Results 261 to 270 of about 3,042,897 (294)

Citizen Participation and Transport Sustainability: A Novel Fuzzy ZE‐AHP and Fuzzy ZE‐MABAC Model, Evidence From Dublin, Ireland

Sustainable Development, EarlyView.
ABSTRACT Strategic citizen engagement is fundamental to the socio‐technical transition toward sustainable urban mobility. This study investigates the integration of digital participatory tools within the transport planning framework of Dublin, Ireland, to facilitate inclusive, transparent, and equitable governance.
Sarbast Moslem, Brian Caulfield
wiley   +1 more source

Detection of unseen malware threats using generative adversarial networks and deep learning models. [PDF]

Sci Rep
Joshi C, Kumar J, Kumawat G.
europepmc   +1 more source

Dual decoding generative adversarial networks for infrared image enhancement. [PDF]

Sci Rep
Yu Y, Jiang L, Hu Q, Cai Q, Zeng Q, Sun X.   +5 more
europepmc   +1 more source

Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design. [PDF]

Brief Bioinform
Zervou MA, Doutsi E, Pantazis Y, Tsakalides P.   +3 more
europepmc   +1 more source

Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm. [PDF]

Psychometrika
Luo N, Ji F.
europepmc   +1 more source

Auxiliary Discrminator Sequence Generative Adversarial Networks for Few Sample Molecule Generation. [PDF]

J Chem Inf Model
Tang H, Long J, Ji B, Wang J.
europepmc   +1 more source

Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives. [PDF]

Bioengineering (Basel)
Izu-Belloso RM, Ibarrola-Altuna R, Rodriguez-Alonso A.   +2 more
europepmc   +1 more source

Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks.
Anil Bharath, Kailash Arulkumaran, Antonia Creswell   +2 more
exaly   +7 more sources

Evolutionary Generative Adversarial Networks [PDF]

IEEE Transactions on Evolutionary Computation, 2019
© 1997-2012 IEEE. Generative adversarial networks (GANs) have been effective for learning generative models for real-world data. However, accompanied with the generative tasks becoming more and more challenging, existing GANs (GAN and its variants) tend ...
Dacheng Tao, Xin Yao, Chang Xu
exaly   +4 more sources