When pitching the use of a model, data scientists rarely report on its potential value. They then experience an unnerving ...
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 ...
Srinubabu Kilaru said Bringing version control and CI/CD into data pipelines changed how quickly we could respond to policy ...
Learn how to choose the right cross-validation method for your machine learning projects. Compare techniques like k-fold, stratified, and leave-one-out cross-validation, and understand when to use ...
Background and Purpose: Radiation dermatitis (RD), a common adverse reaction in breast cancer radiotherapy, impairs quality of life and increases healthcare burdens. Developing an effective risk ...
Objectives Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate ...
Abstract: Effective overtime planning in software projects remains a critical challenge due to the complex interplay between project constraints and human-centric decision-making. This study ...
Abstract: This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and ...
Department of Pharmacy, The second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China Objective: To construct a risk prediction model for potentially inappropriate ...
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