Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
A survey on missing data in machine learning
Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …
from data and often a problem of missing values is encountered. Missing values occur …
Application of machine learning in groundwater quality modeling-A comprehensive review
Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The
prediction of groundwater pollution due to various chemical components is vital for planning …
prediction of groundwater pollution due to various chemical components is vital for planning …
Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time
S Ayvaz, K Alpay - Expert Systems with Applications, 2021 - Elsevier
In this study, a data driven predictive maintenance system was developed for production
lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the …
lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the …
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …
how the superior performance of ensemble can be distilled into a single model using …
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United
States have served as a visible and important communication channel between the scientific …
States have served as a visible and important communication channel between the scientific …
River water quality index prediction and uncertainty analysis: A comparative study of machine learning models
Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface
water quality. This study introduces a new ensemble machine learning model called Extra …
water quality. This study introduces a new ensemble machine learning model called Extra …
Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries
It is often difficult for a machine learning model trained based on a small size of
charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of …
charge/discharge cycling data to produce satisfactory accuracy in the capacity estimation of …