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Personnel Triangulation Using Adaptive Machine Learning

Adaptive Machine Learning is a branch of neural network which develop a system to mimic the human neural system into the machines or can be called as Artificial Intelligence. Currently, AML is vastly developing and implemented into the machines such as mobile phones, computers, games and so on. As f...

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Main Author: HAIRUZAMAN, MOHAMMAD HAFI
Format: Final Year Project
Language:English
Published: IRC 2019
Online Access:http://utpedia.utp.edu.my/20176/1/Final%20Dissertation.pdf
http://utpedia.utp.edu.my/20176/
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spelling my-utp-utpedia.201762019-12-20T16:13:31Z http://utpedia.utp.edu.my/20176/ Personnel Triangulation Using Adaptive Machine Learning HAIRUZAMAN, MOHAMMAD HAFI Adaptive Machine Learning is a branch of neural network which develop a system to mimic the human neural system into the machines or can be called as Artificial Intelligence. Currently, AML is vastly developing and implemented into the machines such as mobile phones, computers, games and so on. As for the personnel triangulation, it is a system used to detect human movements either inside or outside a building. Personnel triangulation consists of two classes which is OPS and IPS. OPS stand for Outdoor Positioning System while IPS is Indoor Positioning System. OPS use GPS or GNSS, from multiple satellites to track down the devices that have the GPS capability, but it is mostly unusable inside a building because of a few causes. But for IPS, there is yet a system used to accurately track the exact tracking device positions. So, by applying AML into the personnel triangulation system, an accurate system might be created to track personnel inside a building and improving the tracking system especially for IPS tracking classes. IRC 2019-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20176/1/Final%20Dissertation.pdf HAIRUZAMAN, MOHAMMAD HAFI (2019) Personnel Triangulation Using Adaptive Machine Learning. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
description Adaptive Machine Learning is a branch of neural network which develop a system to mimic the human neural system into the machines or can be called as Artificial Intelligence. Currently, AML is vastly developing and implemented into the machines such as mobile phones, computers, games and so on. As for the personnel triangulation, it is a system used to detect human movements either inside or outside a building. Personnel triangulation consists of two classes which is OPS and IPS. OPS stand for Outdoor Positioning System while IPS is Indoor Positioning System. OPS use GPS or GNSS, from multiple satellites to track down the devices that have the GPS capability, but it is mostly unusable inside a building because of a few causes. But for IPS, there is yet a system used to accurately track the exact tracking device positions. So, by applying AML into the personnel triangulation system, an accurate system might be created to track personnel inside a building and improving the tracking system especially for IPS tracking classes.
format Final Year Project
author HAIRUZAMAN, MOHAMMAD HAFI
spellingShingle HAIRUZAMAN, MOHAMMAD HAFI
Personnel Triangulation Using Adaptive Machine Learning
author_facet HAIRUZAMAN, MOHAMMAD HAFI
author_sort HAIRUZAMAN, MOHAMMAD HAFI
title Personnel Triangulation Using Adaptive Machine Learning
title_short Personnel Triangulation Using Adaptive Machine Learning
title_full Personnel Triangulation Using Adaptive Machine Learning
title_fullStr Personnel Triangulation Using Adaptive Machine Learning
title_full_unstemmed Personnel Triangulation Using Adaptive Machine Learning
title_sort personnel triangulation using adaptive machine learning
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20176/1/Final%20Dissertation.pdf
http://utpedia.utp.edu.my/20176/
_version_ 1739832722376884224
score 13.211869

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