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 dise

تهدف هذه الدراسة إلى محاولة التعرف على اثر كل من عرض النقود وسعر الصرف على معدل التضخم في الاقتصاد الليبي خلال الفترة 1990-2008. ولتحقيق ذلك فقد تم اختيار الرقم القياسي لأسعار المستهلك ليمثل معدل التضخم، وعرض النقود بالمفهوم الواسع  ممثلا لعرض النقود، وسعر صرف الدينار الليبي مقابل الدولار الأمريكي ممثلا لسعر الصرف وقد أخضعت المتغيرات لاختبار السكون والذي تشير نتائجه إلى أن التضخم وعرض النقود وسعر الصرف

سجلت الدولة الجزائرية محاولتين من أجل التنمية، بهما أرادت القضاء على كل مخلفات الاستعمار الفرنسي، من فقر وجهل وأمراض وحرمان من أبسط ضرورات الحياة، كما عملت جاهدة على أن تكون دولة لها مكانة في صفوف الدول الصاعدة على الأقل، لما حباها الله من موارد مادية ومالية وبشرية، وموقع جيو استراتيجي، قلت مثيلة في العالم، بالنسبة لمساحتها الجغرافية الكبيرة وتنوع تضاريسها الطبيعية، وترتيبها المتقدم في الطا

The investment environment is the incubator for all types of domestic and foreign investments, so if their determinants are encouraging, they increase the levels of investment flows and vice versa, as there is a relationship between the nature of the investment environment and the level of investment flows, and the determinants of the investment environment are numerous and the most important of which are security and political stability, and economic and financial factors that include relative stability In the exchange rate and inflation rates, the availability of banks and their development, transparency and integrity in administrative dealings and the lack of prevalence of administrative and financial corruption, and the clari

In a resource-limited world, there is an urgent need to develop new economic models, from the traditional unsustainable industrial model of product consumption and disposal, to a new model based on the concepts of sustainability in its comprehensive sense, the so-called circular economy, using fewer resources in manufacturing processes and changing practices in product disposal to waste, by removing its use, recycling and manufacturing to start another manufacturing process. In an era of intense competition in domestic and global markets, the importance of the circular economy is highlighted in its ability to strengthen the competitiveness of enterprises in those markets, by reducing the cost and increasing the quality of the pro

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

The emergence of oil fields and subsequent changes in adjacent land use are known to affect settlements and communities. Everywhere the industry emerges, there is little understanding about the impact of oil fields on land use in the surrounding areas. The oil industry in Iraq is one of the most important industries and is almost the main industry in the Iraqi economic sector, and it is very clear that this industry is spread over large areas, and at the same time adjoins with population communities linked to it developmentally.

The rapid development and expansion of oil extraction activities in various regions has led to many challenges related to land-use planning and management. Here, the problem of research  arises on th

Erbil city is located in the northern Iraq with a population of over one million people. Due to water crises farmers usually use wastewater and well water for the agricultural production. In this study six stations were designed to sample waste water and three from well water to define waste water and ground water characteristics. In this study, Residual Na⁺ Carbonate, Mg⁺⁺ hazard, salinity hazard, Kelley index, %sodium, total hardness, permeability index, potential salinity, sodium adsorption ratio, and Irrigation Water Quality Index (IWQI) were determined. The order of average cation concentrations in water was Mg²⁺> Ca²⁺ > Na⁺ > K⁺. While the proportion of main

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D