E-Nose Vapor Identification Based on Dempster–Shafer Fusion of Multiple Classifiers

Li, W.; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

doi:10.1109/tim.2008.922092

Cited by 25 publications

(8 citation statements)

References 24 publications

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“…Particularly, six transient-state features are obtained from the sensor devices filtered by the wavelet denoising method and are utilized to train multiple classifiers, which are 1) multilayer perceptrons, 2) support vector machines, 3) k-nearest neighbors, and 3) Parzen classifier. Particularly, The Dempster-Shafer technology is exploited to integrate all these classifiers to obtain the final results [9].…”

Section: Related Workmentioning

confidence: 99%

Risk assessment for enterprise merger and acquisition via multiple classifier fusion

Duan¹,

Ye²,

Liu³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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This paper aims to solve the problem of Risk assessment for enterprise merger and acquisition (M&A), which is an important problem in modern company management. Firstly, we design an index system to assess risks of enterprise M&A behavior, and six risks are considered: 1) Systemic risk, 2) Law risk, 3) Financial risk, 4) Intermediary risk, 5) Integrated risk, and 6) Information risk. Furthermore, 18 indexes are chosen to cover these six aspects. Secondly, we illustrate how to utilize the proposed risk assessment in the decision system for enterprise M&A risk assessment. We separate the M&A risk assessment process to three steps, that is, 1) Before M&A, and 2) In M&A, and 3) After M&A. Particularly, after the risk assessment process, there are three decisions for enterprise managers, that is, 1) implement the original M&A plan, 2) modify the original M&A plan, and 3) refuse it. Thirdly, we propose the multiple classifier fusion based risk assessment algorithm, which aims to effectively combine the six support vector machines. To relax the limitation of the SVM classifier, we introduce the fuzzy theory in the multiple classifier fusion algorithm, and the category label assignment is determined by utilizing a maximum membership rule. Finally, we conduct an experiment to make performance evaluation by constructing a dataset which includes the M&A data of 200 enterprises, among which 185 enterprises are used as training dataset and others are regarded as testing dataset. Using ROC curve, MAE and MAPE as evaluation criterions, performance of the proposed method is compared with single SVM scheme. Experimental results demonstrate that combining multiple the SVM classifiers together, accuracy of M&A risk assessment is greatly enhanced.

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Section: Related Workmentioning

confidence: 99%

Risk assessment for enterprise merger and acquisition via multiple classifier fusion

Duan¹,

Ye²,

Liu³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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“…Hao Wei et al used PEN3 e-nose for data collection and designed a back-propagation neural network (BPNN) to detect brown core in the Chinese pear variety huangguan [ 3 ]. Winston Li et al used four classifiers—MLP, SVM, KNN, and Parzen—and fusion in Dempster–Shafer to improve the accuracy of odor classification [ 4 ]. In addition, fluctuation enhanced sensing (FES) has been applied to the field of e-noses to detect gas-phase chemicals [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network

Luo

Wen

et al. 2021

Sensors

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The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL divergence scheme (SF-KL). The OI-DSCNN was conducted on the Chinese herbal medicine dataset, and simulation experiments and hardware implementation validate its effectiveness. These findings shed light on quick and accurate odor identification in the FPGA.

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“…Ayhan et al [ 12 ] explored the fluctuation-enhanced sensing method to detect and classify gases with improved accuracy when developing classification models using machine learning algorithms. Some applications include medical diagnostics [ 13 ], space shuttles and stations [ 14 , 15 , 16 ], crime and security [ 17 ], and food and beverages, such as rapeseed to detect volatile compounds in pressed oil [ 18 ], wine [ 19 ], and beer [ 20 ], among others. The latter study describes a low-cost e-nose developed with nine gas sensors to assess the aroma profile of beers coupled with machine learning modeling.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

Fuentes

Summerson

Viejo

et al. 2020

Sensors

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Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.

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E-Nose Vapor Identification Based on Dempster–Shafer Fusion of Multiple Classifiers

Cited by 25 publications

References 24 publications

Risk assessment for enterprise merger and acquisition via multiple classifier fusion

Risk assessment for enterprise merger and acquisition via multiple classifier fusion

FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

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