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 in a random stratified manner from students at the University of Baghdad, College of Education for Pure Sciences/Ibn Al-Haitham, Department of Computer. The proposed AI model utilized three artificial intelligence techniques: Decision Tree (DT), Random Forest (RF), and Gradient Boosting Machine (GBM). The classification accuracy using DT was 92.85 and using GMB was 95.23. The RF technique was applied to find the essential features, and the Pearson correlation was used to find the correlation between the features. The findings indicated that students indeed possess digital intelligence, underscoring the potential for tailored interventions to enhance their digital skills and competencies. This research not only sheds light on the current DI landscape among university students but also paves the way for targeted educational initiatives to foster digital literacy and proficiency in the academic setting.
Digital Intelligence for University Students Using Artificial
Intelligence Techniques
Digital Intelligence
Artificial Intelligence
University Students
DT
RF
GBM
Pearson correlation
Publication Date
Tue Mar 01 2022
Journal Name
Iraqi Journal Of Agricultural Sciences
CORRELATION OF TMPRSS2-ERG GENE FUSION STATUS WITH CLINICOPATHOLOGICAL CHARACTERISTICS IN PROSTATE CANCER OF IRAQI PATIENTS
The aim of this study was to establish the existence and interaction of TMPRSS2 – ERG gene fusion status with clinicopathological features of prostate cancer patients. This research consisted of 123 embedded formalin-fixed tissues obtained from the prostate tumor patients. The above gene fusion is detected through the technique of fluorescent in situ hybridization (FISH) by means of a triple color probe. Seven samples have not been scored due to technical difficulties and 46 patients have fusion (39.6%)
while the remaining (70) have not been seen with fusion. Of the 46 fusion-positive
17 (36%) were caused by ERG-translocation
of the other 29 (63%) were caused by the interstitial segment deletion between the two genes due to their sequential position. In order to find a substantial correlation between 116 patients (fusion took place in 46 and non-fusion in 70) with clinicopathological features
the following findings were calculated: Our findings recorded non-significant correlation at age
BMI and tumor size at which (P-value >0.05).While results showed significant association with: pathological T stage (P value = 0.011)
Gleason score (P value = 0.002)
perineural invasion (P value = 0.047) and PSA (P value = 0.033). The clinicopathological features which had a considerable correlation with fusion status are very important as they aid the doctors in prognosis and treatment of such tumor.