Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network

Macasaet, Dailyne; Illahi, Ana Antoniette C.; Dadios, Elmer P.; Lauguico, Sandy; Alejandrino, Jonnel

doi:10.12720/jait.12.1.36-44

Cited by 9 publications

(1 citation statement)

References 9 publications

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“…The common structure of the ANN is a multilayer feedforward architecture. This structure consists of three layers -the input layer, the hidden layer and the output layer [20]. Note that there can be one or more hidden layers depending on the complexity of the problem.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

Kantanantha

2023

Advances in Transdisciplinary Engineering

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Electricity is important in our modern life and also essential to the development of the country. Since the electricity load consumption in Thailand increases almost every year, the power systems capacity expansion is unavoidable. The purpose of this paper was to apply the machine learning algorithms to forecast the long-term electricity load consumption in Thailand. Three algorithms employed were artificial neural network (ANN), support vector regression (SVR), and k-nearest neighbors (KNN). The performances were evaluated in terms of mean absolute percentage error (MAPE). The results showed that the ANN outperformed other algorithms with a MAPE of 2.586%.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

Kantanantha

2023

Advances in Transdisciplinary Engineering

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Investigation of Chemiresisitive Electronic Nose for the Analysis of Multiple Analytes Using Pattern Recognition Algorithm

Keshari,

Prabhakar Rao,

Sree Rama Murthy

et al. 2024

Sens Imaging

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The multiple analytes produced during the operation of nuclear facilities are required to monitor the smooth operation of the plant in the environment of high temperature and radioactivity in real time. A chemiresisitive electronic nose was investigated and developed to analyze the multiple analytes generated in the nuclear reactor/allied facilities. An electronic nose consists of chemiresisitive sensor, array, housing, hardware, software, and pattern recognition algorithm. The sensor and array of different semiconductor metal oxides were prepared, processed, and developed to sense the multiple analytes. The hardware and data acquisition software (DAS) was designed and developed to acquire the dynamic responses from the array of four sensors. The hardware provides a low excitation voltage for measurement of the dynamic response of four sensors towards the improvement of the life of the sensor. The various experiments were conducted with multiple analytes at different temperatures to study the analysis of analytes. The performance of the hardware and DAS were tested and evaluated with the sensor array responses towards three analytes, viz., hydrogen (H2), formaldehyde (HCHO), and hydrazine (NH2NH2). Different features evaluated from the response traces were processed to teach the instrument using pattern recognition algorithms. The training and real-time testing of the sensor array realized the qualitative discrimination and quantitative estimation of the analytes.

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A Velocity Spectrum Picking Method Based on Detection Fine Tuning Depth Recognition Technology

2021

2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET)

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Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network

Cited by 9 publications

References 9 publications

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

Investigation of Chemiresisitive Electronic Nose for the Analysis of Multiple Analytes Using Pattern Recognition Algorithm

A Velocity Spectrum Picking Method Based on Detection Fine Tuning Depth Recognition Technology

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