Fronk C; Yun J; Singh P; Petzold. L
Bayesian polynomial neural networks and polynomial neural ordinary differential equations. Journal Article
In: arXiv preprint, 2023.
@article{nokey,
title = {Bayesian polynomial neural networks and polynomial neural ordinary differential equations.},
author = {Colby Fronk and Jaewoong Yun and Prashant Singh and Linda Petzold.},
doi = {arXiv:2308.10892},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {arXiv preprint},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Chu J; Singh P; Toor S
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities Conference
2023 IEEE 16th International Conference on Cloud Computing (CLOUD), IEEE IEEE, 2023.
@conference{nokey,
title = {Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities},
author = {Junjie Chu and Prashant Singh and Salman Toor},
url = {https://ieeexplore.ieee.org/abstract/document/10255003},
doi = {https://doi.org/10.1109/CLOUD60044.2023.00065},
year = {2023},
date = {2023-07-02},
urldate = {2023-07-02},
booktitle = {2023 IEEE 16th International Conference on Cloud Computing (CLOUD)},
publisher = {IEEE},
organization = {IEEE},
abstract = {The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.},
keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},
pubstate = {published},
tppubtype = {conference}
}
The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.
Wrede F; Eriksson R; Jiang R; Petzold L; Engblom S; Hellander A; Singh P
Robust and integrative Bayesian neural networks for likelihood-free parameter inference Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE 2022.
@inproceedings{wrede2022robust,
title = {Robust and integrative Bayesian neural networks for likelihood-free parameter inference},
author = {Fredrik Wrede and Robin Eriksson and Richard Jiang and Linda Petzold and Stefan Engblom and Andreas Hellander and Prashant Singh},
url = {https://ieeexplore.ieee.org/abstract/document/9892800
https://arxiv.org/pdf/2102.06521},
doi = {https://doi.org/10.1109/IJCNN55064.2022.9892800},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
pages = {1--10},
organization = {IEEE},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Wrede F; Hellander A
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.
@article{singh2021scalable,
title = {Scalable machine learning-assisted model exploration and inference using Sciope},
author = {Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://academic.oup.com/bioinformatics/article/37/2/279/5876021},
doi = {https://doi.org/10.1093/bioinformatics/btaa673},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {2},
pages = {279--281},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Jiang R M; Wrede F; Singh P; Hellander A; Petzold L R
Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators Journal Article
In: BMC bioinformatics, vol. 22, no. 1, pp. 1–17, 2021.
@article{jiang2021accelerated,
title = {Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators},
author = {Richard M Jiang and Fredrik Wrede and Prashant Singh and Andreas Hellander and Linda R Petzold},
url = {https://link.springer.com/article/10.1186/s12859-021-04255-9},
doi = {https://doi.org/10.1186/s12859-021-04255-9},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BMC bioinformatics},
volume = {22},
number = {1},
pages = {1--17},
publisher = {BioMed Central},
keywords = {Bayesian Inference, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Elamin M M; Toor S
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets Proceedings Article
In: 2020 IEEE Green Technologies Conference (GreenTech), pp. 109–114, IEEE 2020.
@inproceedings{singh2020towards,
title = {Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets},
author = {Prashant Singh and Mona Mohamed Elamin and Salman Toor},
url = {https://ieeexplore.ieee.org/abstract/document/9289758
https://arxiv.org/pdf/2012.09579
},
doi = {https://doi.org/10.1109/GreenTech46478.2020.9289758},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE Green Technologies Conference (GreenTech)},
pages = {109--114},
organization = {IEEE},
keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Vats E; Hast A
Learning surrogate models of document image quality metrics for automated document image processing Proceedings Article
In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 67–72, IEEE 2018.
@inproceedings{singh2018learning,
title = {Learning surrogate models of document image quality metrics for automated document image processing},
author = {Prashant Singh and Ekta Vats and Anders Hast},
url = {https://ieeexplore.ieee.org/abstract/document/8395173
https://arxiv.org/pdf/1712.03738},
doi = {https://doi.org/10.1109/DAS.2018.14},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 13th IAPR International Workshop on Document Analysis Systems (DAS)},
pages = {67--72},
organization = {IEEE},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; van der Herten J; Deschrijver D; Couckuyt I; Dhaene T
A sequential sampling strategy for adaptive classification of computationally expensive data Journal Article
In: Structural and Multidisciplinary Optimization, vol. 55, no. 4, pp. 1425–1438, 2017.
