Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...

Abstract: The integration of artificial intelligence (AI) with wireless sensor networks (WSNs) has substantially advanced the capabilities of these systems to collect, process, and respond to large ...

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...

NTT Research, Inc ., a division of NTT (TY;9432), today announced that members of its Physics & Informatics (PHI) Lab, in ...

Understanding how ozone behaves indoors is vital for assessing human health risks, as people spend most of their time inside.

Abstract: Periodic repetitive tasks are common in industrial production, especially in intelligent equipment for batch processing. This paper studies the disturbance suppression problem of linear ...

Furthermore, 4 machine learning algorithms were assessed: XGBoost (XGB), random forest (RF), logistic regression (LR), and neural networks (NNs). For each dataset, the cross-validated area under the ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...