xFiTRNN: A hybrid self attent linearized phrase structured contextualized transformer based RNN for financial sentence analysis with sentence level explainability
xFiTRNN is a hybrid model for financial sentence analysis, combining self-attention, linearized phrase structures, and a contextualized transformer-based RNN. It improves accuracy in sentiment classification while providing sentence-level explainability. This model is ideal for financial text analysis and decision-making transparency.
Dec 7, 2024
InsightBot
SmartAnswer AI is an AI-powered app that uses LLM models like ChatGPT to analyze sentences and provide accurate answers with clear explanations. It enhances user interaction through natural conversations and simplifies complex queries for better understanding.
Jun 1, 2024
Large Language Models in Computer Science Education: A Systematic Literature Review
Neonatal death is a major issue worldwide. Early prediction of at-risk babies can help prevent death. Using data from 1.4 million newborns, machine learning models like XGBoost, Random Forest, and LSTM were tested. XGBoost and Random Forest had 94% accuracy, while LSTM achieved 99% accuracy. LSTM is the most effective model for predicting neonatal mortality and guiding care.
Mar 1, 2024
A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
Neonatal death is a major issue worldwide. Early prediction of at-risk babies can help prevent death. Using data from 1.4 million newborns, machine learning models like XGBoost, Random Forest, and LSTM were tested. XGBoost and Random Forest had 94% accuracy, while LSTM achieved 99% accuracy. LSTM is the most effective model for predicting neonatal mortality and guiding care.
Dec 1, 2023