scores were integrated into predictive frameworks based on bidirectional long short-term memory (Bi-LSTM) networks and … stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models …

study methods such as Random Forest, Linear Regression, LSTM, and Bi-LSTM which were employed and evaluated using … performance metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared. Among these, the Bi-LSTM model …

combines features from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners …

problem, random oversampling was applied. This study also utilized the BiLSTM model to recognize each entity in new textual … learning model. Several optimization algorithms applied in BiLSTM were evaluated. The results showed improved BiLSTM … precision, recall, and F1-score, respectively. The BiLSTM network captured long-range dependencies in sequential data provided …

Bidirectional Long Short-Term Memory model (BiLSTM) to predict "non-default," "distressed," and "default" loan status, which …

Bidirectional Long Short-Term Memory model (BiLSTM) to predict "non-default," "distressed," and "default" loan status, which …