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
Non-alcoholic fatty liver disease (NAFLD) is one of chronic liver and defines by fat accumulation ≥5% in liver which can progresses to non-alcoholic steatohepatitis (NASH). NAFLD related to obesity as well as non obese individuals. Adiponectin is a cytokine secreted from adipose tissue involved NAFLD pathogenesis and liked with obesity. Irisin is a myokine, has a convenient effect against metabolic diseases such as obesity, disylipemia diabetes type 2 and reversed liver steatosis and may be related with NAFLD. Vitamin D is one of the fat soluble vitamins and more precisely as a pro-hormone through its metabolite (1,25(OH)2 cholecalciferol) the major steroid hormone. After the skin exposure to the light, vitamin D undergoes to
... Show MoreThe emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA
... Show MoreInterleukin -33 is a new member of the IL-1 superfamily of cytokines that is expressed mainly by stromal cells.Its expression is upregulated following pro-inflammatory stimulation. Aim of the present study was to assess the serum IL-33 level and its relationship with inflammatory biomarker CRP in Iraqi females patients with celiac disease. Thirty five patients with celiac disease (CD) and thirty healthy individuals as control group were enrolled in this study,their age ranged (20-35) year.Anti-Gliadin IgA ,IgG and Anti-Tissue IgA ,IgG were estimated in all subjects as diagnostic parameters .ESR and CRP were assayed as inflammatory biomarkers. IL-33 was determined in patients and control groups.
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreThe aim of the current study is to identify the effectiveness of cognitive conflict strategy in comprehending reading among literary fifth students in literature and literature texts. The researcher uses experimental method with partial control. The sample consisted of (80) students distributed into control and experimental groups. The scientific material, the behavioral goals, the teaching plans, and the instrument of the research have been prepared (reading comprehension test) by the researcher.
The instrument's validity and reliability have been calculated and then applied to the sample. After treating the data statistically by using SPSS, the results have revealed that there is a statistically significant difference at the si
... Show MoreShadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit
... Show MoreTrimethoprim derivative Schiff bases are versatile ligands synthesized with carbonyl groups from the condensation of primary amines (amino acids). Because of their broad range of biological activity, these compounds are very important in the medical and pharmaceutical fields. Biological activities such as antibacterial, antifungal and antitumor activity are often seen. Transition metal complexes derived from biological activity Schiff base ligands have been commonly used
Trimethoprim derivative Schiff bases are versatile ligands synthesized with carbonyl groups from the condensation of primary amines (amino acids). Because of their broad range of biological activity, these compounds are very important in the medical and pharmaceutical fields. Biological activities such as antibacterial, antifungal and antitumor activity are often seen. Transition metal complexes derived from biological activity Schiff base ligands have been commonly used.