國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Improving Sensor Network Predictions...

Akter, Syeda Selina.

Improving Sensor Network Predictions Through The Identification of Graphical Features.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Sensor Network Predictions Through The Identification of Graphical Features.
作者:	Akter, Syeda Selina.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:	122 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
附註:	Advisor: Holder, Lawrence B.
Contained By:	Dissertations Abstracts International81-02B.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13856951
ISBN:	9781085612241

Improving Sensor Network Predictions Through The Identification of Graphical Features.
Akter, Syeda Selina.

Improving Sensor Network Predictions Through The Identification of Graphical Features. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 122 p.

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

Thesis (Ph.D.)--Washington State University, 2019.

This item must not be sold to any third party vendors.

We propose a framework that represents sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features to be used by a classifier for prediction tasks in a particular sensor network. The purpose of this graph-based framework is to provide a generic tool for sensor network application builders and practitioners to improve prediction task performance in general through the use of inherent graph structure that exists in sensor networks and through the use of generic graph-based features. We apply our graphical feature based approach to three different kinds of sensor network applications with different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. For smart home activity recognition, our graphical feature-based approach using Support Vector Machine outperformed three widely used methods, Naive Bayes, Hidden Markov Model and Conditional Random Fields and other previous graph-based approaches on three different datasets from three smart apartments. For demographic prediction from smart phone sensors, we evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. In activity recognition using smart phone sensors, we find that adding graph-based features using GPS to basic smart phone sensor data improves activity recognition accuracy compared to using only basic non-graphical features with existence of nodes performing the best. Adding selected edges as features reduced error for some activities. We can conclude that the graphical feature-based framework based on sensor categorization, node and edges as features, and feature selection techniques provides promising results compared to non-graph-based features.

ISBN: 9781085612241Subjects--Topical Terms:

199325
Computer science.

Improving Sensor Network Predictions Through The Identification of Graphical Features.
LDR:03255nmm a2200337 4500 001 570741
005 20200514111949.5
008 200901s2019 ||||||||||||||||| ||eng d
020 $a 9781085612241
035 $a (MiAaPQ)AAI13856951
035 $a AAI13856951
040 $a MiAaPQ $c MiAaPQ
100 1 $a Akter, Syeda Selina. $3 857415
245 1 0 $a Improving Sensor Network Predictions Through The Identification of Graphical Features.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 122 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
500 $a Advisor: Holder, Lawrence B.
502 $a Thesis (Ph.D.)--Washington State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a We propose a framework that represents sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features to be used by a classifier for prediction tasks in a particular sensor network. The purpose of this graph-based framework is to provide a generic tool for sensor network application builders and practitioners to improve prediction task performance in general through the use of inherent graph structure that exists in sensor networks and through the use of generic graph-based features. We apply our graphical feature based approach to three different kinds of sensor network applications with different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. For smart home activity recognition, our graphical feature-based approach using Support Vector Machine outperformed three widely used methods, Naive Bayes, Hidden Markov Model and Conditional Random Fields and other previous graph-based approaches on three different datasets from three smart apartments. For demographic prediction from smart phone sensors, we evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. In activity recognition using smart phone sensors, we find that adding graph-based features using GPS to basic smart phone sensor data improves activity recognition accuracy compared to using only basic non-graphical features with existence of nodes performing the best. Adding selected edges as features reduced error for some activities. We can conclude that the graphical feature-based framework based on sensor categorization, node and edges as features, and feature selection techniques provides promising results compared to non-graph-based features.
590 $a School code: 0251.
650 4 $a Computer science. $3 199325
650 4 $a Remote sensing. $3 195948
650 4 $a Artificial intelligence. $3 194058
650 4 $a Information technology. $3 184390
650 4 $a Electrical engineering. $3 454503
690 $a 0984
690 $a 0489
690 $a 0544
690 $a 0800
690 $a 0799
710 2 $a Washington State University. $b Computer Science. $3 826862
773 0 $t Dissertations Abstracts International $g 81-02B.
790 $a 0251
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13856951