Quality Control of Information Engineering Surveillance Based on Baum-Welch Algorithm

A new method of Quality Control for Information Engineering Surveillance based on Hidden Markov Model (HMM) has been proposed and the related model been built by us. The process of information engineering quality surveillance can be seen as a two-layered random process. The five elements of HMM correspond with the process of quality surveillance through abstracting the characteristics of the surveillance process. Software quality can be estimated under the model. In this paper, we divided the five elements. Therefore, the model was improved from single dimension to multi-dimension, trained by Baum-Welch algorithm. Experimental results show that the proposed model proves to be feasible and real-time when it is used for quality control.

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Real-time speech recognition engine for accent correction using Hidden Markov Model

10.1063/1.5080882 ◽

2018 ◽

Author(s):

J. B. Lazaro ◽

M. C. P. Po ◽

L. M. Ramones ◽

P. M. L. Tolidanes

Keyword(s):

Speech Recognition ◽

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Speech Recognition Engine

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Real-Time Intrusion Prediction Using Hidden Markov Model with Genetic Algorithm

Advances in Intelligent Systems and Computing - Artificial Intelligence and Evolutionary Algorithms in Engineering Systems ◽

10.1007/978-81-322-2126-5_78 ◽

2014 ◽

pp. 731-736 ◽

Cited By ~ 3

Author(s):

T. Divya ◽

Kandasamy Muniasamy

Keyword(s):

Genetic Algorithm ◽

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov

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A Low-power Real-time Hidden Markov Model Accelerator for Gesture User Interface on Wearable Devices

JSTS Journal of Semiconductor Technology and Science ◽

10.5573/jsts.2019.19.4.396 ◽

2019 ◽

Vol 19 (4) ◽

pp. 396-403 ◽

Cited By ~ 1

Author(s):

Hyeon-Gu Do ◽

Seongrim Choi ◽

Jaemin Hwang ◽

Ara Kim ◽

Byeong-Gyu Nam

Keyword(s):

User Interface ◽

Markov Model ◽

Low Power ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Wearable Devices

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Real Time Intrusion Prediction based on Optimized Alerts with Hidden Markov Model

Journal of Networks ◽

10.4304/jnw.7.2.311-321 ◽

2012 ◽

Vol 7 (2) ◽

Cited By ~ 6

Author(s):

Alireza Shameli Sendi ◽

Michel Dagenais ◽

Masoume Jabbarifar ◽

Mario Couture

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov

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Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition

EURASIP Journal on Embedded Systems ◽

10.1186/1687-3963-2006-048085 ◽

2006 ◽

Vol 2006 (1) ◽

pp. 048085 ◽

Cited By ~ 3

Author(s):

Jeffrey Schuster ◽

Kshitij Gupta ◽

Raymond Hoare ◽

AlexK Jones

Keyword(s):

Speech Recognition ◽

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Model Based ◽

Fpga Architecture

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Hidden Markov Model Based Real Time Network Security Quantification Method

2009 International Conference on Networks Security, Wireless Communications and Trusted Computing ◽

10.1109/nswctc.2009.375 ◽

2009 ◽

Cited By ~ 2

Author(s):

Weiming Li ◽

Zhengbiao Guo

Keyword(s):

Network Security ◽

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Quantification Method ◽

Model Based

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Real-Time Gesture-Based Communication Using Possibility Theory-Based Hidden Markov Model

Computational Intelligence ◽

10.1111/coin.12116 ◽

2017 ◽

Vol 33 (4) ◽

pp. 843-862 ◽

Cited By ~ 1

Author(s):

Neha Baranwal ◽

G. C. Nandi ◽

Avinash Kumar Singh

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Possibility Theory

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Rejection of irrelevant human actions in real-time hidden Markov model based recognition systems for wearable computers

Proceedings of the 2nd Conference on Wireless Health - WH '11 ◽

10.1145/2077546.2077555 ◽

2011 ◽

Cited By ~ 3

Author(s):

Jerry Mannil ◽

Mohammad-Mahdi Bidmeshki ◽

Roozbeh Jafari

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Wearable Computers ◽

Human Actions ◽

Model Based ◽

Recognition Systems

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A Topic-Based Hidden Markov Model for Real-Time Spam Tweets Filtering

Procedia Computer Science ◽

10.1016/j.procs.2017.08.075 ◽

2017 ◽

Vol 112 ◽

pp. 833-843 ◽

Cited By ~ 7

Author(s):

Mahdi Washha ◽

Aziz Qaroush ◽

Manel Mezghani ◽

Florence Sedes

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov

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An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

10.22541/au.162685414.44316319/v1 ◽

2021 ◽

Author(s):

G Manoharan ◽

K Sivakumar

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Outlier Detection ◽

Hidden Markov ◽

Research Work ◽

Detection Accuracy ◽

Detection Techniques ◽

Detection Model ◽

Huge Data ◽

Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

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