Artificial neural networks for water quality soft-sensing in wastewater treatment: a review
This paper aims to present a comprehensive survey on water quality soft-sensing of a
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We …
Deep learning applications for hyperspectral imaging: a systematic review
A Ozdemir, K Polat - Journal of the Institute of Electronics and …, 2020 - iecscience.org
Since the acquisition of digital images, scientific studies on these images have been making
significant progress. The sizes and quality of the images obtained have increased greatly …
significant progress. The sizes and quality of the images obtained have increased greatly …
A distributed framework for large-scale protein-protein interaction data analysis and prediction using mapreduce
Protein-protein interactions are of great significance for human to understand the functional
mechanisms of proteins. With the rapid development of high-throughput genomic …
mechanisms of proteins. With the rapid development of high-throughput genomic …
Error compensation of industrial robot based on deep belief network and error similarity
With the advantages of the high degree of freedom and large action space, industrial robots
are gradually widely used in high-end large-scale equipment automatic assembly fields …
are gradually widely used in high-end large-scale equipment automatic assembly fields …
Deep learning-based model predictive control for continuous stirred-tank reactor system
A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment
processes. Its control is a challenging industrial-process-control problem due to great …
processes. Its control is a challenging industrial-process-control problem due to great …
LSTM-MPC: A deep learning based predictive control method for multimode process control
Modern industrial processes often operate under different modes, which brings challenges
to model predictive control (MPC). Recently, most MPC related methods would establish …
to model predictive control (MPC). Recently, most MPC related methods would establish …
[HTML][HTML] A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images
B Gülmez - Annals of Operations Research, 2023 - Springer
The coronavirus first appeared in China in 2019, and the World Health Organization (WHO)
named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March …
named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March …
Sentence-level classification using parallel fuzzy deep learning classifier
F Es-Sabery, A Hair, J Qadir, B Sainz-De-Abajo… - IEEE …, 2021 - ieeexplore.ieee.org
At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and
Twitter, users honestly communicate their opinions and ideas about events, services, and …
Twitter, users honestly communicate their opinions and ideas about events, services, and …
Event-driven model predictive control with deep learning for wastewater treatment process
Wastewater treatment processes (WWTPs) have been considered as complex control
problems, because effluent water standard, stability and multioperational conditions need to …
problems, because effluent water standard, stability and multioperational conditions need to …
An efficient self-organizing deep fuzzy neural network for nonlinear system modeling
G Wang, J Qiao - IEEE Transactions on Fuzzy Systems, 2021 - ieeexplore.ieee.org
A fuzzy neural network (FNN) is an effective learning system that combines neural network
and fuzzy logic, which has achieved great success in nonlinear system modeling. However …
and fuzzy logic, which has achieved great success in nonlinear system modeling. However …