The objective of this paper is to introduce a cognitive learning methodology for the course Software Project Management. The industry realizes every year the need to train its managers in software project management and invests in the training costs though it a known fact that almost all the managers have undergone a course on Software Project Management either at their graduate level or post graduate level. On the other hand the teachers who teach software project management in class find it extremely challenging in sustaining the interest of the students all through the coursework. One obvious reason for this is the lack of real time exposure to live environments in which the industry operates and of course lack of experience of students in the area of management. In an attempt to bridge this gap at least to a certain extent an activity driven teaching model has been proposed in this paper. This paper is aimed at stressing the importance of exposing the students to live case studies and thereby encouraging them in resolving some of the issues posed by the case study. The solutions can be discussed with the students thereof which would lead them realizing the standard practices explained theoretically in the prescribed text book. In this paper one such activity conducted to the students and their responses and how those responses could be traced back to the concepts explained in the text book has been illustrated. An activity driven teaching model for SPM not only sustains the interest of the students with active interaction from them but also reduces the burden on the heavy training costs incurred by the industry in training the future managers.
Diabetic Retinopathy is eye condition caused by high sugar levels inside the blood, which is the origin of excessive pressure inside blood vessels inside the eye, with the smallest vessels being the most vulnerable. This condition does not appear suddenly; rather, it develops gradually over time. After the disease progress, it can show symptoms like blurry vision, changes in vision from blurry to clear, and vice versa, blackspots or dark areas in the vision, poor night vision, fading out of colours, etc. Therefore, pre-emptive identification of disease is one of the beneficial tactics to prevent or get cured of this disease. This technique is also susceptible to human misjudgement, which exists in many clinical diagnoses. An Image Classification Model can accelerate the process of blindness detection in patients. We accomplish this by constructing a classifier using transfer learning that can extract key features from pictures and categorise them into separate stages. This work focused on making an efficient classifier with high accuracy and providing the patient with advance notice of their disease using an easy-to-use mobile application. Our model gave a 0.907 quadratic weighted kappa (QWK) score on independent test dataset and 93.2% accuracy on test time augmented data in multi-class classification. Furthermore, providing the necessary use cases with which the patient can track the diabetic retinopathy screening diagnosis
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