scholarly journals Volatile organic compounds of biofluids for detecting lung cancer by an electronic nose based on artificial neural network

2019 ◽  
Vol 17 (1) ◽  
pp. 67-67 ◽  
Author(s):  
Ehab I. Mohamed ◽  
Marwa A. Mohamed ◽  
Samir M. Abdel-Mageed ◽  
Taher S. Abdel-Mohdy ◽  
Mohamed I. Badawi ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Volatile Organic ◽  
Artificial Neural

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

A PRELIMINARY STUDY ON DETECTION OF LUNG CANCER CELLS BASED ON VOLATILE ORGANIC COMPOUNDS SENSING USING ELECTRONIC NOSE

2015 ◽  
Vol 77 (7) ◽  
Author(s):  
Reena Thriumani ◽  
Amanina Iymia Jeffreea ◽  
Ammar Zakaria ◽  
Yumi Zuhanis Has-Yun Hasyim ◽  
Khaled Mohamed Helmy ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Cells ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Preliminary Investigation ◽  
Lung Cancer Cells ◽  
Polymer Sensors ◽  
Volatile Organic

 This paper proposes a preliminary investigation on the volatile production patterns generated from three sets of in-vitro cancerous cell samples of headspace that contains volatile organic compounds using the electronic nose system.  A commercialized electronic nose consisting of 32 conducting polymer sensors (Cyranose 320) is used to analyze the three classes of signals which are lung cancer cells grown in media, breast cancer cells grown in media and the blank media (without cells). Neural Network (PNN) based classification technique is applied to investigate the performance of an electronic nose (E-nose) system for cancerous lung cell classification.  

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