Food intake calorie prediction using generalized regression neural network

Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regr...

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Main Authors:	Gunawan, Teddy Surya, Kartiwi, Mira, Abdul Malik, Noreha, Ismail, Nanang
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2019
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf

id	iium-72546
recordtype	eprints
spelling	iium-725462019-06-12T02:03:36Z http://irep.iium.edu.my/72546/ Food intake calorie prediction using generalized regression neural network Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang T Technology (General) Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regression Neural Network (GRNN) will be utilized to predict the food intake calorie from the input of digital image. GRNN was utilized due its fast training compared to standard feedforward networks. The food image database comprises of 568 food including sweet, savory, processed, whole foods, and beverages. The calorie has the ranged from 0 kcal (plain water) to 11830 (roasted goose) with median 235.5 kcal. The optimum spread parameter for GRNN was found to be 0.46 when the 568 images was distributed randomly, i.e. 80% training and 20% testing. Due to very large variation of the calorie needs to be predicted, GRNN has rather large prediction error. This could be alleviated using more training data, use other features like texture and segmentation, or deep neural network. IEEE 2019-04-15 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf application/pdf en http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf Gunawan, Teddy Surya and Kartiwi, Mira and Abdul Malik, Noreha and Ismail, Nanang (2019) Food intake calorie prediction using generalized regression neural network. In: 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 28th-30th November 2018, Songkla, Thailand. https://ieeexplore.ieee.org/document/8688787 10.1109/ICSIMA.2018.8688787
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang Food intake calorie prediction using generalized regression neural network
description	Many devices have been proposed to monitor the calorie intake and eating behaviors. These wearable devices uses various sensing modalities, such as acoustic, visual, inertial, EEG (electroglottography), EMG (electromyography), capacitive and piezoelectric sensors. In this paper, Generalized Regression Neural Network (GRNN) will be utilized to predict the food intake calorie from the input of digital image. GRNN was utilized due its fast training compared to standard feedforward networks. The food image database comprises of 568 food including sweet, savory, processed, whole foods, and beverages. The calorie has the ranged from 0 kcal (plain water) to 11830 (roasted goose) with median 235.5 kcal. The optimum spread parameter for GRNN was found to be 0.46 when the 568 images was distributed randomly, i.e. 80% training and 20% testing. Due to very large variation of the calorie needs to be predicted, GRNN has rather large prediction error. This could be alleviated using more training data, use other features like texture and segmentation, or deep neural network.
format	Conference or Workshop Item
author	Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang
author_facet	Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Ismail, Nanang
author_sort	Gunawan, Teddy Surya
title	Food intake calorie prediction using generalized regression neural network
title_short	Food intake calorie prediction using generalized regression neural network
title_full	Food intake calorie prediction using generalized regression neural network
title_fullStr	Food intake calorie prediction using generalized regression neural network
title_full_unstemmed	Food intake calorie prediction using generalized regression neural network
title_sort	food intake calorie prediction using generalized regression neural network
publisher	IEEE
publishDate	2019
url	http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/ http://irep.iium.edu.my/72546/1/72546%20Food%20Intake%20Calorie%20Prediction.pdf http://irep.iium.edu.my/72546/2/72546%20Food%20Intake%20Calorie%20Prediction%20SCOPUS.pdf
first_indexed	2023-09-18T21:42:47Z
last_indexed	2023-09-18T21:42:47Z
_version_	1777413251208839168

Food intake calorie prediction using generalized regression neural network

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