HS Saeed, SS Abdul-Jabbar, SG Mohammed, EA Abed, HS Ibrahem, Solid State Technology, 2020

The housing sector in Iraq has suffered from multiple problems, the most prominent of which is the failure of the housing market to achieve housing production that faces population increases and a high number of households.

The research aims to foresight the future of the housing sector,  and to identify the expectations of experts in the development of the housing sector and housing production, in order to overcome the obstacles and problems.

Foresight does not mean forecasting about future events, but rather it is a process and skill aimed at familiarity with providing sufficient knowledge to achieve the desired future goals, Accordingly, adopting a method of future foresight will reduce error,  surprise and s

A simple, environmentally benign, cost-effective, and sensitive colorimetric determination for the pharmaceutical drug dexamethasone sodium phosphate (DXP) has been developed by the formation of a colored complex with fluoranil. The process was sensitive and linear over the range 1 to 40 μg/mL, excellent correlation coefficient 0.9989, recovery% 99.80 ± 1.3, limit of detection (LOD) and limit of quantification (LOQ) were 0.23 and 0.9 μg/mL, respectively, and good RSD ~1.63%. The experimental conditions were optimized after an intensive study. The approach was validated statistically for the quantification of the analyte in its pure and/or pharmaceutical form. Despite the proposed approach is selective, it still can be applied for

The research aims to show the relationship between the use of automated accounting systems technology and its impact on enhancing the efficiency and effectiveness of the internal control system in a sample of Bahraini universities in light of the rapid changes in the                                       electronic business environment. Automated accounting and its impact on enhancing the efficiency and effectiveness of the internal control system, and it is concluded through the analytical study of the research sample that there is a percenta

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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