Abstract: Currently hundreds of studies in the literature have shown the link between food and reducing the risk of chronic diseases. This study investigates the use of natural language processing and artificial intelligence techniques in developing a classifier that is able to identify, extract and analyze food-health articles automatically. In particular, this research focusses on automatic identification of health articles pertinent to roles of food in lowering the risk of cardiovascular disease, type-2 diabetes and cancer. Three hundred food-health articles on that topic were analyzed to help identify a unique key (Identifier) for each set of publications. These keys were employed to construct a classifier that is capable of performing online search for identifying and extracting scientific articles in request. The classifier showed promising results to perform automatic analysis of food-health articles which in turn would help food professionals and researchers to carry out efficient literature search and analysis in a timely fashion.

Marco Ross, El Sayed Mahmoud and El-Sayed M. Abdel-Aal, “Exploring Identifiers of Research Articles Related to Food and Disease using Artificial Intelligence” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091101

@article{Ross2018,
title = {Exploring Identifiers of Research Articles Related to Food and Disease using Artificial Intelligence},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2018.091101},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091101},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Marco Ross and El Sayed Mahmoud and El-Sayed M. Abdel-Aal},
}