Context-aware recommender systems using hierarchical hidden Markov model

2019 ◽  
Vol 518 ◽  
pp. 89-98 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Recommender Systems ◽  
Hidden Markov ◽  
Context Aware ◽  
Hierarchical Hidden Markov Model

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%.

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 Statistical structure of locomotion and its modulation by odors

2018 ◽  
Author(s):  
Liangyu Tao ◽  
Siddhi Ozarkar ◽  
Jeff Beck ◽  
Vikas Bhandawat
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Analytical Methods ◽  
Hidden Markov ◽  
Stereotyped Behavior ◽  
Fruit Flies ◽  
Statistical Structure ◽  
Hierarchical Hidden Markov Model ◽  
Stereotyped Behaviors

AbstractMost behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly’s locomotion can be decomposed into a small number of locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.

Detection and Prediction of Abnormal Behavior Based on User Profile in Ubiquitous Home Network Using Hierarchical Hidden Markov Model

2013 ◽  
Vol 11 (9) ◽  
pp. 1814-1819
Author(s):  
Jaewan Shin ◽  
Dongkyoo Shin ◽  
Dongil Shin ◽  
Cheonsik Kim ◽  
Jonghyuk Park
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
User Profile ◽  
Abnormal Behavior ◽  
Home Network ◽  
Hierarchical Hidden Markov Model ◽  
Behavior Based
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