語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Accurate, Energy-Efficient, and Secu...
~
Akmandor, Ayten Ozge.
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
作者:
Akmandor, Ayten Ozge.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, 2020
面頁冊數:
230 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
附註:
Advisor: Jha, Niraj K.
Contained By:
Dissertations Abstracts International82-01B.
標題:
Artificial intelligence.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27961840
ISBN:
9798662418011
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
Akmandor, Ayten Ozge.
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 230 p.
Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Thesis (Ph.D.)--Princeton University, 2020.
This item must not be sold to any third party vendors.
Machine learning (ML) algorithms automate the data-label mapping and decision-making processes. The output of the ML algorithms determines the next step in real-world applications. If the ML output is diagnosis of a disease, e.g., in smart healthcare applications, then the next step is the treatment options. On the other hand, if the ML output is the traffic condition, e.g., in smart city applications, then the next step is traffic regulation. It is desirable to have ML-based systems that are simultaneously accurate, secure, energy-efficient, low-cost, silently operable, maintainable, customizable, low-delay, and scalable. However, there are tradeoffs among these design goals that impact the complexity of the overall system. This thesis focuses on the accuracy, security, and energy-efficiency objectives.We first target the accuracy objective with a new dual-space (feature and semantic space) classification approach: SECRET. While traditional supervised learning approaches operate in the feature space only, SECRET utilizes both feature and semantic spaces in the classification process. It incorporates class affinity and dissimilarity information into the decision process using the semantic space. This property enables SECRET to make informed decisions on class labels, thus enhancing its overall classification performance. By demonstrating the significant benefits of the semantic space, SECRET opens up a new research direction for the machine learning community.Next, we introduce an automatic stress detection and alleviation system: SoDA. SoDA takes advantage of emerging wearable medical sensors to continuously monitor human stress levels and mitigate stress as it arises. It performs stress detection and alleviation in a user-transparent manner. When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user.Furthermore, we present machine learning models that are built with smartphone and wearable medical sensor data and enable the smartphone to understand us. We call this system your smartphone understands you (YSUY). By understanding our physical, mental, and emotional states, YSUY promotes quality of life of the users by assessing their states and shining light on fundamental human-centric needs.Finally, we introduce a simultaneously smart, secure, and energy-efficient Internet-of-Things (IoT) sensor architecture: SSE. In SSE, we use inference in the compressively-sensed domain and transmit data to the base station when an event of interest occurs. Since on-sensor compression and inference drastically reduce the amount of data that need to be transmitted, we are able to reduce the IoT sensor energy by two-to-three orders of magnitude. A small part of this energy bonus is used to carry out encryption and hashing to ensure data confidentiality and integrity. Overall, we achieve smartness through decision-making inferences, security through encryption and hashing, and energy efficiency through both compression and decision-making inferences. By performing data compression and machine learning inference on the IoT sensor node, the SSE approach not only enables the IoT system to push signal processing and decision-making to the extreme of the edge-side (i.e., the sensor node), but also solves the data security and energy efficiency problems simultaneously.
ISBN: 9798662418011Subjects--Topical Terms:
194058
Artificial intelligence.
Subjects--Index Terms:
Accuracy
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
LDR
:04523nmm a2200361 4500
001
594565
005
20210521101655.5
008
210917s2020 ||||||||||||||||| ||eng d
020
$a
9798662418011
035
$a
(MiAaPQ)AAI27961840
035
$a
AAI27961840
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Akmandor, Ayten Ozge.
$3
886577
245
1 0
$a
Accurate, Energy-Efficient, and Secure Machine Learning Models: Applications to Smart Healthcare.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2020
300
$a
230 p.
500
$a
Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
500
$a
Advisor: Jha, Niraj K.
502
$a
Thesis (Ph.D.)--Princeton University, 2020.
506
$a
This item must not be sold to any third party vendors.
520
$a
Machine learning (ML) algorithms automate the data-label mapping and decision-making processes. The output of the ML algorithms determines the next step in real-world applications. If the ML output is diagnosis of a disease, e.g., in smart healthcare applications, then the next step is the treatment options. On the other hand, if the ML output is the traffic condition, e.g., in smart city applications, then the next step is traffic regulation. It is desirable to have ML-based systems that are simultaneously accurate, secure, energy-efficient, low-cost, silently operable, maintainable, customizable, low-delay, and scalable. However, there are tradeoffs among these design goals that impact the complexity of the overall system. This thesis focuses on the accuracy, security, and energy-efficiency objectives.We first target the accuracy objective with a new dual-space (feature and semantic space) classification approach: SECRET. While traditional supervised learning approaches operate in the feature space only, SECRET utilizes both feature and semantic spaces in the classification process. It incorporates class affinity and dissimilarity information into the decision process using the semantic space. This property enables SECRET to make informed decisions on class labels, thus enhancing its overall classification performance. By demonstrating the significant benefits of the semantic space, SECRET opens up a new research direction for the machine learning community.Next, we introduce an automatic stress detection and alleviation system: SoDA. SoDA takes advantage of emerging wearable medical sensors to continuously monitor human stress levels and mitigate stress as it arises. It performs stress detection and alleviation in a user-transparent manner. When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user.Furthermore, we present machine learning models that are built with smartphone and wearable medical sensor data and enable the smartphone to understand us. We call this system your smartphone understands you (YSUY). By understanding our physical, mental, and emotional states, YSUY promotes quality of life of the users by assessing their states and shining light on fundamental human-centric needs.Finally, we introduce a simultaneously smart, secure, and energy-efficient Internet-of-Things (IoT) sensor architecture: SSE. In SSE, we use inference in the compressively-sensed domain and transmit data to the base station when an event of interest occurs. Since on-sensor compression and inference drastically reduce the amount of data that need to be transmitted, we are able to reduce the IoT sensor energy by two-to-three orders of magnitude. A small part of this energy bonus is used to carry out encryption and hashing to ensure data confidentiality and integrity. Overall, we achieve smartness through decision-making inferences, security through encryption and hashing, and energy efficiency through both compression and decision-making inferences. By performing data compression and machine learning inference on the IoT sensor node, the SSE approach not only enables the IoT system to push signal processing and decision-making to the extreme of the edge-side (i.e., the sensor node), but also solves the data security and energy efficiency problems simultaneously.
590
$a
School code: 0181.
650
4
$a
Artificial intelligence.
$3
194058
650
4
$a
Electrical engineering.
$3
454503
653
$a
Accuracy
653
$a
Energy-efficiency
653
$a
Machine learning
653
$a
Security
653
$a
Smart healthcare
690
$a
0800
690
$a
0544
710
2
$a
Princeton University.
$b
Electrical Engineering.
$3
730299
773
0
$t
Dissertations Abstracts International
$g
82-01B.
790
$a
0181
791
$a
Ph.D.
792
$a
2020
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27961840
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000193525
電子館藏
1圖書
電子書
EB 2020
一般使用(Normal)
編目處理中
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27961840
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入