Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
BMJ

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
InfoQ

Why Most Machine Learning Projects Fail to Reach Production

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
IEEE

Hybrid Tree-Based Machine Learning Models for State-of-Charge and Core Temperature Estimation in EV Batteries

Abstract: Accurate estimation of State-of-Charge (SoC) and core temperature is fundamental to optimizing the performance, safety, and longevity of Lithium-Ion Batteries (LiBs), particularly in ...
Frontiers

Machine learning-based estimation of trunk fat percentage and its association with cardiometabolic risk leveraging two large national cohorts

Obesity represents a critical global public health challenge, with its prevalence among adults having risen markedly over the past decade (1). A key limitation in the field is the reliance on ...
MIT Technology Review

Yann LeCun’s new venture is a contrarian bet against large language models

In an exclusive interview, the AI pioneer shares his plans for his new Paris-based company, AMI Labs. Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian ...
Physics World

Physics-based battery model parameterization from impedance data

Electrochemical impedance spectroscopy (EIS) provides valuable insights into the physical processes within batteries – but how can these measurements directly inform physics-based models? In this ...
Gothamist

How much are you paying for a NYC Christmas tree in this economy?

Gothamist is tracking how much New Yorkers are paying for their Christmas trees — and we want to hear from you. Fill out this form and share the price of your evergreen. Although paying nearly $100 ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

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 ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...
Gizmodo

‘Imagine a Cube Floating in the Air’: The New AI Dream Allegedly Driving Yann LeCun Away from Meta

One of the most important AI scientists in Big Tech wants to scrap the current approach to building human-level AI. What we need, Yann LeCun has indicated, are not large language models, but “world ...