A Context-Aware Service Selection Mechanism Based on Hidden Markov Model

2013 ◽  
Author(s):  
Xiao Zheng ◽  
Yaqing Shi ◽  
Xiujun Wang ◽  
Chunying Xu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Service Selection ◽  
Context Aware ◽  
Selection Mechanism ◽  
Context Aware Service

Context aware Hidden Markov Model for Intention process mining

2021 ◽  
Author(s):  
Imane Choukri ◽  
Hatim Guermah ◽  
Abdelmajid Daosabah ◽  
Mahmoud Nassar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Process Mining ◽  
Hidden Markov ◽  
Context Aware

scholarly journals Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

2020 ◽  
pp. 37-44 ◽  
Author(s):  
Aji Gautama Putrada ◽  
Nur Ghaniaviyanto Ramadhan ◽  
Maman Abdurohman
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Naive Bayes ◽  
Hidden Markov ◽  
Naïve Bayes ◽  
Context Aware ◽  
Movement Data ◽  
Hierarchical Hidden Markov Model ◽  
Door Lock

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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