The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tender phase of

The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tend

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak

Abstract: Iatrogenic furcal root perforations are serious complications during dental treatment. This study was aimed to compare the sealing ability of new bioceramic root repair material TotalFill® with the other perforation repair materials (GIC, MTA and Biodentine) using a dye- extraction method.Materials and Methods: Forty extracted, human mandibular molars with non-fused well developed root were collected. Artificial perforations were made from the external surface of the teeth. Then the teeth were randomly divided into 4 experimental groups (n= 10) according to the type of repair material used in this study; Medifil glass ionomercement, TotalFill® bioceramic root repair material, BiodentineTM and MTA Plus. The specimens were then im

This study analyses six political cartoons selected based on their relevance to current Iraqi political issues, specifically the period between 2005 and 2015, from American online newspapers (calgecartooms.com). The selection criteria included the cartoons' satirical elements, visual rhetoric, and their ability to engage with themes such as power dynamics, social issues, and public opinion. It sheds light on how these cartoons can function as mediators of meanings between the cartoonists and the readers. The data is examined using multimodal discourse analysis (MDA), which combines language study with the analysis of other visual elements, like colors, gestures, and images, to understand meaning (O’Halloran et al., 2011). The Visual Socia

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att