Quality control charts are limited to controlling one characteristic of a production process, and it needs a large amount of data to determine control limits to control the process. Another limitation of the traditional control chart is that it doesn’t deal with the vague data environment. The fuzzy control charts work with the uncertainty that exists in the data. Also, the fuzzy control charts investigate the random variations found between the samples. In modern industries, productivity is often of different designs and a small volume that depends on the market need for demand (short-run production) implemented in the same type of machines to the production units. In such cases, it is difficult to determine the contr

In the field of civil engineering, the adoption and use of Falling Weight Deflectometers (FWDs) is seen as a response to the ever changing and technology-driven world. Specifically, FWDs refer to devices that aid in evaluating the physical properties of a pavement. This paper has assessed the concepts of data processing, storage, and analysis via FWDs. The device has been found to play an important role in enabling the operators and field practitioners to understand vertical deflection responses upon subjecting pavements to impulse loads. In turn, the resultant data and its analysis outcomes lead to the backcalculation of the state of stiffness, with initial analyses of the deflection bowl occurring in conjunction with the measured or assum

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

This research sought to present a concept of cross-sectional data models,  A crucial double data to take the impact of the change in time and obtained from the measured phenomenon of repeated observations in different time periods, Where the models of the panel  data were defined by different types of fixed , random and mixed, and Comparing them by studying and analyzing the mathematical relationship between the influence of time with a set of basic variables Which are the main axes on which the research is based and is represented by the monthly revenue of the working individual and the profits it generates, which represents the variable response And its relationship to a set of explanatory variables represented by the

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

This research aims to identify the reality of teaching political science research methods curriculum, to observe practices, and differences in teaching and learning between the Arab and Western universities. Moreover, it focuses on the difficulties that face students' acquisition of the course skills. The research uses the course model of some Western and Arab universities as case study.

This research shows that the curriculum do not reach yet the final form as other political science curriculums, and its upcoming changes will reflect the needs of stakeholders. The best method to teach this curriculum is to use applied learning in groups, learning by doing, and finally problem-based learning approach. Using optimal assessment deep