Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model

Afia, Abdellatif El; Aoun, Oussama; Barrio‐García, Salvador del

doi:10.1007/s13748-019-00183-1

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…This matrix corresponds to the probability with which the elements of the alphabet of observations are emitted for each state. The emission matrix that we use in this work is defined in [75,94], and it is detailed as follows:…”

Section: S-phmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Section: S-phmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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“…There are many methods of sign language recognition, one of which utilizes statistical analysis techniques to derive the various eigenvectors of a sample and then classify it. Hidden markov model (HMM) [ 5 ] is a typical representative of this. The second method employs template matching technology, that is, first construct a defined template, then match the original data with the template, and use the similarity as a reference to complete the identification.…”

Section: Introductionmentioning

confidence: 99%

Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine

Gao

Wang

Xue

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Sign language plays a significant role in smooth communication between the hearing-impaired and the healthy. Chinese fingerspelling is an important composition of Chinese sign language, which is suitable for denoting terminology and using as basis of gesture sign language learning. We proposed a Chinese fingerspelling recognition approach via Hu moment invariant and RBF support vector machine. Hu moment invariant was employed to extract image feature and RBF-SVM was employed to classify. Meanwhile, 10-fold across validation was introduced to avoid overfitting. Our method HMI-RBF-SVM achieved overall accuracy of 86.47 ± 1.15% and was superior to three state-of-the-art approaches.

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Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model

Limam

Afia

Chiheb

2020

Heuristics for Optimization and Learning

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Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model

Cited by 10 publications

References 29 publications

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine

Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model

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