The research problem arose from the researchers’ sense of the importance of Digital Intelligence (DI), as it is a basic requirement to help students engage in the digital world and be disciplined in using technology and digital techniques, as students’ ideas are sufficiently susceptible to influence at this stage in light of modern technology. The research aims to determine the level of DI among university students using Artificial Intelligence (AI) techniques. To verify this, the researchers built a measure of DI. The measure in its final form consisted of (24) items distributed among (8) main skills, and the validity and reliability of the tool were confirmed. It was applied to a sample of 139 male and female students who were chosen

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Soil fertility is a crucial factor in measuring soil quality, it indicates the extent to which soil can support plant life. Soil fertility is measured by the amount of macro and micronutrients, pH, etc. Soil nutrients are depleted after each harvest and therefore must be added. To maintain soil nutrient levels, fertilizer is added to the soil. Adding fertilizer in the precise amount is a matter of great importance because excess or insufficient application can harm plant life and reduce productivity. The use of modern technology is a solution to this problem. Although automated techniques for sowing, weeding, crop harvesting, etc. have been proposed and implemented, none of the techniques are aimed to maintaining soil fertility. The study a

The aim: to evaluate combined microscopy techniques for determining the morphological and optical properties of methadone hydrochloride (MDN) crystals. Materials and methods: MDN crystal formation was optimized using a closed container method and crystals were characterized using polarized light microscope (PLM), scanning electron microscopy (SEM) and confocal microscopy (CM). SEM and CM were used to determine MDN crystal thickness and study its relationship with crystal retardation colours using the Michel-Levy Birefringence approach. Results: Dimensions (mean±SD) of diamond shaped MDN crystals were confirmed using SEM and CM. Crystals were 46.4±15.2 Vs 32.0±8.3 µm long, 28.03±8.2 Vs 20.85±5.5 µm wide, and 6.62±

Background: This study was designed to measure the displacement pattern of posterior palatal seal (pps) area in different forms of the palate and with different impression techniques. Materials and method: This study was used to measure the displacement pattern of (pps)in different palatal shapes by using different impression materials Korrecta wax No.4,Green compound and design of House for pps for each palatal forms by using a 3D Scanner of CAD/CAM and measuring the distance between 2 points in pps area by using Caural Threw. Result: The results show highly significant differences between these techniques and the control group (impression with light body) Conclusion: The physiological impression technique of pps with Korecta wax no.4

KE Sharquie, AA Noaimi, HA Al-Mudaris, Journal of Cosmetics, Dermatological Sciences and Applications, 2013 - Cited by 4

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