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Identification of malicious activities in industrial internet of things based on deep learning models

Journal of information security and applications, 2018-08, Vol.41, p.1-11

2018

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State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

Journal of cleaner production, 2019-10, Vol.234, p.1153-1164

2019

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A variational mode decompoisition approach for analysis and forecasting of economic and financial time series

Expert systems with applications, 2016-08, Vol.55, p.268-273

Lahmiri, Salim

2016

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Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

Industrial & engineering chemistry research, 2020-01, Vol.59 (1), p.464-474

2020

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Solar Irradiance Forecasting Using Deep Neural Networks

Procedia computer science, 2017, Vol.114, p.304-313

2017

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Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

Bioresource technology, 2018-06, Vol.257, p.102-112

2018

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BA-PNN-based methods for power transformer fault diagnosis

Advanced engineering informatics, 2019-01, Vol.39, p.178-185

2019

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Digitale Bildverarbeitung: Grundkurs mit neuronalen Netzen und MATLAB®-Praktikum

Werner, Martin

1. Aufl. 2021 edition., 2020

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Modelling of a post-combustion CO2 capture process using neural networks

Fuel (Guildford), 2015-07, Vol.151, p.156-163

2015

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PEDOT:PSS/QCM-based multimodal humidity and pressure sensor

Sensors and actuators. B, Chemical, 2016-11, Vol.236 (C), p.91-98

2016

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Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Physical chemistry chemical physics : PCCP, 2020-10, Vol.22 (4), p.22889-22899

2020

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Sleep staging using nocturnal sound analysis

Scientific reports, 2018-09, Vol.8 (1), p.13474-14, Article 13474

2018

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Accurate predicting the viscosity of biodiesels and blends using soft computing models

Renewable energy, 2018-05, Vol.120, p.488-500

2018

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Energy futures prices forecasting by novel DPFWR neural network and DS-CID evaluation

Neurocomputing (Amsterdam), 2019-04, Vol.338, p.1-15

2019

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Company Bankruptcy Prediction with Neural Networks

Artificial Intelligence and Soft Computing, p.183-189

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Establishment and analysis of quality art-evaluation model based on BP neural network

Journal of physics. Conference series, 2021-06, Vol.1941 (1), p.12088

Cui, Guoling

2021

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Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

Journal of physics. Conference series, 2021-03, Vol.1811 (1), p.12092

2021

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Building feedforward neural networks with random weights for large scale datasets

Expert systems with applications, 2018-09, Vol.106, p.233-243

2018

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Predicting body fat percentage from anthropometric and laboratory measurements using artificial neural networks

Applied soft computing, 2018-06, Vol.67, p.834-839

2018

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Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

1st ed., 2005

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Neuronale Netze - Grundlagen: Mit Beispielprogrammen in Java

Kaffka, Thomas

1, 2017

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Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm

Resources, conservation and recycling, 2010-04, Vol.54 (6), p.359-363

2010

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Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments

Neural computing & applications, 2021-04, Vol.33 (7), p.2667-2683

2021

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Wind power generation via ground wind station and topographical feedforward neural network (T-FFNN) model for small-scale applications

Journal of cleaner production, 2017-02, Vol.143, p.1246-1259

2017

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Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

Molecular informatics, 2017-01, Vol.36 (1-2), p.np-n/a

2017

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Identification of malicious activities in industrial internet of things based on deep learning models

State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

A variational mode decompoisition approach for analysis and forecasting of economic and financial time series

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

Solar Irradiance Forecasting Using Deep Neural Networks

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

BA-PNN-based methods for power transformer fault diagnosis

Digitale Bildverarbeitung: Grundkurs mit neuronalen Netzen und MATLAB®-Praktikum

Modelling of a post-combustion CO2 capture process using neural networks

PEDOT:PSS/QCM-based multimodal humidity and pressure sensor

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Sleep staging using nocturnal sound analysis

Accurate predicting the viscosity of biodiesels and blends using soft computing models

Energy futures prices forecasting by novel DPFWR neural network and DS-CID evaluation

Company Bankruptcy Prediction with Neural Networks

Establishment and analysis of quality art-evaluation model based on BP neural network

Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

Building feedforward neural networks with random weights for large scale datasets

Predicting body fat percentage from anthropometric and laboratory measurements using artificial neural networks

Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

Neuronale Netze - Grundlagen: Mit Beispielprogrammen in Java

Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm

Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments

Wind power generation via ground wind station and topographical feedforward neural network (T-FFNN) model for small-scale applications

Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

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