Cluster analysis (clustering) is mainly concerned with dividing a number of data elements into clusters. The paper applies this method to create a gathering of symmetrical government agencies with the aim to classify them and understand how far they are close to each other in terms of administrative and financial corruption by means of five variables representing the prevalent administrative and financial corruption in the state institutions. Cluster analysis has been applied to each of these variables to understand the extent to which these agencies are close to other in each of the cases related to the administrative and financial corruption.           

In this study, the sonochemical degradation of phenol in water was investigated using two types of ultrasonic wave generators; 20 kHz ultrasonic processor and 40 kHz ultrasonic cleaner bath. Mineralization rates were determined as a function of phenol concentration, contact time, pH, power density, and type of ultrasonic generator. Results revealed that sonochemical degradation of the phenol conversion was enhanced at increased applied power densities and acidic conditions. At 10 mg/L initial concentration of phenol, pH 7, and applied power density of 3000 W/L, the maximum removal efficiency of phenol was 93% using ultrasonic processor at 2h contact time. Whereby, it was 87% using and ultrasonic cleaner bath at 16h contact time and 150 W

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

The research aims to examine the integration effect among resource consumption accounting (RCA) system and the enterprise resource planning (ERP) on both costs reduction and quality improvement. The study questioner form distributed to two different respondents as the unit of analysis. The research reached various conclusions most important of which is the integration relationship can help solve the special difficulties in managing the economic unit data. Moreover, the integration provides a clear picture of the causal relationships between resources, resource quantities, and associated costs

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual an

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

In this study, we tackle the understudied area of Artificial Intelligence (AI) and its role in examining how modern revolutions may affect political systems across the Middle Eastern region. despite hundreds of studies documenting Middle Eastern uprisings over the past three decades, there has been little effort to harness AI to better understand or predict these multifaceted events. This study seeks to address this gap by assessing the performance of AI-intelligence in analyzing (broadly) revolutionary processes and their effects on regional political systems. The research uses a mixedmethod methodology that involves a systematic literature review of contemporary scholarly articles, and an analytics study using AI tools. Our results show t

Economic organizations operate in a dynamic environment, which necessitates the use of quantitative techniques to make their decisions. Here, the role of forecasting production plans emerges. So, this study aims to the analysis of the results of applying forecasting methods to production plans for the past years, in the Diyala State Company for Electrical Industries.

The Diyala State Company for Electrical Industries was chosen as a field of research for its role in providing distinguished products as well as the development and growth of its products and quality, and because it produces many products, and the study period was limited to ten years, from 2010 to 2019. This study used the descriptive approa

Artificial intelligence (AI) offers significant benefits to biomedical research and academic writing. Nevertheless, using AI-powered writing aid tools has prompted worries about excessive dependence on these tools and their possible influence on writing proficiency. The current study aimed to explore the academic staff’s perspectives on the impact of AI on academic writing. This qualitative study incorporated in-person interviews with academic faculty members. The interviews were conducted in a semi-structured manner, using a predetermined interview guide consisting of open-ended questions. The interviews were done in person with the participants from May to November 2023. The data was analyzed using thematic analysis. Ten academics aged