Comprehensive Investigation
Comprehensive investigations across diverse scientific domains are currently focused on improving the robustness, fairness, and efficiency of machine learning models. Research emphasizes addressing biases in models, particularly concerning race and gender, and enhancing their generalizability across different datasets and applications, often employing techniques like domain adaptation and data augmentation. These efforts are crucial for ensuring the reliability and ethical deployment of AI in various fields, ranging from healthcare and social media analysis to industrial automation and natural language processing. The ultimate goal is to develop more accurate, trustworthy, and equitable AI systems.
Papers
Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability
Masaru Yamada
Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors
Fatin Abrar Shams, Rashed Hasan Ratul, Ahnaf Islam Naf, Syed Shaek Hossain Samir, Mirza Muntasir Nishat, Fahim Faisal, Md. Ashraful Hoque