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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 Sep 06 2020
Journal Name
European Journal Of Dental Education
Evaluation of technology‐based learning by dental students during the pandemic outbreak of coronavirus disease 2019
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Publication Date
Wed Mar 30 2022
Journal Name
Journal Of Educational And Psychological Researches
Evaluating the Distance Learning Experience in light of the Spread of the Corona Pandemic - Covid 19 from the Teachers' Point of View in Islamic Science Institutes in the Sultanate of Oman
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The study aimed to evaluate the distance learning experience in light of the spread of the Corona pandemic - Covid19 - from the teachers' point of view in Islamic Science Institutes in the Sultanate of Oman, which was applied during the second semester of the 2019/2020 academic year. The study sample consisted of (77) teachers from The Islamic Science Institutes of The Sultan Qaboos Higher Center for Culture and Science. The researchers prepared a questionnaire to evaluate the reality of the experience. The study results  revealed, the followings: The Department of Educational Affairs and Training at The Sultan Qaboos Higher Center for Culture and Science was able to a moderate degree in the rapid transition to a distance learning s

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Publication Date
Tue Nov 06 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Correlation between Thyroid-related Hormones and Preeclampsia
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Objectives: In order to highlight the TSH and thyroid hormones levels in preeclamptic and healthy pregnant
women.
Methodology: Ninety patients with preeclampsia were divided into two groups according to the severity of
disease; those with mild disease (37 patients) and those with a severe form (53 patients). A separate group of 30
normal women were included as a normal control group. Venus blood samples were collected from all groups
and the serum was obtained for hormone analysis by ELISA test. Results are expressed using SPSS for window
version 11.0.
Results: Mean serum TSH levels were significantly increased in both of mild and severe preeclampsia compared
with normal pregnancy, and T3 serum level showed a sign

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network
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         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Revision of some Biomarkers with Cytokines in Breast Cancer
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Breast cancer is the most repeatedly detected cancer category and the second reason cause of cancer-linked deaths among women worldwide. Tumor bio-indictor is a term utilized to describe possible indicators for carcinoma diagnosis, development and progression. The goal of this study is to evaluate part of some cytokines and biomarkers for both serum and saliva samples in breast cancer then estimate their potential value in the early diagnosis of breast cancer by doing more researches in saliva, and utilizing saliva instead of blood (serum and plasma) in sample collection from patients. Serum and salivary samples were taken from 72 patients with breast cancer and 45 healthy controls, in order to investigate the following

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Publication Date
Fri Jul 01 2022
Journal Name
Iop Conference Series: Earth And Environmental Science
A study Some Technical Indicators of the Local Designed Machine
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Abstract<p>The effect of compound machine on wheat/ AlNoor cultivar was studied based on some technical indicators. were tested under three speeds ( 2.541, 3.433 and 4.091km.hr<sup>-1</sup>) and three tillage depths (14, 16 and 18cm). The experiments were conducted in a factorial experiment under complete randomized design with three replications. The results showed that the 2.541km.hr<sup>-1</sup> practical speed was significantly better than other two speed in all studied conditions. Except for the FC, which achieved the best results with the third speed 4.091 km.hr<sup>-1</sup>. mechanical parameters, plant growth parameters and yield and growth parameters. The 1</p> ... Show More
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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
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Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

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Publication Date
Fri Aug 05 2022
Journal Name
International Journal Of Language And Literary Studies
Literature and Ecology: Promoting an Eco-Consciousness through Children Literature
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The study scrutinises intermingled relations between children literature and some ecological issues. Such interwoven relationships would be highly recommended to encourage children to explore and identify themselves with nature from early ages to avoid facing an extreme experience later on.  The research limits its scope to two novels Suzanne Collins’ (1962) The Hunger Games trilogy (2003-2007) and William Golding’s (1911-1993) Lord of the Flies (1954), and both novels have no direct connections with Ecology and the Eco-consciousness, yet it offers an insightful description about Man’s experience with Nature. Moreover, it raises serious moral questions, raises awareness, heals wounds and suggests solutions for the problems th

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Publication Date
Wed Jan 30 2013
Journal Name
Al-kindy College Medical Journal
Using the Abbreviated Version of Modification of Diet in Renal Disease Formula as A Primary Estimate for the Prevalence of Undiagnosed Chronic Kidney Disease in Patients with Acute Stroke
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Introduction: The association between acute stroke and
renal function is well known. The aim of this study is to
know which group of patients with acute stroke is more
likely to have undiagnosed Chronic Kidney Disease and
which risk factors are more likely to be associated with.
Methods:We studied 77 patients who were diagnosed to
have an acute stroke.Patients were selected between
April2011andJune 2011 using the " 4-variable
Modification of
Diet in Renal Disease Formula " which estimates
Glomerular Filtration Rate using four variables :serum
creatinine ,age ,race and gender.
Results :The study included 38 male and 39 females
patients ,aged (35-95) years. Glomerular Filtration Rate in
patients wi

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