Traditionally, fish growth has been estimated using statistical models. More recently, machine learning has gained ground as a predictive tool. However, both approaches have limitations. Statistical ...
This research initiative highlights the importance of ethical and explainable artificial intelligence in workforce ...
A-Hybrid-AI-Model-Integrating-LSTM-XGBoost-for-Interpretable-Prediction-Clustering-of-Water-Quality A-Hybrid-AI-Model-Integrating-LSTM-XGBoost-for-Interpretable ...
Disparities in data availability and monitoring capacity have left many cities exposed to infrastructure failures they cannot predict in advance. While some urban centers deploy advanced analytics, ...
ABSTRACT: This paper explores effective methods for predicting gold prices, proposing three modeling strategies: a standalone Long Short-Term Memory (LSTM) network, a Convolutional Self-Attention (CSA ...
Introduction: An inherited blood disorder that bounds the production of beta globin, an important protein that has a handsome contribution in the development of hemoglobin and Red Blood Cells (RBC).
NRMSE-based comparison of five machine learning models (LR, SVR, RNN, Uni-LSTM, and Bi-LSTM) across the three COVID-19 periods. Bi-LSTM consistently delivers the best or nearly best performance in ...
ABSTRACT: This study proposes a dual-architecture Explainable Artificial Intelligence (XAI) framework designed to unify risk scoring methodologies across corporate and retail lending domains. The ...
This project is designed to identify unusual patterns and potential faults in time-series data (e.g., server metrics, sensor readings, financial data). It goes beyond simple anomaly scoring by: The ...
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