A hybrid method for dimensionality reduction in microarray data based on advanced binary ant colony algorithm

2016 ◽  
Author(s):  
Amirreza Rouhi ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Dimensionality Reduction ◽  
Microarray Data ◽  
Hybrid Method ◽  
Ant Colony Algorithm ◽  
Ant Colony

Speech Recognition Using Combined Fuzzy and Ant Colony algorithm

2016 ◽  
Vol 6 (5) ◽  
pp. 2205
Author(s):  
Fooad Jalili ◽  
Milad Jafari Barani
Keyword(s):  
Speech Recognition ◽  
Dimensionality Reduction ◽  
Input Signal ◽  
Speech Signal ◽  
Fuzzy System ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Ant Colony ◽  
Speech Detection

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm.  First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals.  Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>

scholarly journals Speech Recognition Using Combined Fuzzy and Ant Colony algorithm

2016 ◽  
Vol 6 (5) ◽  
pp. 2205
Author(s):  
Fooad Jalili ◽  
Milad Jafari Barani
Keyword(s):  
Speech Recognition ◽  
Dimensionality Reduction ◽  
Input Signal ◽  
Speech Signal ◽  
Fuzzy System ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Ant Colony ◽  
Speech Detection

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm.  First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals.  Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>

Solving the Crossbar Switching Problem Using n-state Ant Colony Algorithm

2013 ◽  
Vol 133 (6) ◽  
pp. 1242-1250
Author(s):  
Li-Qing Zhao ◽  
Rong-Long Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Crossbar Switching

PID Parameters Optimization Using Ant-Colony Algorithm for Human Heart Control

2017 ◽  
Vol 10 (2) ◽  
pp. 94
Author(s):  
Mohamed Aabid ◽  
Ahmed Elakkary ◽  
Nacer Sefiani
Keyword(s):  
Human Heart ◽  
Ant Colony Algorithm ◽  
Parameters Optimization ◽  
Ant Colony

Data association method of SLAM based on ant colony algorithm

2009 ◽  
Vol 29 (1) ◽  
pp. 136-138 ◽  
Author(s):  
Wen-jing ZENG ◽  
Tie-dong ZHANG ◽  
Yu-ru XU ◽  
Da-peng JIANG
Keyword(s):  
Ant Colony Algorithm ◽  
Data Association ◽  
Ant Colony ◽  
Association Method
Sign in / Sign up

Export Citation Format

Share Document