Perceptions of students about the educational environment in Gomal Medical College Dera Ismail Khan

Introduction: Student satisfaction, academic performance, and learning effectiveness have all been related to the educational environmental perceptions in medical institutes. Students' opinions of their educational environment might help medical schools discover impediments and opportunities to improve student learning. Objective: To evaluate student perception related to current traditional and integrated learning methodologies in the Gomal Medical College Dera Ismail Khan. Methods:  The Dundee Ready Education Environment Measure (DREEM) questionnaire was provided to students of Gomal Medical College Dera Ismail Khan after approval from the institute. One hundred and sixty-one students out of 190 returned the questionnaire. The data was analyzed with SPSS 27.0. All questions were evaluated on a 5-point Likert scale. Results: The mean age of the students was 21.34±1.73 years. There were 89 (55.34%) males and 72 (42.66%) female students. The mean DREEM score among the students was 116.15±3.73. The highest score among the students was 153 and the lowest score was 42. The scores were higher among males than females. Conclusion:  The students were more positive and self-assured. Students had a more positive attitude toward teachers, the learning environment, and the atmosphere. However, they were lacking in social perceptionrequiring attention. KEYWORDS: Dundee Ready Educational Environment Measure, educational environment, medical institutes, perception

Software Defect Prediction Via Machine Learning Classifiers

To improve software reliability, software should be developed without defects. SDP models can be employed to identify defective code sections at initial stage during the software development. When defects are ascertained early, it helps the practitioners to prioritize the efforts for testing and allocating more resources to defective modules. This results in improved software quality, reliability, and efficiency. Despite the fact that the numbers of approaches have been used in the past for SDP but most of them are not practically applicable. Manual feature selection is mostly performed by majority of feature selection methods. Core aim of this research is to propose iterative feature selection technique using Boruta (random forest) for SDP model. This research proposes Two?step preprocessing using SMOTE and BORUTA. Support Vector Machine (SVM), Neural Networks and XGboostclassifiers are used by MLC. Furthermore to confirm the accuracy, performance and capability of each classifier on PROMISE dataset evaluationmeasures AUC,recall, F1-measure, andaccuracy are used