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>

scholarly journals A Novel Intrusion Detection System in WSN using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Sarah Salaheldin Lutfi ◽  
◽  
Mahmoud Lutfi Ahmed ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Base Station ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Fuzzy Filter ◽  
Neuro Fuzzy

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. A new method of intrusion detection using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm (HNF-ACA) is proposed in this study, which has been able to map the network status directly into the sensor monitoring data received by base station, accordingly that base station can sense the abnormal changes in network.The hybridized Sugeno-Mamdani based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. The Modified Mutation Based Ant Colony Algorithm technique improves the accuracy of determining the membership values of input trust values of each node in fuzzy filters. To end, the proposed method was tested on the WSN simulation and the results showed that the intrusion detection method in this work can effectively recognise whether the abnormal data came from a network attack or just a noise than the existing methods.

scholarly journals Classification of Alcohol and Carbon Monoxide using Gas Sensors by Heuristic Algorithm based Neural Network

2018 ◽  
Vol 1 (1) ◽  
pp. 130-137
Author(s):  
M. Fatih Adak ◽  
Nejat Yumusak
Keyword(s):  
Neural Networks ◽  
Carbon Monoxide ◽  
Gas Sensors ◽  
Ant Colony Algorithm ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Electronic Noses ◽  
Popular Subject ◽  
The Internet Of Things

The classification of electronic noses data and odors is an issue that needs to be taken to a higher level in industry, science and health. Because of Industry 4.0 and the Internet of Things is todays popular subject, it reinforces this proposal. In this study, the classification of alcohol and carbon monoxide gases which can be used frequently in industry and health fields has been classified. In order for the classification to be successful, neural networks were trained by the help of heuristic algorithms and more successful results than traditional methods have gained. Neural networks, especially trained with the Ant Colony algorithm, have achieved the best classification success in both training and test data. These results show that neural networks trained with Ant Colony algorithm will give successful results in classification of gases such as alcohol and carbon monoxide.

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