This paper investigates the possibility of rapid and accurate automated diagnosis of red blood cell disorders and describes a method to detect malarial parasites and thalassaemia in blood sample images acquired from light microscopes. As malaria and thalassaemia are life threatening diseases and an enormous global health problem, rapid and precise differentiation is necessary in clinical settings. The analysis of blood is a powerful diagnostic tool for the detection of these diseases. Visual inspection of microscopic images is the most widely used technique for determination of malaria and possible thalassaemia and it is a labour-intensive repetitive and time consuming task. Two back propagation Artificial Neural Network models (3 layers and 4 layers) was employed together with image analysis techniques to evaluate the accuracy of the classification in the recognition of medical image patterns associated with morphological features of erythrocytes in the blood. The three layers Artificial Neural Network (ANN) architecture had the best performance with an error of 2.74545e-005 and 86.54% correct recognition rate. The trained three layer ANN acts as a final detection classifier to determine diseases. A medical consultation system has been jointly used with this system to provide clinical decision making ability. A questioning and answering dialog on the basis of patient history, physical examination and routine diagnostic test has been conducted in the medical consultation system with image analyzing result made by the trained ANN

During the Covid-19 pandemic, universities adopted several learning modes to ensure the contention of the education activities. They adopted many methods of learning such as online once (online real-time with no recorded lecture; face-to-screen instead of face-to-face), online repeatable (online real-time with recorded lecture), Self (pre-recorded lecture only), and blended (pre-recorded lecture with face-to-face learning). Since all these methods are adopted without any evidence of their acceptance by the students, the rouse of this study is to evaluate each of these modes and identify the most preferred mode of learn-ing. Also, this study tested the new method of blended learning preferred by the students. A questionnaire was shared among the students in two faculties in the University of Kelaniya Sri Lanka, and 903 were responded. Accordingly, this study found that the majority of the students preferred online real-time lectures together with the recorded lectures. This method was again tested with a selected student group and confirmed. Thus, this study recommends face-to screen lectures together with the recorded lesson is the most appropriate method to adopt during the new normal context.

Abstract-Source code plagiarism is a severe problem in academia. In academia programming assignments are used to evaluate students in programming courses. Therefore checking programming assignments for plagiarism is essential. If a course consists of a large number of students, it is impractical to check each assignment by a human inspector. Therefore it is essential to have automated tools in order to assist detection of plagiarism in programming assignments.Majority of the current source code plagiarism detection tools are based on structured methods. Structural properties of a plagiarized program and the original program differ significantly. Therefore it is hard to detect plagiarized programs when plagiarism level is 4 or above by using tools which are based on structural methods. This paper presents a new plagiarism detection method, which is based on machine learning techniques. We have trained and tested three machine learning algorithms for detecting source code plagiarism. Furthermore, we have utilized a meta-learning algorithm in order to improve the accuracy of our system.

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