Grammatical category disambiguation based on second order hidden Markov model

2002 ◽  
Author(s):  
Sun Jian ◽  
Wang Wei ◽  
Zhong Yixin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order ◽  
Grammatical Category

scholarly journals Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254 ◽  
Author(s):  
Yang Sung-Hyun ◽  
Keshav Thapa ◽  
M Humayun Kabir ◽  
Lee Hee-Chan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Human Activities ◽  
Time Complexity ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Elderly Care ◽  
Second Order ◽  
Assistive Living

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

scholarly journals Emirati-Accented Emotion Verification based on HMM3s, HMM2s, and HMM1s

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Corpus ◽  
Third Order ◽  
First Order ◽  
Accented Speech ◽  
Speaker Independent

he proposed research is dedicated to verifying the claimed emotion of speaker-independent and text-independent formed on three dissimilar classifiers. The HMM3 short for Third-Order Hidden Markov Model, HMM2 short for Second-Order Hidden Markov Model, and HMM1 short for First-Order Hidden Markov Model are the three classifiers utilized in this study. Our work has been evaluated on our collected Emirati-accented speech corpus which entails 50 speakers of Emirati origin (25 female and 25 male) uttering sentences in six emotions by means of the extracted features by Mel-Frequency Cepstral Coefficients (MFCCs). Our outcomes prove that HMM3 is superior to each of HMM1 and HMM2 to authenticate the claimed emotion. The achieved results formed on HMM3 are very similar to the outcomes attained in the subjective valuation by Arab listeners.

scholarly journals Second-order autoregressive Hidden Markov Model

2017 ◽  
Vol 31 (3) ◽  
pp. 653-665
Author(s):  
Daiane Aparecida Zuanetti ◽  
Luis Aparecido Milan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order

scholarly journals An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiao Fu ◽  
Jiaxu Zhang ◽  
Yue Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Study Results

Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.

MicroRNA target prediction based on second-order Hidden Markov Model

2010 ◽  
Vol 5 (2) ◽  
pp. 171-179
Author(s):  
Song Gao ◽  
Liangsheng Zhang ◽  
Diangang Qin ◽  
Tienan Feng ◽  
Yifei Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Target Prediction ◽  
Second Order ◽  
Microrna Target ◽  
Microrna Target Prediction
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