Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Learner centered design approach for E-learning using 3D virtual tutors(IEEE Computer Society help@computer.org, 2013) Mukherjee, S.; Singhal, H.; Jha, P.; Kokane, A.; Rastogi, P.; Mittal, R.; Guddeti, G.Most of the existing E-learning system designs have focused on the development of feature-rich, but usable systems with little effort in motivating students to develop interest in the teaching-learning process. This paper discusses the learner centered design approach for web-based tutoring to motivate young learners using 3D virtual tutors in a requirement-based, flexible pedagogical model. Students can choose course(s) and the study-mode. In the guided mode, the student is mentored by a human tutor, whereas a student in un-guided mode is tutored by 3D avatar. The student has access to study materials, educational videos and applets that are provided by the tutors, the student also has access to forums for doubt clearing and online assignments to be submitted for tutors' evaluation. Tutors can track students' progress using online quiz and reports modules. Further, tutors have access to teaching aids like online chat system and whiteboard-based teaching in a virtual classroom environment. © 2013 IEEE.Item CNN Based Tropical Cyclone Intensity Estimation Using Satellite Images Around Indian Subcontinent(Springer Science and Business Media Deutschland GmbH, 2024) Jha, P.; Sumam David, S.; Vijayasenan, D.In this work, we have used deep learning models for estimating tropical cyclone (TC) intensity using satellite images. This is an image to regression problem, where an image is given as input and intensity value is estimated as output. In the literature, various deep learning methods have been proposed for TC intensity estimation but their focus on cyclones around the Indian subcontinent is limited. We have implemented three models: regression model, classification model, and a multitask model having regression and classification output as two tasks. We have worked with two sets of input data. One set of data contains single channel input containing infrared (IR) brightness temperature satellite image. Another set of data contains two channel inputs having infrared (IR) brightness temperature satellite image as one of the channels, and rain rate derived from passive microwave (PMW) satellite image as another channel. We have used satellite images for cyclones occurring in the Atlantic, Northeast Pacific, and North Central Pacific regions from 2006 through 2016. For cyclones around the Indian subcontinent, we have used satellite images from 2005- 2016. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.