International Journal of Information Technology, Communications and Convergence
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International Journal of Information Technology, Communications and Convergence (2 papers in press)
School Dropout Profiling and Prediction Approach using Machine Learning by Khamisi Kalegele Abstract: Tens of thousands of children drop out of schools in Sub-sahara Africa where the widely adopted interventions are either reactive in nature or uninformed. The increasing availability of disaggregated data has opened new prospects for proactive interventions using new data technologies in unprecedented ways. However, awareness and skills among wider interest groups including school managers, researchers and developers are at staggering low levels. This paper demonstrates how a machine learning approach can be used to profile students and predict the likelihood of dropping out of school in order to enable proactive interventions and potentially inform youth related policies. Using well known open dataset, supervised and unsupervised profiling approaches are demonstrated, compared and proved to be better performers than a traditional approach. The approaches can be replicated under real production environment using actual data to enable informed interventions and reduce dropouts. Keywords: School dropout; Profiling; Prediction;Truancy; Youth empowerment; Machine Learning; Classification; Data-driven; Absenteeism; Africa.
Special Issue on: ICCDC 2019 Convergence of Information and Communication Technologies for Next-Generation Networking and Applications
Intelligent Remote Health Monitoring System by Samik Basu, Mahasweta Ghosh, Soma Barman (Mandal) Abstract: With more than 45% of WHO member states failing to achieve the WHO standard of 1:1000 doctor to patient ratio; under current scenario intelligent and remote self-health monitoring becomes too vital for regular health monitoring of people. Rural people have even less doctor to patient ratio than urban people and so cost-effective intelligent vital sign monitoring system becomes even more essential. In our proposed system, we have used sensors to monitor vital health parameters like heart rate, SpO2 and body temperature and upload this information to the cloud server, using IoT platform, for remote access to the doctors. In case of non-availability of internet services, the proposed system is capable of predicting the cardiac health condition of the person independently by using Machine Learning algorithm. Email and health indicator unit alert the person at the remote-end and system-end respectively in case of emergencies. Keywords: Embedded Processing; Raspberry Pi 3B+; Health Monitoring System; Remote Access; Machine Learning; Support Vector Machine; Confusion Matrix; IoT; ThingSpeak Cloud; Green Energy.