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Original Article

An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data

, , , , , , , & show all
Pages 726-736 | Received 28 Dec 2020, Accepted 16 Apr 2021, Published online: 05 May 2021

References

  • Bashir, M. K.; Schilizzi, S.; Pandit, R. The Determinants of Rural Household Food Security in the Punjab, Pakistan. An Econ Anal. 2012, 1784–2016-141882 doi:https://doi.org/10.22004/ag.econ.122526
  • Ericksen, P. J.;. Conceptualizing Food Systems for Global Environmental Change Research. Glob. Environ. Chang. 2008, 18(1), 234–245. DOI: https://doi.org/10.1016/j.gloenvcha.2007.09.002.
  • Ingram, J.;. A Food Systems Approach to Researching Food Security and Its Interactions with Global Environmental Change. Food Secur. 2011, 3(4), 417–431. DOI: https://doi.org/10.1007/s12571-011-0149-9.
  • Mkwambisi, D. D.; Fraser, E. D. G.; Dougill, A. J. Urban Agriculture and Poverty Reduction: Evaluating How Food Production in Cities Contributes to Food Security, Employment and Income in Malawi. J. Int. Dev. 2011, 23(2), 181–203. DOI: https://doi.org/10.1002/jid.1657.
  • Gittelsohn, J.; Mookherji, S.; Pelto, G. Operationalizing Household Food Security in Rural Nepal. Food Nutr. Bull. 1998, 19(3), 210–222. DOI: https://doi.org/10.1177/156482659801900304.
  • GoP. Economic Survey of Pakistan 2019-20. Econ. Advis. Wing, Islam. Pakistan. 2020.
  • C. D. Nutrition Wing Government of Pakistan, Islamabad, NNSP. National Nutrition Survey Pakistan. 2019.
  • GoP. Economic Survey of Pakistan 2016-17. Econ. Advis. Wing, Islam. Pakistan. 2017, 115–125.
  • Bashir, M. K.; Schilizzi, S.; Pandit, R. The Determinants of Rural Household Food Security in the Punjab, Pakistan: An Econometric Analysis. 2012.
  • Sharma, P.; Gulati, A. Approaches to Food Security in Brazil, China, India, Malaysia, Mexico, and Nigeria: Lessons for Developing Countries. 2015.
  • Okori, W.; Obua, J. Machine Learning Classification Technique for Famine Prediction. in Proceedings of the world congress on engineering, 2011, 2, 6–8.
  • Liu, J.; Yu, J. Research on Development of Android Applications. In 2011 4th International Conference on Intelligent Networks and Intelligent Systems, 2011,69–72.
  • Amir, R. M.; Shahbaz, B.; Ali, T.; Zafar, M. I. Analysis of Household Food Security Concerns and Coping Strategies of Small Farmers in Northwestern Highlands of Pakistan. Pakistan J. Agric. Sci. 2013, 50, 505–510.
  • Gayi, S. K.;. Does the WTO Agreement on Agriculture Endanger Food Security in Sub-Saharan Africa? Food Secur. Indic. Meas. Impact Trade Openness. 2007, 284–321.
  • FAO. Trade Reforms and Food Security: Conceptualizing the Linkages. 2003.
  • Alinovi, L.; Mane, E.; Romano, D. Measuring Household Resilience to Food Insecurity: Application to Palestinian Households. EC-FAO Food Secur. Program. Rom. 2009.
  • Garrett, J. L.; Ruel, M. T. Are Determinants of Rural and Urban Food Security and Nutritional Status Different? Some Insights from Mozambique. World Dev. 1999, 27(11), 1955–1975. DOI: https://doi.org/10.1016/S0305-750X(99)00091-1.
  • McKeown, D. Food Security: Implications for the Early Years. Background paper. Toronto Public Health. 2006.
  • Bashir, M. K.; Schilizzi, S. “‘have Policies in Pakistan Been Effective for Improving Food Security?’ Wanted: Disaggregated Policy Assessment! World Appl. Sci. J. 2012, 17(9), 1182–1191.
  • Gorton, D.; Bullen, C. R.; Mhurchu, C. N. Environmental Influences on Food Security in High-income Countries. Nutr. Rev. 2010, 68(1), 1–29. DOI: https://doi.org/10.1111/j.1753-4887.2009.00258.x.
  • Maharjan, K. L.; Khatri-Chhetri, A. Household Food Security in Rural Areas of Nepal: Relationship between Socio-economic Characteristics and Food Security Status. 2006.
  • Akter, S.; Basher, S. A. The Impacts of Food Price and Income Shocks on Household Food Security and Economic Well-being: Evidence from Rural Bangladesh. Glob. Environ. Chang. 2014, 25, 150–162. DOI: https://doi.org/10.1016/j.gloenvcha.2014.02.003.
  • Campbell, Bruce M., Sonja J. Vermeulen, Pramod K. Aggarwal, Caitlin Corner-Dolloff, Evan Girvetz, Ana Maria Loboguerrero, Julian Ramirez-Villegas et al. “Reducing risks to food security from climate change.” Global Food Security 11 (2016): 34–43.. doi:https://doi.org/10.1016/j.gfs.2016.06.002
