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
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
... Show MoreIn this study, has been discussed the issue of non-interest income and its impact on the Iraqi banking sector profit for the period between (2008-2017) as it was the main objective of the study is to find the relationship between the non-interest income and the profits of the banking sector in order to know the size of the sector's dependence on non-interest income As well as an analysis of its profitability compared to selected countries, And to test hypotheses, the financial ratios and some statistical tests to determine the stability of the time series such as the test (Correlegram , Dickey -Fuller (depending on the statistical program (E-Views V8) and a simple linear regression method by (Minitab
... Show MoreThe research aims to study the basic concepts of the underwriting policy with its various indicators. The researcher studies the underwriting policy with its various indicators (sex, health status, age of the insured, insurance amount, The method of acceptance, payment method, and duration of insurance) where each of these indicators constitute an important factor in the productivity of life insurance policies, where the productivity of life insurance policies face many difficulties because insurance is a service and not a tangible material commodity and its benefits and not current. Therefore, the life insurance company needs to use a prudent underwriting policy so as not to endanger its financial position due to the expansion of the un
... Show MoreFind extract This research aims to find out (after learning strategy cells in the development of critical thinking and reflective thinking at the fifth-grade students in the geographic literary material). And follow researcher Almhnj demo for the purpose of achieving the goals of current research and design on an experimental group and a control with a test group after me and adopted chose researcher sample Find a way Mqsidih students of junior high Kadhimiya Boys of the breeding Baghdad / Karkh II and to verify the effectiveness of the search coined researcher hypotheses following cases: - 1. No statistically significant between the mean scores of the experimental group and the students taught her students that strategy differences (lea
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
... Show MoreAir-conditioning systems (ACs) are essential in hot and humid climates to ensure acceptable ambient air quality as well as thermal comfort for buildings users. It is essential to improve refrigeration system performance without increasing the effects of global warming potential (GWP) and ozone depletion potential (ODP). The main objective of this study is to evaluate the performance of an air conditioning system that operates with a liquid suction heat exchanger (LSHX) through implementing refrigerants with zero OPD and low GWP (i.e., R134a and R1234yf). Liquid suction heat exchanger (LSHX) was added to an automobile air conditioning system (AACS).When Liquid suction heat exchanger was added to the cycle, primary results indicated t
... Show MoreGingivitis, the initial stage of periodontal disease, is characterised by inflammation driven by dental biofilm and associated with oxidative stress. Matcha tea, a powdered green tea rich in antioxidants, has shown potential health benefits. This study aimed to investigate the effect of Matcha tea consumption on clinical periodontal parameters and salivary antioxidant levels in patients with gingivitis.
A randomised controlled clinical trial was conducted with 41 participants diagnosed with gingivitis.