RosaElysabeth/modeleprediction

Leveraging Explainable Artificial Intelligence for Enhanced Predictive Modeling, A Case Study on Food Security and Nutrition in Madagascar Leveraging Explainable Artificial Intelligence for Enhanced Predictive Modeling, A Case Study on Food Security and Nutrition in Madagascar This research investigates the synergy between machine learning (ML) and explainable artificial intelligence (XAI) to predict complex phenomena, spe-cifically focusing on food security and nutrition in Madagascar in alignment with Sustainable Development Goals. ML's data learning capability, though powerful, requires manual analysis prone to errors. In the context of ML advancements, challenges in applying these techniques to food security under-score the need for a profound understanding of relevant algorithms. XAI emerges as a solution, extracting valuable insights from vast datasets while ensuring transparent decision-making processes. Motivated by the potential to address complex food security issues in Mad-agascar, the research aims to combine ML's predictive strength with XAI's transparency, offering valuable tools for resource management, urban plan-ning, and public health. Challenges encompass understanding ML tech-niques, ensuring result transparency, and managing high-quality data. Objectives include developing innovative ML approaches for food security predic-tion, exploring XAI methods for enhanced understanding, and establishing robust data management protocols. Expected contributions include methodo-logical advancements, transparent models for decision-makers, and practical applications to real-world challenges in Madagascar. By emphasizing this case study, the research seeks a significant local im-pact while contributing to global knowledge in predicting complex phenome-na and advancing Sustainable Development Goals.