IJBTT

Exploring Machine Learning Algorithm to Analyze Young Adults' Perception of Hunger Issues in the Context of the United Nations' Zero Hunger Initiative
	© 2024 by IJCTT Journal
	Volume-72 Issue-9
	Year of Publication : 2024
	Authors : Beatrice Ayodele Amune, Samara Antonia Burris
	DOI :  10.14445/22312803/IJCTT-V72I9P101

How to Cite?

Beatrice Ayodele Amune, Samara Antonia Burris, "Exploring Machine Learning Algorithm to Analyze Young Adults' Perception of Hunger Issues in the Context of the United Nations' Zero Hunger Initiative ," International Journal of Computer Trends and Technology, vol. 72, no. 9, pp.1-5, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P101

Abstract
The United Nations' Zero Hunger initiative, an ambitious goal of eradicating hunger and ensuring food security by 2030, requires a comprehensive understanding of global perceptions to tailor effective policies and interventions. Young adults, who are at the forefront of social and political activism, represent a crucial demographic in this effort. Their views on hunger and food insecurity can significantly influence public discourse and policy directions. This review article examines the development and application of Machine Learning (ML) algorithms to analyze young adults' perceptions of hunger within the context of the Zero Hunger initiative. By reviewing recent studies and advancements, the article highlights how ML techniques such as Natural Language Processing (NLP), sentiment analysis, and predictive modeling have been utilized to extract meaningful insights from large datasets. These techniques have proven instrumental in understanding the complex and evolving perspectives of young adults on hunger issues, which are influenced by various social, economic, and cultural factors. Furthermore, the article delves into the challenges and ethical considerations associated with applying ML in this context, including data quality, algorithmic bias, and privacy concerns. It emphasizes the importance of developing transparent and interpretable models that both researchers and policymakers can trust. The article also suggests future directions for research, including the integration of multi-modal data and the development of real-time analytics tools. By addressing these challenges and exploring new methodologies, the potential of ML to contribute to the Zero Hunger initiative can be fully realized, making it a critical tool in the global fight against hunger.

Keywords
Machine Learning, Natural Language Processing, Young adult, Hunger issues, Hunger initiative.

Reference

[1] J. Chen, and T. Wong, “Privacy and Bias in Machine Learning: Challenges in Social Data Analysis,” Journal of AI and Ethics, vol. 12, no. 4, pp. 367-382, 2023.
[2] FAO, The State of Food Security and Nutrition in the World 2022, Food and Agriculture Organization of the United Nations, 2022. [Online]. Available: https://www.fao.org/publications/sofi/2022
[3] Susan T. Fiske, and Shelley E. Taylor, Social Cognition: From Brains to Culture, Annual Review of Psychology, vol. 74, pp. 33-60, 2023.
[Google Scholar] [Publisher Link]
[4] M. B. Johnson et al., “Predictive Modeling in Social Sciences: Applications and Challenges,” Journal of Data Science and Applications, vol. 15, no. 2, pp. 210-225, 2022.
[5] H. Kim et al., “Algorithmic Fairness in Machine Learning: Addressing Bias and Inequality,” Journal of Machine Learning Research, vol. 22, no. 3, pp. 15-28, 2023.
[6] S. Kumar et al., “Real-Time Data Analytics for Social Media: Applications in Policy Making,” Journal of Big Data and Society, vol. 11, no. 1, pp. 119-134, 2024.
[7] X. Li, and Y. Sun, “Multi-Modal Machine Learning: Integrating Text, Images, and Video Data,” Journal of Artificial Intelligence Research, vol. 69, no. 3, pp. 203-218, 2023.
[8] T. Nguyen et al., “Exploring the Impact of Economic Inequality on Young Adults' Perception of Hunger: A Machine Learning Approach,” Journal of Social Data Science, vol. 27, no. 2, pp. 120-140, 2024.
[9] R. Patel, and A. Shah, “Social Media and Public Perception: A Machine Learning Perspective,” Journal of Information Science, vol. 48, no. 6, pp. 789-804, 2023.
[10] J. Smith, and M. Jones, “The Role of Young Adults in Shaping Hunger Policy: Insights from Social Media Analysis,” Journal of Public Policy Research, vol. 14, no. 3, pp. 185-202, 2022.
[11] M. Thompson, and S. Lee, “Enhancing Transparency in Machine Learning for Social Applications: A Framework for Explainable AI,” Journal of AI Ethics, vol. 22, no. 1, pp. 102-118, 2023.
[12] UN, Sustainable Development Goals Report, United Nations, 2023. [Online]. Available: https://www.un.org/sustainabledevelopment/progress-report/
[13] WFP, Global Report on Food Crises (GRFC), World Food Programme, 2023. [Online]. Available: https://www.wfp.org/publications/global-report-food-crises-2023
[14] WHO, The Global Health Observatory Explore a World of Health Data, World Health Organization, 2023. [Online]. Available: https://www.who.int/data/gho/
[15] L. Xu, and Q. Wang, “Explainable AI for Social Media Analysis: Enhancing Trust and Transparency,” Journal of Big Data, vol. 16, no. 1, pp. 58-75, 2024.
[16] Y. Zhang et al., “Sentiment Analysis of Young Adults' Perceptions of Hunger: A Cross-Regional Study,” International Journal of Data Science, vol. 18, no. 1, pp. 85-102, 2023.