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
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.
Quality control of well logs has always been an important objective in reservoir studies because of the key role played by well logs as input data. This study aims to make a quality control on well logs data for two wells of Yamama formation in southern Iraqi field to ensuring and enhancing the measurement accuracy. In the beginning, the calibration data of before and after surveys are applied as initial evaluation for the quality of density log in well R-1. Then, depth matching is used to fit the depth of all logs in each well. After that, the comparison between the main and repeat sections is helped to check the repeatability. Finally, all uncorrected logs are environmentally corrected to remove the effects of the borehole conditi
... Show MoreCoeliac disease is an immunologically mediated disease of the small intestinal mucosa, characterized by flattening of the small intestinal villi, increased numbers of intra-epithelial lymphocytes and inflammatory cell infiltrates in the lamina propria, resulting in gut damage and nonspecific malabsorption of nutrients. The disease is elicited by ingestion of gluten, a protein found in several cereals, principally wheat, but also barley and to a lesser extent, oats. Successful treatment is avoidance of dietary gluten. Long-standing evidence suggests a T-cell-mediated response to peptides derived from the gliadin fraction of wheat gluten, leading to immunologically mediated intestinal injury in genetically susceptible individuals. The
... Show MoreGeneral Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k
... Show MoreGender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
... Show MoreBackground : Gastroesophageal reflux disease (GERD) is one of chronic gastrointestinal diseases in which patient may be asymptomatic or was complained from heartburn and regurgitation or pulmonary symptoms. Aim of the study : Examine the serum level of sHLA-G in GERD patients and can be used as a biomarker for early detection of GERD disease. Materials and methods : The design of the study was a case- control prospective enrolled forty patients consulted Gastroenterology Unit- Al-Kindy Teaching Hospital, were diagnosed as GERD by their physician, and compared to second forty control healthy group form January-2023 to May-2024. Serum used for quantitative assessment of soluble HLA-G (sHLA-G) using a sandwich enzyme-linked immunosorbent a
... Show MoreBackground: Imprelon® Biostar foils are new alternative tray material that has become increasingly popular because oftheir several advantages. Also, (Duran®) is another type of Biostar foils which is used in splint therapy. This study assessed some mechanical properties of these two types Biostar sheets in comparison with some types of acrylic resins used for construction of trays and splints. Materials and Methods: A total of 150 specimens were prepared, 30 specimens for each test, 10 for each group material in order to assess some mechanical properties of the Imprelon® Biostar foil (dimension stability, surface roughness and shear bond strength of Imprelon® materialto zinc oxide impression material) and compare them to that of the oth
... Show MoreThe emergence of mixed matrix membranes (MMMs) or nanocomposite membranes embedded with inorganic nanoparticles (NPs) has opened up a possibility for developing different polymeric membranes with improved physicochemical properties, mechanical properties and performance for resolving environmental and energy-effective water purification. This paper presents an overview of the effects of different hydrophilic nanomaterials, including mineral nanomaterials (e.g., silicon dioxide (SiO2) and zeolite), metals oxide (e.g., copper oxide (CuO), zirconium dioxide (ZrO2), zinc oxide (ZnO), antimony tin oxide (ATO), iron (III) oxide (Fe2O3) and tungsten oxide (WOX)), two-dimensional transition (e.g., MXene), metal–organic framework (MOFs), c
... Show MoreThe aim of this study is to estimate the parameters and reliability function for kumaraswamy distribution of this two positive parameter (a,b > 0), which is a continuous probability that has many characterstics with the beta distribution with extra advantages.
The shape of the function for this distribution and the most important characterstics are explained and estimated the two parameter (a,b) and the reliability function for this distribution by using the maximum likelihood method (MLE) and Bayes methods. simulation experiments are conducts to explain the behaviour of the estimation methods for different sizes depending on the mean squared error criterion the results show that the Bayes is bet
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