Review of ML and AutoML solutions to forecast time-series data
Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …
of the same variable are analyzed to predict the future values of the time series. Its …
Research on disease prediction based on improved DeepFM and IoMT
Z Yu, SU Amin, M Alhussein, Z Lv - IEEE Access, 2021 - ieeexplore.ieee.org
In recent years, with the increase of computer computing power, Deep Learning has begun
to be favored. Its learning of non-linear feature combinations has played a role that …
to be favored. Its learning of non-linear feature combinations has played a role that …
DNN model development of biogas production from an anaerobic wastewater treatment plant using Bayesian hyperparameter optimization
H Sadoune, R Rihani, FS Marra - Chemical Engineering Journal, 2023 - Elsevier
Deep neural networks have been regarded as accurate models to predict complex
fermentation processes due to their capacity to learn from a high number of data sets via …
fermentation processes due to their capacity to learn from a high number of data sets via …
Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms
Flood is considered as the most devastating natural hazards that cause the death of many
lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland …
lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland …
Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19
The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for
being able to deal with a large volume of data from a time series with nonlinearities. As a …
being able to deal with a large volume of data from a time series with nonlinearities. As a …
Bushfire management strategies: current practice, technological advancement and challenges
Bushfires are classified as catastrophic disasters capable of inflicting significant destruction.
The key detrimental consequences of bushfires include the loss of human lives, trauma …
The key detrimental consequences of bushfires include the loss of human lives, trauma …
Impact of hyperparameters on deep learning model for customer churn prediction in telecommunication sector
A Dalli - Mathematical Problems in Engineering, 2022 - Wiley Online Library
In this paper, in order to predict a customer churn in the telecommunication sector, we have
analysed several published articles that had used machine learning (ML) techniques …
analysed several published articles that had used machine learning (ML) techniques …
A multi-variate heart disease optimization and recognition framework
Cardiovascular diseases (CVD) are the most widely spread diseases all over the world
among the common chronic diseases. CVD represents one of the main causes of morbidity …
among the common chronic diseases. CVD represents one of the main causes of morbidity …
Tool wear monitoring using a novel parallel BiLSTM model with multi-domain features for robotic milling Al7050-T7451 workpiece
K Zhang, D Zhou, C Zhou, B Hu, G Li, X Liu… - The International Journal …, 2023 - Springer
Industrial robots have great potential to machine large parts. However, the vibration or
chattering induced by their inherent weak stiffness can easily damage or break the tool …
chattering induced by their inherent weak stiffness can easily damage or break the tool …
Deep neural network for multi‐class classification of medicinal plant leaves
Plant diseases are a critical issue in the farming industry, and early identification is essential
for plant monitoring. The leaves of plants represent the majority of disease symptoms …
for plant monitoring. The leaves of plants represent the majority of disease symptoms …