Gas Classification Using Deep Convolutional Neural Networks

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Fraser, Michael

doi:10.3390/s18010157

Cited by 159 publications

(95 citation statements)

References 22 publications

(24 reference statements)

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“…We chose these approaches because, unlike the classical feature selection method used in artificial olfactory systems, they also used the raw data to process the gas signals. In that way, in (Peng, Zhao, Pan, & Ye, 2018) was presented an approach based on a Deep…”

Section: Discussionmentioning

confidence: 99%

“…This approach is based on a neural network classifier that is feed with the raw data to perform the discrimination tasks (Peng, Zhao, Pan, & Ye, 2018). Inspired by the mentioned approach and looking to accelerate the response, we propose a rapid detection method in wine quality control, focused on an online solution that lets to achieve faster results using only an early portion of the signals, similar to the presented in (Längkvist, Coradeschi, Loutfi, & Balaguru Rayappan, 2013) for a meat spoilage application, but using a supervised method: deep MLP neural network.…”

Section: Rapid and Online Detection Approach Using Deep Mlpmentioning

confidence: 99%

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Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid

Gamboa

Silva

et al. 2019

LWT

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It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier.The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.

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Section: Discussionmentioning

confidence: 99%

Section: Rapid and Online Detection Approach Using Deep Mlpmentioning

confidence: 99%

Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid

Gamboa

Silva

et al. 2019

LWT

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“…LSTM obtained encouraging results in several fields, such as activity recognition [21] or estimating building energy consumption [22]. Moreover, modeling spatial features in time series by means of Convolutional Neural Networks (CNNs) [19,23] achieved promising results in speech recognition [24] or gas classification [25], together with LSTMs models [26].…”

Section: Related Workmentioning

confidence: 99%

Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring

Almonacid-Olleros

Almonacid

Fernández-Carrasco

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.

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“…So, distinguishing correctly and proper treatment leads to better cure of the disease. In this paper, deep convolutional neural network (DCNN) [5], a machine learning classification technique is used to classify the skin cancer images. As accuracy is the most important factor in this problem, by taking more number of images for training the network and by increasing the number of iterations, the DCNN accuracy can be enhanced.…”

Section: Problem Definitionmentioning

confidence: 99%

Classification of skin cancer images using TensorFlow and inception v3

Sai¹,

G²,

Ramya³

et al. 2018

IJET

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It is easy for a human eye to distinguish the images of similar appearance but classifying the images like that of cancer affected skin requires more expertise. And as the skin cancer cases are increasing globally, it requires more number of human experts. To overcome this problem, many people are working on constructing machine learning classifiers which can detect skin cancer automatically by classifying skin images. This paper concentrates on developing an approach for predicting skin cancer by classifying images using deep convolution neural network. The proposed work is tested on standard cancer dataset and obtained more than 85% accuracy.

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Gas Classification Using Deep Convolutional Neural Networks

Cited by 159 publications

References 22 publications

Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid

Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid

Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring

Classification of skin cancer images using TensorFlow and inception v3

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