@article{singh2017sequential,
title = {A sequential sampling strategy for adaptive classification of computationally expensive data},
author = {Prashant Singh and Joachim van der Herten and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://link.springer.com/article/10.1007/s00158-016-1584-1
https://users.ugent.be/~didschri/papers/2017_04_Springer_SMO.pdf
},
doi = {https://doi.org/10.1007/s00158-016-1584-1},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Structural and Multidisciplinary Optimization},
volume = {55},
number = {4},
pages = {1425--1438},
publisher = {Springer Berlin Heidelberg},
keywords = {Classification, Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Rossi M; Couckuyt I; Deschrijver D; Rogier H; Dhaene T
Constrained multi-objective antenna design optimization using surrogates Journal Article
In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 30, no. 6, pp. e2248, 2017.
@article{singh2017constrained,
title = {Constrained multi-objective antenna design optimization using surrogates},
author = {Prashant Singh and Marco Rossi and Ivo Couckuyt and Dirk Deschrijver and Hendrik Rogier and Tom Dhaene},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jnm.2248
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2017_11_Wiley_IJNM.pdf},
doi = {https://doi.org/10.1002/jnm.2248},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {International Journal of Numerical Modelling: Electronic Networks, Devices and Fields},
volume = {30},
number = {6},
pages = {e2248},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Couckuyt I; Elsayed K; Deschrijver D; Dhaene T
Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-kriging surrogate models Journal Article
In: Journal of Optimization Theory and Applications, vol. 175, no. 1, pp. 172–193, 2017.
@article{singh2017multi,
title = {Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-kriging surrogate models},
author = {Prashant Singh and Ivo Couckuyt and Khairy Elsayed and Dirk Deschrijver and Tom Dhaene},
url = {https://link.springer.com/article/10.1007/s10957-017-1114-3
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2017_10_Springer_JOTA.pdf},
doi = {https://doi.org/10.1007/s10957-017-1114-3},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Optimization Theory and Applications},
volume = {175},
number = {1},
pages = {172--193},
publisher = {Springer US},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Hellander A
Surrogate assisted model reduction for stochastic biochemical reaction networks Proceedings Article
In: 2017 Winter Simulation Conference (WSC), pp. 1773–1783, IEEE 2017.
@inproceedings{singh2017surrogate,
title = {Surrogate assisted model reduction for stochastic biochemical reaction networks},
author = {Prashant Singh and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/8247915
},
doi = {https://doi.org/10.1109/WSC.2017.8247915},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {2017 Winter Simulation Conference (WSC)},
pages = {1773--1783},
organization = {IEEE},
keywords = {Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Vats E; Hast A; Singh P
Automatic document image binarization using bayesian optimization Proceedings Article
In: Proceedings of the 4th International Workshop on Historical Document Imaging and Processing, pp. 89–94, 2017.
@inproceedings{vats2017automatic,
title = {Automatic document image binarization using bayesian optimization},
author = {Ekta Vats and Anders Hast and Prashant Singh},
url = {https://dl.acm.org/doi/abs/10.1145/3151509.3151520
https://arxiv.org/pdf/1709.01782
},
doi = {https://doi.org/10.1145/3151509.3151520},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Proceedings of the 4th International Workshop on Historical Document Imaging and Processing},
pages = {89--94},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Couckuyt I; Elsayed K; Deschrijver D; Dhaene T
Shape optimization of a cyclone separator using multi-objective surrogate-based optimization Journal Article
In: Applied Mathematical Modelling, vol. 40, no. 5-6, pp. 4248–4259, 2016.
@article{singh2016shape,
title = {Shape optimization of a cyclone separator using multi-objective surrogate-based optimization},
author = {Prashant Singh and Ivo Couckuyt and Khairy Elsayed and Dirk Deschrijver and Tom Dhaene},
url = {https://www.sciencedirect.com/science/article/pii/S0307904X15007210
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2016_03_Elsevier_APM.pdf},
doi = {https://doi.org/10.1016/j.apm.2015.11.007},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Applied Mathematical Modelling},
volume = {40},
number = {5-6},
pages = {4248--4259},
publisher = {Elsevier},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P
Design of experiments for model-based optimization PhD Thesis
Ghent University, 2016.
@phdthesis{singh2016design,
title = {Design of experiments for model-based optimization},
author = {Prashant Singh},
url = {https://biblio.ugent.be/publication/7223691/file/7223692},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
school = {Ghent University},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {phdthesis}
}
Singh P; Claeys T; Vandenbosch G A; Pissoort D
Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning Journal Article
In: IEEE Transactions on Electromagnetic Compatibility, vol. 59, no. 2, pp. 704–709, 2016.
