An Electronic Nose Natural Neural Learning Model in Real Work Environment

Ouhmad, Slimane; Makkaoui, Khalid El; Beni‐Hssane, Abderrahim; Hajami, Abdelmajid; Ezzati, Abdellah

doi:10.1109/access.2019.2941473

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Then, the second part was the comparison analysis, testing accuracy rate, response time, and stability. Meanwhile, three benchmarks were selected from the latest research achievements [43,51,54]. Ref.…”

Section: Experiments Methodsmentioning

confidence: 99%

“…Ref. [43] presented a natural neural learning model inspired electronic nose system, called NNL; ref. [51] proposed an online drift compensation by the adaptive active learning on mixed kernel for electronic nose, called AAL; and ref.…”

Section: Experiments Methodsmentioning

confidence: 99%

“…For example, in [42], a novel technique to solve shortages of low-concentration samples of electronic nose based on global and local features fusion was presented. In [43], a natural neural learning model inspired electronic nose system was devised. To be specific, a natural on-line training with only one sample, to extract both eigen-weights and eigen-bias, was built to elaborate a natural identifier neural model in a real work environment.…”

Section: The Inherent Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

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Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

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Section: Experiments Methodsmentioning

confidence: 99%

Section: Experiments Methodsmentioning

confidence: 99%

Section: The Inherent Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

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“…However, due to historical reasons, economic level and other factors, we are restricted from destroying the natural environment. Therefore, if we want to achieve sustainable development, we must damage the contradiction between environmental and resource protection and ecological balance, and we must coordinate to achieve a win-win situation [13,14].…”

Section: Natural Environment Protection and National Integrationmentioning

confidence: 99%

On the Combination of Natural Environment Protection and Ethnic Development

2022

NEP

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National culture is the soul of a country and also represents the belief and spirit of the people in this region to live together. With the development of economic globalization, cultural exchanges between China and other countries have become increasingly frequent. In such complex and changeable situations, fierce competition and resource shortage, natural environment protection has become a problem that we must pay attention to and strive to solve. This paper mainly analyzes the problems of ethnic integration and related legal systems in China from the perspective of protecting the ecological environment. Through the relevant theoretical research on the construction of ecological civilization, the corresponding suggestions and measures are put forward. Finally, the economic development model with national characteristics is designed based on the principles of natural environment protection, and the model is tested. The test results show that the economic development model with national characteristics has a fast data processing time, a short economic development delay time, and high efficiency.

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“…The typical identifier model system has been developed to the point where it can accurately control the safety of the air in any neighborhood under a variety of natural conditions [3]. In this study, very poisonous chemicals, electronic nose (e-nose) sensors, natural identifier neural learning (NINL), artificial neural network (ANN), and are all used.…”

mentioning

confidence: 99%

Toxic Gases Detection and Tolerance Level Classification Using Machine Learning Algorithms

Deepan,

Saravanan

2023

International Journal of Electronics and Telecommunications

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with rapid population increases, people are facing the challenge to maintain healthy conditions. One of the challenges is air pollution. Due to industrial development and vehicle usage air pollution is becoming a high threat to human life. This air pollution forms through various toxic contaminants. This toxic contamination levels increase and cause severe damage to the living things in the environment. To identify the toxic level present in the polluted air various methods were proposed by the authors, But failed to detect the tolerance level of toxic gases. This article discusses the methods to detect toxic gasses and classify the tolerance level of gasses present in polluted air. Various sensors and different algorithms are used for classifying the tolerance level. For this purpose "Artificial Sensing Methodology" (ASM), commonly known as e-nose, is a technique for detecting harmful gases. SO2-D4, NO2-D4, MQ-135, MQ136, MQ-7, and other sensors are used in artificial sensing methods (e-nose). "Carbon monoxide, Sulfur dioxide, nitrogen dioxide, and carbon dioxide" are all detected by these sensors. The data collected by sensors is sent to the data register from there it is sent to the Machine learning Training module (ML) and the comparison is done with real-time data and trained data. If the values increase beyond the tolerance level the system will give the alarm and release the oxygen.

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An Electronic Nose Natural Neural Learning Model in Real Work Environment

Cited by 9 publications

References 33 publications

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

On the Combination of Natural Environment Protection and Ethnic Development

Toxic Gases Detection and Tolerance Level Classification Using Machine Learning Algorithms

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