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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
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Analysing errors in learning the preasent continuous tense:Associating interference with strategy of instruction
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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Compression-based Data Reduction Technique for IoT Sensor Networks
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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

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Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Foreign Trade of Iraq Between the imperatives of economic diversification And the challenges of joining the (WTO)
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         Interested in this research shed light on the reality of foreign trade to Iraq Who suffers from a marked deterioration due to poor economic diversification of the country And increase the degree of economic exposure   , Which creates a state of extreme caution towards the question of accession to the (WTO)   , As controls Iraq's foreign trade commodity  , a president of one oil As well as the contribution of this item  , and by a large formation in GDP   , And that such a large and dangerous decline in the degree of economic diversification will create negative effects On overall economic activity components&

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Publication Date
Mon Jul 01 2019
Journal Name
2019 International Conference On Electrical Engineering And Informatics (iceei)
Health Information Exchange: Current Trends, Challenges and Recommendations
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Publication Date
Mon Jan 01 2024
Journal Name
Pathology - Research And Practice
Artificial intelligence in cancer diagnosis: Opportunities and challenges
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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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Publication Date
Fri Jan 31 2025
Journal Name
Passer Journal Of Basic And Applied Sciences
Enhanced Security Taxonomy for Fog-Enabled VANETs: A Comprehensive Survey on Attacks, Challenges, Applications and Architectures
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Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c

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Publication Date
Sat Jan 01 2022
Journal Name
Pharmaceutical Sciences Asia
Exploring the role of community pharmacists in preventing the onsite infection during COVID-19 pandemic
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This study aimed to evaluate the preparedness and adherence of community pharmacists to the International Pharmaceutical Federation (FIP) Health Advisory COVID-19 guidelines for pharmacists (July 2020) during COVID-19 pandemic. This was a cross-sectional study based on electronic survey using google form, which was distributed from November 19, 2020 to January 1, 2021 using social media platforms. The survey measured 21 pharmacy preventive measures (PM). A multivariate regression analysis was used to identify factors influencing pharmacy implementing of PM. Hand disinfection after serving patients represented the main adopted measure (89.3%). Surprisingly, only 35.4% of participants implemented the proper ways of hand disinfection during fa

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Publication Date
Fri Dec 23 2022
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Plant-derived Molecules for the Treatment of Tuberculosis: A Review
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Synthetic anti-TB drugs are being used to treat tuberculosis (TB) as they are effective, however, they are accompanied by many side effects. The disease has remained largely uncured till date. The use of plant extracts or phytochemicals along with the anti-TB drugs is a very attractive strategy to make the treatment more effective as phytochemicals have no side-effects, are much less toxic than synthetic anti-TB drugs, are safe to use and most importantly, do not produce resistant strains as opposed to synthetic anti-TB drugs. Approximately 420,000 plant species have been identified globally and among them only a few have been explored for their therapeutic potential. Traditional medicine in different parts of the world has employed crud

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Publication Date
Sat Aug 30 2025
Journal Name
Gsc Biological And Pharmaceutical Sciences
The role of environmental stress in fish health: A review
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Fish are regarded as a crucial indicator of alterations in the aquatic environment due to their position at the apex of the food chain. Monitoring these alterations is crucial for identifying modifications in the aquatic ecosystem. The principal elements influencing fish health are temperature, pH, dissolved oxygen, salinity, pesticide contamination, microplastics, and algal presence. These elements substantially influence fish health regarding development, reproduction, respiration, oxygen stress, and the internal enzymes associated with digesting and other metabolic functions. Alterations in global environmental conditions and anthropogenic pollutants result in modifications to fish populations, their lives, and their behavior and

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