@article{singh2016automated,
title = {Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning},
author = {Prashant Singh and Tim Claeys and Guy AE Vandenbosch and Davy Pissoort},
url = {https://ieeexplore.ieee.org/abstract/document/7776759
https://lirias.kuleuven.be/retrieve/639699},
doi = {https://doi.org/10.1109/TEMC.2016.2632741},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {IEEE Transactions on Electromagnetic Compatibility},
volume = {59},
number = {2},
pages = {704--709},
publisher = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Gong X; Trogh J; Braet Q; Tanghe E; Singh P; Plets D; Hoebeke J; Deschrijver D; Dhaene T; Martens L; others
Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories Journal Article
In: IET Science, Measurement & Technology, vol. 10, no. 4, pp. 375–382, 2016.
@article{gong2016measurement,
title = {Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories},
author = {Xu Gong and Jens Trogh and Quentin Braet and Emmeric Tanghe and Prashant Singh and David Plets and Jeroen Hoebeke and Dirk Deschrijver and Tom Dhaene and Luc Martens and others},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-smt.2015.0213
https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-smt.2015.0213},
doi = {https://doi.org/10.1049/iet-smt.2015.0213},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {IET Science, Measurement & Technology},
volume = {10},
number = {4},
pages = {375--382},
publisher = {The Institution of Engineering and Technology},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Vermeeren G; Singh P; Aerts S; Deschrijver D; Dhaene T; Joseph W; Martens L
Surrogate-based fast peak mass-averaged SAR assessment Proceedings Article
In: Annual Meeting of the Bioelectromagnetics Society and the European BioElectromagnetics Association (BioEM 2015), pp. 135–137, 2015.
@inproceedings{vermeeren2015surrogate,
title = {Surrogate-based fast peak mass-averaged SAR assessment},
author = {Günter Vermeeren and Prashant Singh and Sam Aerts and Dirk Deschrijver and Tom Dhaene and Wout Joseph and Luc Martens},
url = {https://biblio.ugent.be/publication/7257304/file/7257373.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Annual Meeting of the Bioelectromagnetics Society and the European BioElectromagnetics Association (BioEM 2015)},
pages = {135--137},
keywords = {Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Couckuyt I; Ferranti F; Dhaene T
A constrained multi-objective surrogate-based optimization algorithm Proceedings Article
In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3080–3087, IEEE 2014.
@inproceedings{singh2014constrained,
title = {A constrained multi-objective surrogate-based optimization algorithm},
author = {Prashant Singh and Ivo Couckuyt and Francesco Ferranti and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/6900581
https://biblio.ugent.be/publication/5733039/file/5733050.pdf},
doi = {https://doi.org/10.1109/CEC.2014.6900581},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {2014 IEEE Congress on Evolutionary Computation (CEC)},
pages = {3080--3087},
organization = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Ferranti F; Deschrijver D; Couckuyt I; Dhaene T
Classification aided domain reduction for high dimensional optimization Proceedings Article
In: Proceedings of the Winter Simulation Conference 2014, pp. 3928–3939, IEEE 2014.
@inproceedings{singh2014classification,
title = {Classification aided domain reduction for high dimensional optimization},
author = {Prashant Singh and Francesco Ferranti and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/7020218
https://biblio.ugent.be/publication/5955500/file/5955522},
doi = {https://doi.org/10.1109/WSC.2014.7020218},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the Winter Simulation Conference 2014},
pages = {3928--3939},
organization = {IEEE},
keywords = {Classification, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Adaptive classification algorithm for EMC-compliance testing of electronic devices Journal Article
In: Electronics Letters, vol. 49, no. 24, pp. 1526–1528, 2013.
@article{singh2013adaptive,
title = {Adaptive classification algorithm for EMC-compliance testing of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/el.2013.2766
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/el.2013.2766},
doi = {https://doi.org/10.1049/el.2013.2766},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {Electronics Letters},
volume = {49},
number = {24},
pages = {1526--1528},
publisher = {The Institution of Engineering and Technology},
keywords = {Classification, Inverse Problem, Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Deschrijver D; Dhaene T
A balanced sequential design strategy for global surrogate modeling Proceedings Article
In: 2013 Winter Simulations Conference (WSC), pp. 2172–2179, IEEE 2013.
@inproceedings{singh2013balanced,
title = {A balanced sequential design strategy for global surrogate modeling},
author = {Prashant Singh and Dirk Deschrijver and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/6721594
https://biblio.ugent.be/publication/4315532/file/4315539},
doi = {https://doi.org/10.1109/WSC.2013.6721594},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {2013 Winter Simulations Conference (WSC)},
pages = {2172--2179},
organization = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Efficient measurement procedure for hotspot detection in near-field pattern of electronic devices Proceedings Article
In: BESTCOM Meeting, 2013.
@inproceedings{singh2013efficient,
title = {Efficient measurement procedure for hotspot detection in near-field pattern of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://biblio.ugent.be/publication/4083180/file/4083328.pdf},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {BESTCOM Meeting},
keywords = {Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}