Date of Award:

8-2018

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Vladimir Kulyukin

Committee

Vladimir Kulyukin

Committee

Xiaojun Qi

Committee

Haitao Wang

Abstract

Nowadays, everyone is very busy and running around trying to maintain a balance between their work life and family, as the working hours are increasing day by day. In such hassled life people either ignore or do not give enough attention to a healthy diet. An imperative part of a healthy eating routine is the cognizance and maintenance of nourishing data and comprehension of how extraordinary sustenance and nutritious constituents influence our bodies. Besides in the USA, in many other countries, nutritional information is fundamentally passed on to consumers through nutrition labels (NLs) which can be found in all packaged food products in the form of nutrition table. However, sometimes it turns out to be challenging to utilize this information available in these NLs notwithstanding for consumers who are health conscious as they may not be familiar with nutritional terms and discover it hard to relate nutritional information into their day by day activities because of lack of time, inspiration, or training. So it is essential to automate this information gathering and interpretation procedure by incorporating Machine Learning based algorithm to abstract nutritional information from NLs on the grounds that it enhances the consumer’s capacity to participate in nonstop nutritional information gathering and analysis.

Checksum

ae36983fbcc2981bd3559a0f3c0ce909

Download

Share

COinS

Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .