Comparison Between Hidden Markov Models and Artificial Neural Networks in the Classification of Bearing Defects

2018 ◽  
pp. 68-78
Author(s):  
Miloud Sedira ◽  
Ridha Ziani ◽  
Ahmed Felkaoui
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Bearing Defects ◽  
Artificial Neural

Hidden Markov Models for Recognition Using Artificial Neural Networks

2006 ◽  
pp. 126-134 ◽  
Author(s):  
V. Bevilacqua ◽  
G. Mastronardi ◽  
A. Pedone ◽  
G. Romanazzi ◽  
D. Daleno
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Artificial Neural

Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models

2020 ◽  
Vol 221 (3) ◽  
pp. 1484-1498 ◽  
Author(s):  
Runhai Feng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Hybrid System ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Rock Properties ◽  
Complex Data ◽  
Artificial Neural

SUMMARY Lithofacies is one of the most important reservoir parameters, which could provide a qualitative description for hydrocarbon and geothermal reservoirs. Various techniques, such as artificial neural networks (ANN) and hidden Markov models (HMM), have been applied to extract this information, with the well log suites as inputs. However, both of these methods have their own limitations, such as no geological priors in ANN, since log samples along the depth direction are treated independently. While in HMM, a simple Gaussian assumption is usually adopted, which may not be sufficient for complex data distributions. In order to address these problems, a new method is proposed, which combines ANN and HMM into a hybrid system. This new technique allows for a more flexible approach to the probability distributions of rock properties, without any Gaussian assumption being made. At the same time, the geological dependence between adjacent samples is introduced by a representative transition matrix of lithofacies. The output probability from ANN must be reformulated to an emission function before it can be fed into the HMM, which is achieved via the Bayes’ rule. Then the Viterbi algorithm in HMM is applied for the decoding of sequential patterns in the subsurface. In this fashion, the classification process can be proceeded statistically and geologically. Better performance of the proposed approach, compared with other classification methods, is demonstrated in two case studies.

scholarly journals ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

2019 ◽  
Author(s):  
Cynthia Maria Chibani ◽  
Florentin Meinecke ◽  
Anton Farr ◽  
Sascha Dietrich ◽  
Heiko Liesegang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Markov Models ◽  
International Committee ◽  
Biological Data ◽  
Sequencing Technologies ◽  
Artificial Neural ◽  
Taxonomic Markers ◽  
Artificial Neural Network Ann

AbstractBackground/ MotivationIn the era of affordable next generation sequencing technologies we are facing an exploding amount of new phage genome sequences. This requests high throughput phage classification tools that meet the standards of the International Committee on Taxonomy of Viruses (ICTV). However, an accurate prediction of phage taxonomic classification derived from phage sequences still poses a challenge due to the lack of performant taxonomic markers. Since machine learning methods have proved to be efficient for the classification of biological data we investigated how artificial neural networks perform on the task of phage taxonomy.ResultsIn this work, 5,920 constructed and refined profile Hidden Markov Models (HMMs), derived from 8,721 phage sequences classified into 12 well known phage families, were used to scan phage proteome datasets. The resulting Phage Family-proteome to Phage-derived-HMMs scoring matrix was used to develop and train an Artificial Neural Network (ANN) to find patterns for phage classification into one of the phage families. Results show that using the 100 fold cross-validation test, the proposed method achieved an overall accuracy of 84.18 %. The ANN was tested on a set of unclassified phages and resulted in a taxonomic prediction. The ANN prediction was benchmarked against the prediction resulting of multi-HMM hits, and showed that the ANN performance is dependent on the quality of the input matrix.ConclusionsWe believe that, as long as some phage families on public databases are underrepresented, multi-HMM hits can be used as a classification method to populate those phage families, which in turn will improve the performance and accuracy of the ANN. We believe that the proposed method is an effective and promising method for phage classification. The good performance of the ANN and HMM based predictor indicates the efficiency of the method for phage classification, where we foresee its improvement with an increasing number of sequenced viral genomes.

Infant Cry Recognition System

2022 ◽  
pp. 629-647
Author(s):  
Yosra Abdulaziz Mohammed
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Acoustic Features ◽  
Infant Cry ◽  
Identification Rate ◽  
The One

Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.

Sign in / Sign up

Export Citation Format

Share Document