors of the modulus are assigned larger than 3 and the multiplier is 1, the distance between an initial value and the private key

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Publication Date
Thu Mar 25 2021
Journal Name
International Journal For Research In Applied Sciences And Biotechnology
Review Article: Defective Genes Cause Disease
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Variation in DNA, and genes to a lesser or greater extent, can play an important role in most diseases; that is because this variation in will reflect and affect the function of DNA, and genes (combined genes and DNA or separately). This can be affected by environment, life style, as well as the inheriting from parents and previous generations. All these factors can contribute in human diseases. There are different alterations in genes, like imbalance and inequality in chromosomes, disorder in gene (deficiency in gene, which could be complex or single disorder), and cancer. In the last decades, scientists were focus on medicine and genetics; they pay an extensive attention to reach better understanding about diseases and their cause

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Publication Date
Mon May 06 2019
Journal Name
Journal Of Physical Education
Designing Teaching Aids and Their Effects on Learning and Retaining Diving and Cartwheel on Floor Exercises in Women’s’ Artistic Gymnastics
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Designing Teaching Aids and Their Effects on Learning and Retaining Diving and Cartwheel on Floor Exercises in Women’s’ Artistic Gymnastics

The research aimed at designing teaching aids that develop and help retain diving and cartwheel for third year college of physical education and sport sciences students in women’s artistic gymnastics. In addition to that, the researchers aimed at identifying the effect of these aids on learning and retaining cartwheel and diving in floor exercises. The researchers used the experimental method. The subjects were (20) third year female students from the college of physical education and sport sciences/ university of Baghdad sections K and H. the main experiment lasted for

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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
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Publication Date
Thu Dec 15 2022
Journal Name
Al-academy
Strategies of brain-based learning theory and its impact on the achievement of students of the Department of Art Education in Teaching Methods
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The aim of the current research is to reveal the effect of using brain-based learning theory strategies on the achievement of Art Education students in the subject of Teaching Methods. The experimental design with two equal experimental and control groups was used. The experimental design with two independent and equal groups was used, and the total of the research sample was (60) male and female students, (30) male and female students represented the experimental group, and (30) male and female students represented the control group. The researcher prepared the research tool represented by the cognitive achievement test consisting of (20) questions, and it was characterized by honesty and reliability, and the experiment lasted (6) weeks

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Publication Date
Sun Mar 17 2019
Journal Name
Baghdad Science Journal
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures
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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

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