  • Marie-Sainte, Souad Larabi, Nada Alalyani, Sihaam Alotaibi, Sanaa Ghouzali, and Ibrahim Abunadi. “Arabic natural language processing and machine learning-based systems.” IEEE Access 7 (2018): 7011–7020
  • Onan, A. (2020). Sentiment Analysis on Product Reviews Based on Weighted Word Embeddings and Deep Neural Networks. Concurrency and Computation: Practice and Experience. e5909.
  • Onan, A.; Toçoğlu, M. A. Weighted Word Embeddings and Clustering‐based Identification of Question Topics in MOOC Discussion Forum Posts; Computer Applications in Engineering Education, 2020.
  • Onan, Aytuğ, Serdar Korukoğlu, and Hasan Bulut. “Ensemble of keyword extraction methods and classifiers in text classification.” Expert Systems with Applications 57 (2016): 232–247
  • Onan, A.;. Mining Opinions from Instructor Evaluation Reviews: A Deep Learning Approach. Comput. Appl. Eng. Educ. 2020, 28(1), 117–138. DOI: https://doi.org/10.1002/cae.22179.
  • Onan, A.; (2020, October). Sentiment Analysis in Turkish Based on Weighted Word Embeddings. In 2020 28th Signal Processing and Communications Applications Conference (SIU). (pp. 1–4). IEEE.
  • Onan, A.;. Sentiment Analysis on Massive Open Online Course Evaluations: A Text Mining and Deep Learning Approach; Computer Applications in Engineering Education, 2020.
  • Onan, A.; (2019, August). Deep Learning Based Sentiment Analysis on Product Reviews on Twitter. In International Conference on Big Data Innovations and Applications. (pp. 80–91). Springer, Cham.
  • Onan, A.;. Two-stage Topic Extraction Model for Bibliometric Data Analysis Based on Word Embeddings and Clustering. IEEE Access. 2019, 7, 145614–145633. DOI: https://doi.org/10.1109/ACCESS.2019.2945911.
  • Onan, A.; Toçoğlu, M. A. A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification. IEEE Access. 2021, 9, 7701–7722. DOI: https://doi.org/10.1109/ACCESS.2021.3049734.
  • Abid, M.; Scheffran, J.; Schneider, U. A.; Ashfaq, M. Farmers’ Perceptions of and Adaptation Strategies to Climate Change and Their Determinants: The Case of Punjab Province, Pakistan. Earth Syst. Dyn. 2015, 6(1), 225–243. DOI: https://doi.org/10.5194/esd-6-225-2015.
  • Ahmed, M.; Ullah, S.; Paracha, Z. U. H. The Retail Food Sector in Pakistan. Int. J. Acad. Res. Bus. Soc. Sci. 2012, 2(12), 122.
  • Pinckney, T. C.;. The Demand for Public Storage of Wheat in Pakistan. Intl Food Policy Res Inst. 1989, 77.20–30
  • Frankenberger, T. R.; McCaston, M. K. The Household Livelihood Security Concept. Food Nutr. Agric. 1998,22, 30–35
  • Ahmed, U. I.; Ying, L.; Bashir, M. K.; Abid, M.; Zulfiqar, F. Status and Determinants of Small Farming Households’ Food Security and Role of Market Access in Enhancing Food Security in Rural Pakistan. PLoS One. 2019, 12(10), e0185466. DOI: https://doi.org/10.1371/journal.pone.0185466.
  • Enenkel, Markus, Linda See, Mathias Karner, Mònica Álvarez, Edith Rogenhofer, Carme Baraldès-Vallverdú, Candela Lanusse, and Núria Salse. “Food security monitoring via mobile data collection and remote sensing: Results from the Central African Republic.” PloS one 10, 11 (2015): e0142030
  • Breiman, L.;. Random Forests. Mach. Learn. 2001, 45(1), 5–32. DOI: https://doi.org/10.1023/A:1010933404324.
  • JJames, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning. 112. New York: springer, 2013
  • Tan, P.-N.; Steinbach, M.; Kumar, V. Classification: Basic Concepts, Decision Trees, and Model Evaluation. Introd. Data Min. 2006, 1, 145–205.
  • Bradley, A. P.;. The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognit. 1997, 30(7), 1145–1159. DOI: https://doi.org/10.1016/S0031-3203(96)00142-2.
  • Powers, D. M. Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. 2011.
  • Huang, Y. J.; Powers, R.; Montelione, G. T. Protein NMR Recall, Precision, and F-measure Scores (RPF Scores): Structure Quality Assessment Measures Based on Information Retrieval Statistics. J. Am. Chem. Soc. 2005, 127(6), 1665–1674. DOI: https://doi.org/10.1021/ja047109h.
  • Ege, T.; Yanai, K. Image-based Food Calorie Estimation Using Recipe Information. IEICE Trans. Inf. Syst. 2018, E101.D(5), 1333–1341. DOI: https://doi.org/10.1587/transinf.2017MVP0027.
  • Meyers, Austin, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, and Kevin P. Murphy. “Im2Calories: towards an automated mobile vision food diary.” In Proceedings of the IEEE International Conference on Computer Vision, pp. 1233–1241. 2015.USA