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BRB based Deep Learning Approach with Application in Sensor Data Streams
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. (Erasmus Master PERCCOM BRB Research Group)
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

Predicting events based on available data is an effective way to protect human lives. Issuing health alert based on prediction of environmental pollution, executing timely evacuation of people from vulnerable areas based on prediction of natural disasters are the application areas of sensor data stream where accurate and timely prediction is crucial to safeguard people and assets. Thus, prediction accuracy plays a significant role to take precautionary measures and minimize the extent of damage. Belief rule-based Expert System (BRBES) is a rule-driven approach to perform accurate prediction based on knowledge base and inference engine. It outperforms other such knowledge-driven approaches, such as, fuzzy logic, Bayesian probability theory in terms of dealing with uncertainties. On the other hand, Deep Learning is a data-driven approach which belongs to Artificial Intelligence (AI) domain. Deep Learning discovers hidden data pattern by performing analytics on huge amount of data. Thus, Deep Learning is also an effective way to predict events based on available data, such as, historical data and sensor data streams. Integration of Deep Learning with BRBES can improve prediction accuracy further as one can address the inefficiency of the other to bring down error gap. We have taken air pollution prediction as the application area of our proposed integrated approach. Our combined approach has shown higher accuracy than relying only on BRBES and only on Deep Learning.

Place, publisher, year, edition, pages
2019. , p. 107
Keywords [en]
BRBES, Deep Learning, integration, sensor data, predict
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-75974OAI: oai:DiVA.org:ltu-75974DiVA, id: diva2:1350970
External cooperation
Department of Computer Science and Engineering, University of Chittagong, Bangladesh
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level (120 credits)
Supervisors
Examiners
Note

This is a Master Thesis Report as part of degree requirement of Erasmus Mundus Joint Master Degree (EMJMD) in Pervasive Computing and Communications for Sustainable Development (PERCCOM).

Available from: 2019-09-17 Created: 2019-09-12 Last updated: 2019-09-17Bibliographically approved

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