Resonate, the leading provider of predictive consumer intelligence, today announced that its data science team has been recognized with multiple prestigious awards and speaking invitations at some of ...

A new wearable sensor system detects overeating in real time, using video and thermal data to analyze behavioral patterns and ...

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to cleaning ingredients (handling missing values, standardization) Model ...

Abstract: Data imputation (DI) is a common means of enhancing data quality. To adapt to the flourishing field of machine learning (ML), an innovative class of imputation methods that consider ...

Objective: To develop and validate a machine learning (ML)-based prediction model of Bethesda III nodules and create a nomogram based on the best model. Methods: We collected data on patients with ...

Smart cities are challenged by the lack of data, despite the increasing number of sensors and intelligent systems; the issue persists. In a comprehensive review, researchers from Shandong Technology ...

Data were labeled with computable phenotypes in 30 studies, and the most often used method in machine learning models was boosting methods (18 studies). The most common metric used to assess model ...

Chandra Madhumanchi stands out as a pioneering force in the fields of machine learning (ML), artificial intelligence (AI), and data engineering. With over two decades of experience and a reputation ...

The handling of missing data in cognitive diagnostic assessment is an important issue. The Random Forest Threshold Imputation (RFTI) method proposed by You et al. in 2023 is specifically designed for ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research. Both ...