Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Abstract: In the so-called explained artificial intelligence, there is a need to build small models, but accurate and intuitive for the analyst. It is necessary to formalize, which models are ...

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

If you’ve ever tried to build a agentic RAG system that actually works well, you know the pain. You feed it some documents, cross your fingers, and hope it doesn’t hallucinate when someone asks it a ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Decision-making during the early stages of research and development (R&D) should be ...

Abstract: This study utilizes decision tree and logistic regression models to explore the factors contributing to medical claim denials and identify areas for improvement. We adapt undersampling ...

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...

Balancing water quality standards while facilitating economic growth with uncertain factors in a complex system is challenging for policy makers. This case study analyses the fictional town of Fortuna ...

Decision trees are a powerful tool for decision-making and predictive analysis. They help organizations process large amounts of data and break down complex problems into clear, logical steps. Used in ...

1 Department of Nursing, Harbin Medical University, Harbin, China 2 Department of Nursing, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China Objective: This ...