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Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis

Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis

Andrew Lukyamuzi, John Ngubiri, Washington Okori
Copyright: © 2016 |Pages: 17
ISBN13: 9781466698406|ISBN10: 1466698403|EISBN13: 9781466698413
DOI: 10.4018/978-1-4666-9840-6.ch060
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MLA

Lukyamuzi, Andrew, et al. "Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis." Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1309-1325. https://doi.org/10.4018/978-1-4666-9840-6.ch060

APA

Lukyamuzi, A., Ngubiri, J., & Okori, W. (2016). Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis. In I. Management Association (Ed.), Big Data: Concepts, Methodologies, Tools, and Applications (pp. 1309-1325). IGI Global. https://doi.org/10.4018/978-1-4666-9840-6.ch060

Chicago

Lukyamuzi, Andrew, John Ngubiri, and Washington Okori. "Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis." In Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1309-1325. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9840-6.ch060

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Abstract

Food insecurity is a global challenge affecting millions of people especially those from least developed regions. Famine predictions are being carried out to estimate when shortage of food is most likely to happen. The traditional data sets such as house hold information, price trends, crop production trends and biophysical data used for predicting food insecurity are both labor intensive and expensive to acquire. Current trends are towards harnessing big data to study various phenomena such sentiment analysis and stock markets. Big data is said to be easier to obtain than traditional datasets. This study shows that phone messages archives and telephone conversations as big datasets are potential for predicting food crisis. This is timely with the current situation of massive penetration of mobile technology and the necessary data can be gathered to foster studies such as this. Computation techniques such as Naïve Bayes, Artificial Networks and Support Vector Machines are prospective candidates in this strategy. If the strategy is to work in a nation like Uganda, areas of concern have been highlighted. Future work points at exploring this approach experimentally.

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