| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A data-driven spatially-specific vaccine allocation framework for COVID-19
|
Hong, Zhaofu
|
|
|
339 |
1-2 |
p. 203-226
|
artikel
|
| 2 |
A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning
|
Wang, Jiguang
|
|
|
339 |
1-2 |
p. 427-453
|
artikel
|
| 3 |
A deep learning approach to improve built asset operations and disaster management in critical events: an integrative simulation model for quicker decision making
|
Galera-Zarco, Carlos
|
|
|
339 |
1-2 |
p. 573-612
|
artikel
|
| 4 |
A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem
|
Kosanoglu, Fuat
|
|
|
339 |
1-2 |
p. 79-110
|
artikel
|
| 5 |
A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model
|
Zhao, Wenhui
|
|
|
339 |
1-2 |
p. 227-259
|
artikel
|
| 6 |
An improved transformer model with multi-head attention and attention to attention for low-carbon multi-depot vehicle routing problem
|
Zou, Yang
|
|
|
339 |
1-2 |
p. 517-536
|
artikel
|
| 7 |
Automatic MILP solver configuration by learning problem similarities
|
Hosny, Abdelrahman
|
|
|
339 |
1-2 |
p. 909-936
|
artikel
|
| 8 |
Collaborative truck multi-drone delivery system considering drone scheduling and en route operations
|
Thomas, Teena
|
|
|
339 |
1-2 |
p. 693-739
|
artikel
|
| 9 |
Complex networks and deep learning for copper flow across countries
|
Federico, Lorenzo
|
|
|
339 |
1-2 |
p. 937-963
|
artikel
|
| 10 |
Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing
|
Choi, Jeongsub
|
|
|
339 |
1-2 |
p. 185-201
|
artikel
|
| 11 |
COVID-19 vaccine hesitancy: a social media analysis using deep learning
|
Nyawa, Serge
|
|
|
339 |
1-2 |
p. 477-515
|
artikel
|
| 12 |
Data-driven optimization models for inventory and financing decisions in online retailing platforms
|
Yang, Bingnan
|
|
|
339 |
1-2 |
p. 741-764
|
artikel
|
| 13 |
Deep learning applications in operations research
|
Kumar, Ajay
|
|
|
339 |
1-2 |
p. 1-2
|
artikel
|
| 14 |
Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey
|
Abdel-Basset, Mohamed
|
|
|
339 |
1-2 |
p. 3-51
|
artikel
|
| 15 |
Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales
|
Efat, Md. Iftekharul Alam
|
|
|
339 |
1-2 |
p. 297-328
|
artikel
|
| 16 |
Does the energy sector serve as a hedge and safe haven?
|
Azad, A. S. M. Sohel
|
|
|
339 |
1-2 |
p. 369-395
|
artikel
|
| 17 |
Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network
|
Lee, Sangho
|
|
|
339 |
1-2 |
p. 813-833
|
artikel
|
| 18 |
End-to-end risk budgeting portfolio optimization with neural networks
|
Uysal, A. Sinem
|
|
|
339 |
1-2 |
p. 397-426
|
artikel
|
| 19 |
Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits
|
La Torre, Davide
|
|
|
339 |
1-2 |
p. 455-475
|
artikel
|
| 20 |
Exploiting time-varying RFM measures for customer churn prediction with deep neural networks
|
Mena, Gary
|
|
|
339 |
1-2 |
p. 765-787
|
artikel
|
| 21 |
Forecasting duty-free shopping demand with multisource data: a deep learning approach
|
Zhang, Dong
|
|
|
339 |
1-2 |
p. 861-887
|
artikel
|
| 22 |
Great partners: how deep learning and blockchain help improve business operations together
|
Luo, Suyuan
|
|
|
339 |
1-2 |
p. 53-78
|
artikel
|
| 23 |
High-dimensional stochastic control models for newsvendor problems and deep learning resolution
|
Ma, Jingtang
|
|
|
339 |
1-2 |
p. 789-811
|
artikel
|
| 24 |
Identifying purchase intention through deep learning: analyzing the Q &D text of an E-Commerce platform
|
Ma, Jing
|
|
|
339 |
1-2 |
p. 329-348
|
artikel
|
| 25 |
IDILIM: incident detection included linear management using connected autonomous vehicles
|
Gokasar, Ilgin
|
|
|
339 |
1-2 |
p. 889-908
|
artikel
|
| 26 |
Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks
|
Boustani, Noureddine
|
|
|
339 |
1-2 |
p. 613-630
|
artikel
|
| 27 |
Incorporating causality in energy consumption forecasting using deep neural networks
|
Sharma, Kshitij
|
|
|
339 |
1-2 |
p. 537-572
|
artikel
|
| 28 |
Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach
|
Yang, Nan
|
|
|
339 |
1-2 |
p. 631-662
|
artikel
|
| 29 |
Intermittent demand forecasting with transformer neural networks
|
Zhang, G. Peter
|
|
|
339 |
1-2 |
p. 1051-1072
|
artikel
|
| 30 |
Management of resource sharing in emergency response using data-driven analytics
|
Zhang, Jifan
|
|
|
339 |
1-2 |
p. 663-692
|
artikel
|
| 31 |
Multichannel convolution neural network for gas mixture classification
|
Oh, YongKyung
|
|
|
339 |
1-2 |
p. 261-295
|
artikel
|
| 32 |
Optimizing inland container shipping through reinforcement learning
|
Tomljenovic, Vid
|
|
|
339 |
1-2 |
p. 1025-1050
|
artikel
|
| 33 |
Role of land use in China’s urban energy consumption: based on a deep clustering network and decomposition analysis
|
Fan, Wei
|
|
|
339 |
1-2 |
p. 835-859
|
artikel
|
| 34 |
Root cause analysis of manufacturing variation from optical scanning data
|
Bui, Anh Tuan
|
|
|
339 |
1-2 |
p. 111-130
|
artikel
|
| 35 |
The third party logistics provider freight management problem: a framework and deep reinforcement learning approach
|
Abbasi-Pooya, Amin
|
|
|
339 |
1-2 |
p. 965-1024
|
artikel
|
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