Proceedings Article10.1109/ICEAST.2019.8802538
A Deep Learning Model for Odor Classification Using Deep Neural Network
Boonyawee Grodniyomchai,Khattiya Chalapat,Kulsawasd Jitkajornwanich,Saichon Jaiyen +3 more
- 01 Jul 2019
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TL;DR: This research adopts the Deep Neural Network (DNN) model to identify some types of odor including odorless, beer odor, whisky odor, and wine odor and it can signify that the proposed deep learning model can achieve the best average accuracy.
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Abstract: The odor is an environment that surrounds us. However, to identify the odor by using the human nose in order to prove the odor is very dangerous. Therefore, the artificial intelligent (AI) system should be built based on machine learning in order to achieve more accurate results. This research adopts the Deep Neural Network (DNN) model to identify some types of odor including odorless, beer odor, whisky odor, and wine odor. Each contains 60 instances that are obtained from seven sensors of the electronic nose. The experiments are conducted, and the results are compared to the comparative machine learning methods including Multilayer Perceptron (MLP), Decision Tree and Naive Bayes (NB). From the experimental results, it can signify that the proposed deep learning model can achieve the best average accuracy.
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Citations
Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
Zhenyi Ye,Yuan Liu,Qiliang Li +2 more
TL;DR: In this article, a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
94
Gas Recognition in E-Nose System: A Review
TL;DR: In this paper , the authors investigate several gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations, and find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise.
Identification of odor emission sources in urban areas using machine learning-based classification models
TL;DR: In this article , the authors proposed a method using machine learning-based classification models to identify odor sources in urban areas, where decision tree (DT) and random forest (RF) algorithms were used as classification models for identifying odor sources with 23 variables.
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Advanced Algorithms for Low Dimensional Metal Oxides-Based Electronic Nose Application: A Review
Xi Wang,Yangming Zhou,Zhikai Zhao,Xiujuan Feng,Mingzhi Jiao +4 more
TL;DR: In this article , the authors reviewed the principles and performances of typical gas recognition methods of the electronic nose up to now and compares and analyzes the classical gas recognition method and the neural network-based methods.
An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans
Chung-Hong Lee,I-Te Chen,Hsin-Chang Yang,Yenming J. Chen +3 more
TL;DR: In this paper , an e-nose system was developed to discriminate the aroma of freshly roasted coffee in different production regions, and the extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics.
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References
Code Smell Detection: Towards a Machine Learning-Based Approach
Francesca Arcelli Fontana,Marco Zanoni,Alessandro Marino,Mika V. Mäntylä +3 more
- 22 Sep 2013
TL;DR: This paper proposes an approach for smells detection based on machine learning techniques, outlines some common problems faced and describes the different steps of the approach and the algorithms used for the classification.
162
Bad-smell prediction from software design model using machine learning techniques
Nakarin Maneerat,Pomsiri Muenchaisri +1 more
- 11 May 2011
TL;DR: This work presents methodology for predicting bad-smells from software design model using seven machine learning algorithms and concludes that the methodology have proximity to actual values.
79
A CNN-based simplified data processing method for electronic noses
Pei-Feng Qi,Qing-Hao Meng,Ming Zeng +2 more
- 01 May 2017
TL;DR: A simplified method based on convolutional neural network (CNN) for e-noses that not only uses fewer sampling points to perform classification, but also can automatically implement feature generation without signal pre-processing step which significantly improves detection efficiency and simplifies data processing procedures of e-Noses.
38
Comparison of multivariate normalization techniques as applied to electronic nose based pattern classification for black tea
Bipan Tudu,Bikram Kow,Nabarun Bhattacharyya,Rajib Bandyopadhyay +3 more
- 01 Nov 2008
TL;DR: A comparative study of different normalization techniques for enhancing pattern classification of black tea using electronic nose marginally enhances the pattern recognition accuracy of electronic nose system.
16
A new embedded e-nose system to identify smell of smoke
Salahedin Sadeghifard,Leili Esmaeilani +1 more
- 16 Jul 2012
TL;DR: This work examines the important applications of modern electronic noses and focus on fire detection system due to advantages over classical method of detections, and has 97.2% efficiency in smoke classification.
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