Results 211 to 220 of about 57,852 (256)

Electrically Coded Retinomorphic Spectrophotodetector

Advanced Materials, EarlyView.
Self‐powered retinomorphic pyro‐photodetector is demonstrated that avoids machine‐learning post‐processing and covers 365–940 nm. Electrostatic balancing of built‐in potential produces an electrical wavelength code, delivering <3 nm wavelength decoding accuracy with ∼46 µs response.
Mohit Kumar, Hyunmin Dang, Hyungtak Seo
wiley   +1 more source

Interpretable Machine Learning Identifies Hub Biomarkers of Renal Fibrosis and Their Potential Medical Applications. [PDF]

Drug Des Devel Ther
Zhang X, Lv Y, Wang H, Yao R, Du Y, Shi J, Fan J, Yu B, Zheng G.   +8 more
europepmc   +1 more source

Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES. [PDF]

Int J Cardiol Cardiovasc Risk Prev
Lin YT, Lin LY, Chuang KJ.
europepmc   +1 more source

Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning. [PDF]

iScience
Qian M, Li L, Cheng Y, Hou Y, Fu S, Xu Z.   +5 more
europepmc   +1 more source

Interpretable machine learning to predict NOAF in ICU patients with CKD: validation in US and Chinese cohorts. [PDF]

Front Med (Lausanne)
Zhang S, Ren N, Yin Q, Yang C, Nie L, Zhao J, Liu Y, Huang J, Chen Y.   +8 more
europepmc   +1 more source

RETRACTED: Interpretable machine learning framework for predicting Urban air quality. [PDF]

PLoS One
Latif RMA, Iqbal T, Abdel Qader I, Ikram A, Alsolai H, Alabdullah B, Alhayan F, Ghazal TM.   +7 more
europepmc   +1 more source

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Interpretable Machine Learning in Healthcare

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2018
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of
Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert   +2 more
openaire   +1 more source

Interpretable machine learning with reject option

at - Automatisierungstechnik, 2018
Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples.
Brinkrolf, Johannes, Hammer, Barbara
openaire   +2 more sources

Interpretable Machine Learning for Meteorological Data

2021 The 5th International Conference on Machine Learning and Soft Computing, 2021
Weather forecasting is the task to predict the state of the atmosphere in a given location. In the past, the weather forecast has been done through physical models of the atmosphere as a fluid. It becomes the problem of solving sophisticated equations of fluid dynamics.
Ngoan-Thanh Trieu, Bernard Pottier, Vincent Rodin, Hiep Xuan Huynh 0001   +3 more
openaire   +1 more source