Results 1 to 10 of about 67,470 (116)

Interpretable machine learning for building energy management: A state-of-the-art review

Advances in Applied Energy, 2023
Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data.
Zhe Chen, Fu Xiao, Fangzhou Guo, Jinyue Yan   +3 more
doaj   +3 more sources

Review on Interpretable Machine Learning in Smart Grid

Energies, 2022
In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field.
Chongchong Xu, Zhicheng Liao, Chaojie Li, Xiaojun Zhou, Renyou Xie   +4 more
doaj   +3 more sources

Review Study of Interpretation Methods for Future Interpretable Machine Learning

IEEE Access, 2020
In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important.
Jian-Xun Mi, An-Di Li, Li-Fang Zhou
doaj   +3 more sources

Interpretable machine learning for shoreline forecasting [PDF]

Scientific Reports
Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in ...
Mahmoud Al Najar, Dennis G. Wilson, Rafael Almar   +2 more
doaj   +2 more sources

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review

Diagnostics, 2022
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of
Yehualashet Megersa Ayano, Friedhelm Schwenker, Bisrat Derebssa Dufera, Taye Girma Debelee   +3 more
doaj   +1 more source

Interpretable machine learning methods for predictions in systems biology from omics data

Frontiers in Molecular Biosciences, 2022
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell.
David Sidak, Jana Schwarzerová, Jana Schwarzerová, Wolfram Weckwerth, Wolfram Weckwerth, Steffen Waldherr   +5 more
doaj   +1 more source

Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture

Computer Methods and Programs in Biomedicine Update, 2023
Background: Machine learning (ML) has demonstrated success in classifying patients’ diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains
Tong Ling, Luo Jake, Jazzmyne Adams, Kristen Osinski, Xiaoyu Liu, David Friedland   +5 more
doaj   +1 more source

R.ROSETTA: an interpretable machine learning framework

BMC Bioinformatics, 2021
Background Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz.
Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer, Patricia Stoll, Susanne Bornelöv, Aleksander Øhrn, Lars Feuk, Jan Komorowski   +8 more
doaj   +1 more source

Ultra-fast interpretable machine-learning potentials

npj Computational Materials, 2023
All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive ...
Stephen R. Xie, Matthias Rupp, Richard G. Hennig   +2 more
doaj   +1 more source

An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology

MethodsX, 2023
Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables.
Mallory Lai, Shaun S. Wulff, Yongtao Cao, Timothy J. Robinson, Rasika Rajapaksha   +4 more
doaj   +1 more source