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Abhishek, Appaji M.
- Impact of Novel Enhanced Learning on Results for Courses on Control Systems and Biostatistics in Medical Electronics Programme
Authors
1 Department of Medical Electronics, B M S College of Engineering, Bangalore, IN
Source
Journal of Engineering Education Transformations, Vol 28, No Spl Iss (2015), Pagination: 343-347Abstract
Enhanced learning is an important effective pedagogical tool which helps students to understand the concepts better. Assessment Tool is a concept to be used as against the mundane assessment method. This paper gives an experience report of the first author in implementation of two important courses taken by the students of department of Medical Electronics in two different semesters. The courses under consideration are with no laboratory attachment and so the students fall short of experiencing the outcomes for different concepts of the subject. An assessment tool was therefore introduced in the form of usage of a software tool to implement the concepts learnt in theory. The novel assessment tool usage introduced was assessed to quantify the effect of practical exposure. The improvement thus seen had a normal distribution in the scores obtained by students and looked ideal for the range of student group which usually had a skewed pattern.Keywords
Assessment Tool, Biostatistics, Control Systems, Teaching Beyond Syllabus.
- Semi Quantitative Analysis and Classification of Alzheimer Disease using Positron Emission Tomography Images
Authors
1 Department of Medical Electronics, B M S College of Engineering, Bangalore, Karnataka-560019, IN
2 Department of Medical Electronics, B M S College of Engineering, Bangalore, Karnataka-560019, IN
Source
Digital Image Processing, Vol 4, No 14 (2012), Pagination: 819-824Abstract
Alzheimer’s disease is an irreversible, degenerative neurological brain disease. It is marked by the buildup of plaque and tangles in the neurons of Alzheimer’s patients. A person with Alzheimer’s disease will have memory loss and mental functions are inhibited. Though there is no clear picture, but experts believe as many as 5.1 million Americans are currently suffering from Alzheimer’s (Alzheimer’s Fact Sheet) and proportionally there are Alzheimic patients. Although there is no cure for the disease, early detection of Alzheimer’s is crucial because it allows the patient to immediately begin a drug regimen that decrease the pace of the process of the disease. Biomarker tools for early diagnosis and disease progression in Alzheimer’s disease (AD) remain key issues in AD diagnosis .Efficient methods to identify early AD and to monitor the treatment effects in mild, moderate AD patients could revolutionize current trial practice. In modern medicine, PET imaging using fluorodeoxyglucose (FDG) is the most effective method of diagnosing AD. Brain changes in severe AD diseases are hypometabolism in frontal and temporal lobes of the brain, extreme shrinkage of cerebral cortex, severely enlarged ventricles. In this paper, we have proposed a novel methodology to do early detection using feature selection and classification algorithm that will address the above clinical problem.