Study of Pleural Effusions of Pancreatic Aetiology in a Tertiary Care Centre

Rachakonda, Ramakrishna; Kalyankumar, P; Mandava, Venu

doi:10.14260/jemds/2017/789

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Food Recognition Using Wavelet-lstm1

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2023

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Cited by 1 publication

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“…A multi-senor fusion based system was developed to classify collected activity data between eating and non-eating activities [17]. Camera based objection detection method was used to determine normal eating and stressed eating [18]. The performance of method can be degraded if objects are not aligned to line-ofsight of camera.…”

Section: Food Recognition Using Wavelet-lstmmentioning

confidence: 99%

Food Recognition System: A New Approach Based on Wavelet-LSTM

Hussain

2023

SJET

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An automated system for analyzing daily dietary intake is essential for human well-being and healthcare. This work presents a novel wearable necklace embedded with a piezoelectric sensor and a microcontroller to monitor food ingestion of users. To effectively represent the food ingestion patterns, the sensor signal is dynamically segmented using a bidirectional search technique. Each segmented food intake pattern consists of a chewing sequence and a swallow peak. We exploit wavelet transform to decompose the complex food ingestion patterns, collected by the sensor, into frequency sub-bands at discrete scales. The frequency sub-bands are used as sequences to train long short-term memory (LSTM) for the recognition of 5 food categories. Our proposed recognition model based on wavelet-LSTM recognizes 5 food classes with an accuracy of 98.1%

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Section: Food Recognition Using Wavelet-lstmmentioning

confidence: 99%

Food Recognition System: A New Approach Based on Wavelet-LSTM

Hussain

2023

SJET

View full text Add to dashboard Cite

show abstract

Study of Pleural Effusions of Pancreatic Aetiology in a Tertiary Care Centre

Cited by 1 publication

References 8 publications

Food Recognition System: A New Approach Based on Wavelet-LSTM

Food Recognition System: A New Approach Based on Wavelet-LSTM

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