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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
Wed Jun 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
The Impact of the Strategic Leadership in Strategic Learning Exploratory Research at Anbar University
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        This study aims to know the extent of the impact of Strategic Leadership as an independent variable in Strategic Learning as a dependent variable to help the senior leadership in Anbar University to take the right decisions to develop Strategic Learning programs in light of the circumstances of the Covid-19 and the sudden decisions adopted by the university to switch to E-learning and to blend. The survey was conducted by distributing a questionnaire that was adopted as a primary tool in data collection from the study sample represented by the university's senior leaders, An intentional random sample of (105) was selected from our community of (127), the data were analyzed by (SPSS) Depe

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Publication Date
Sun Oct 01 2023
Journal Name
Journal Of Surgical Case Reports
Agenesis of flexor pollicis longus without thenar hypoplasia: a case report and literature review
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Abstract<p>We present a case of congenital of flexor pollicis longus agenesis without thenar hypoplasia in a 12-year-old girl with no history of trauma. Two-staged corrective surgery was planned. In the first stage, the flexor pulley was reconstructed using silicone followed by the second stage 3 months later when flexor pollicis longus reconstruction was performed using tendon transfer of the flexor digitorum superficialis. The patient completed post-operative physiotherapy and the result of the surgical treatment in both functional and cosmetic aspects was, in the authors’ opinion, excellent.</p>
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Publication Date
Sat Jan 01 2022
Journal Name
Ieee Access
Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review
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Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

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Publication Date
Thu Aug 15 2024
Journal Name
Advanced Journal Of Chemistry, Section A
Historical Background, Literature Review on the Synthesis and Applicability of Azo-Dye Compounds: An Extensive Review
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In this article, the detailed information was presented about azo-dyes, including a general description of this class of compounds, which included a historical overview, observations on the chemical structure of these compounds, particularly focusing on the azo group (-N=N-) responsible for their coloring properties. In addition, we provided a schematic of the first synthesized azo-compound. Furthermore, we mentioned the general properties of them and viewed a detailed explanation for the categorization of them either aliphatic or aromatic, subdivisions for each category or which category is the most widespread, and then illustrated the types for this class of organic compounds. The effective operator in these compounds called diazonium-sal

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Publication Date
Sun Sep 03 2023
Journal Name
Al-mansour Journal
Biometrics Systems Challenges in a Post-COVID-19 Pandemic World: A review
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One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an

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Publication Date
Sun Apr 06 2014
Journal Name
Journal Of Educational And Psychological Researches
Effectiveness of cooperative learning in at the implementation of floral material for celli graph and decoration
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The purpose of this resesrh know (the effectiveness of cooperative lerarning implementation of floral material for calligraphy and ornamentation) To achieve the aim of the research scholar put the two zeros hypotheses: in light of the findings of the present research the researcher concluded a number of conclusions, including: -
1 - Sum strategy helps the learner to be positive in all the information and regulations, monitoring and evaluation during the learning process.
2 - This strategy helps the learner to use information and knowledge and their use in various educational positions, and to achieve better education to increase its ability to develop thinking skills and positive trends towards the article.
In light of this, the

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Publication Date
Tue Jul 02 2013
Journal Name
Journal Of Baghdad College Of Dentistry
Local Drug Delivery Systems for Treating Periodontal Diseases: A Review of Literature
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Publication Date
Wed Sep 30 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Local drug delivery systems for treating periodontal diseases (A review of literature)
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In this review of literature, the light will be concentrated on the local drugs delivery systems for treating the periodontal diseases. Principles, types, advantages and indications of each type will be discussed in this paper.

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
An Overview of Audio-Visual Source Separation Using Deep Learning
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    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

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