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Centre for Digital Transformation (CDT) and IEEE NSW Computer Society Joint Research Seminar
October 24, 2019 @ 1:00 pm - 2:00 pm
Topic 1: Towards a General Prediction System for the Primary Delay in Urban Railways
Presenter: Mr Jianqing Wu
Abstract: Nowadays, a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing ‘big data’ methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System framework, which combines General Transit Feed Specification, Critical Point Search, and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open-source data collection and processing tool that reduces the barrier to the use of the different open data sources. Finally, we demonstrate an advanced deep learning model, the novel ConvLSTM Encoder-Decoder model with Critical Point Search for better primary delay predictions.
Topic 2: Context Oriented m-Learning: A m-Health Model for on-time Interventive Type-2 Diabetic Patient Education in Nepal using Blockchain Technologies
Presenter: Mr Mahesh Kayastha
Abstract: Researches on the prevalence of Type-2 diabetes in Nepal indicate needs for further research on delivering diabetic education effectively in the country. This proposed research aims to build an m-health model and a mobile app to deliver push notification based educational content to address this need. It will investigate the mechanism for the push notification-based m-learning model to support education of these patients. In order to address another challenge that faces policymakers in Nepal in collecting trustworthy diabetic patient data for policy and research purposes, the proposed m-Health application will be built on Blockchain technologies to store tamperproof blood sugar data. Both quantitative and qualitative research will be undertaken to investigate how the proposed model can contribute new knowledge in m-health for chronic disease management