Deep autonomous learning machine for IoT streaming analytics

With the technological advancement in Internet of Things (IoT), it has been employed in multiple areas such as healthcare, manufacturing, home automations. The wide applications of IoT accelerate the rate of data generation, resulting in an explosion of data. Furthermore, IoT devices generate and tr...

Full description

Saved in:
Bibliographic Details
Main Author: Li, Jinquan
Other Authors: Mahardhika Pratama
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138224
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-138224
record_format dspace
spelling sg-ntu-dr.10356-1382242020-04-29T05:29:59Z Deep autonomous learning machine for IoT streaming analytics Li, Jinquan Mahardhika Pratama School of Computer Science and Engineering STMicroelectronics mpratama@ntu.edu.sg Engineering::Computer science and engineering With the technological advancement in Internet of Things (IoT), it has been employed in multiple areas such as healthcare, manufacturing, home automations. The wide applications of IoT accelerate the rate of data generation, resulting in an explosion of data. Furthermore, IoT devices generate and transmit data in streams which lead to increase interest in real-time data stream classification. Consequently, traditional algorithms are inefficient to cope with the large volumes of data stream. In various IoT applications, the use of recurrent neural network (RNN) is desirable due to the sequential nature of the data stream. This allows an RNN-based classifier to handle sequential data and to retain temporal information. However, with the large volume of data stream, traditional RNN-based classifiers are offline in nature and impractical in the streaming context. ADL and NADINE are therefore, introduced to address data stream problems in the continual fashion. The ADL and NADINE are compared with traditional RNN-based classifiers to demonstrates their performance in data stream classification. Bachelor of Engineering (Computer Science) 2020-04-29T05:29:59Z 2020-04-29T05:29:59Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138224 en SCSE19-0079 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Li, Jinquan
Deep autonomous learning machine for IoT streaming analytics
description With the technological advancement in Internet of Things (IoT), it has been employed in multiple areas such as healthcare, manufacturing, home automations. The wide applications of IoT accelerate the rate of data generation, resulting in an explosion of data. Furthermore, IoT devices generate and transmit data in streams which lead to increase interest in real-time data stream classification. Consequently, traditional algorithms are inefficient to cope with the large volumes of data stream. In various IoT applications, the use of recurrent neural network (RNN) is desirable due to the sequential nature of the data stream. This allows an RNN-based classifier to handle sequential data and to retain temporal information. However, with the large volume of data stream, traditional RNN-based classifiers are offline in nature and impractical in the streaming context. ADL and NADINE are therefore, introduced to address data stream problems in the continual fashion. The ADL and NADINE are compared with traditional RNN-based classifiers to demonstrates their performance in data stream classification.
author2 Mahardhika Pratama
author_facet Mahardhika Pratama
Li, Jinquan
format Final Year Project
author Li, Jinquan
author_sort Li, Jinquan
title Deep autonomous learning machine for IoT streaming analytics
title_short Deep autonomous learning machine for IoT streaming analytics
title_full Deep autonomous learning machine for IoT streaming analytics
title_fullStr Deep autonomous learning machine for IoT streaming analytics
title_full_unstemmed Deep autonomous learning machine for IoT streaming analytics
title_sort deep autonomous learning machine for iot streaming analytics
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138224
_version_ 1681056595479363584