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21

Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
Bartz-Beielstein, Thomas

Optimization and Control Machine Learning Applications 90C26 I.2.6; G.1.6
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22

Deep Learning for Recognition of Javanese Batik Patterns.
Mardani, Danis Aditya ; Pranowo ; Santoso, Albertus Joko
AIP Conference Proceedings; 2020, Vol. 2217 Issue 1, p030012-1-030012-7, 7p, 3 Diagrams, 1 Chart, 2 Graphs

UNESCO BATIK ARTIFICIAL neural networ... DEEP learning PATTERN recognition syst... PROGRAMMING languages
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23

Process-Oriented Parallel Programming with an Application to Data-Intensive Computing
Givelberg, Edward

Computer Science - Progr... Computer Science - Distr...
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24

tinie – a software package for electronic transport through two-dimensional cavities in a magnetic field.
Duda, R. ; Keski-Rahkonen, J. ; Solanpää, J. ; et al.
Computer Physics Communications. Jan2022, Vol. 270, pN.PAG-N.PAG. 1p.

ELECTRONIC packaging PYTHON programming langu... MAGNETIC fields INTEGRATED software QUANTUM dots MODULAR construction
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25

The Broadview Radar Altimetry & the GOCE Gravity Mission User Toolboxes.
Benveniste, Jérôme ; Knudsen, Per ; Escolà, Roger ; et al.
Geophysical Research Abstracts. 2019, Vol. 21, p1-1. 1p.

RADAR altimetry PYTHON programming langu... GRAPHICAL user interface... TOOLBOXES OPEN source software APPLICATION program inte...
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27

Distributed Computing with Python
Francesco Pierfederici ; Francesco Pierfederici ; Francesco Pierfederici ; et al.
2016

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28

Distributed Computing with Python
Francesco Pierfederici ; Francesco Pierfederici ; Francesco Pierfederici ; et al.
2016

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30

Distributed Computing with Python
Francesco Pierfederici ; Francesco Pierfederici ; Francesco Pierfederici ; et al.
2016

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31

Distributed Computing with Python
Francesco Pierfederici ; Francesco Pierfederici ; Francesco Pierfederici ; et al.
2016

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33

Python handbook for engineering students
Abdi, Bahareh; Department of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlands ; Abdi, Bahareh; Department of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlands
TU Delft OPEN Books;

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34

Comparison of Multiprocessor and Multi-Threaded Implementations of the Entropy Approach to Impute Gaps in Data in Python
Zemlianyi, Oleksii ; Baibuz, Oleh ; Zemlianyi, Oleksii ; et al.
Challenges and Issues of Modern Science; Vol. 2 (2024): Challenges and Issues of Modern Science; 300-304; 3083-5704; 10.15421/cims.2

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35

Numeric Computation and Statistical Data Analysis on the Java Platform
Chekanov, Sergei V. author. ; SpringerLink (Online service) ; Chekanov, Sergei V. author. ; et al.

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37
38


Naito, Ryuto ; Naito, Ryuto

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39

Computación Evolutiva Descentralizada de Modelo Híbrido usando Blockchain y Prueba de Trabajo de Optimización
Consultor de tesis ; Pabón Burbano, María Constanza ; Bastidas Caicedo, Harvey Demian ; et al.
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Lienig, "A parallel genetic algorithm for performance-driven VLSI routing," IEEE Trans. Evol. Comput, vol. 1, no. 1, pp. 29-39, 1997.; H. Piereval, "Distributed evolutionary algorithms for simulation optimization," IEEE Trans Sys. Man. Cybern. , vol. 30, no. 1, pp. 15-24, 2000.; K.C.Tan, "Automating the drug-sheduling of cancer chemotherapy via evolutionary computation," Artif.Intellig.Med., vol. 25, no. 2, pp. 169-185, 2002.; J. Creput, "Automatic mesh generation for mobile network dimensioning using evolutionary approach," Evol.Comput, vol. 9, no. 1, pp. 18-30, 2005.; J.Liu, "An evolutionary autonomous agents approach to image feature extraction.," IEEE Trans. Evol. Comput., vol. 1, no. 2, pp. 141-158, 1997.; M. Epitropakis, "Hardware-friendly higher-order neural network training using distributed evolutionary algorithms.," Appl. Soft. comput. , vol. 10, no. 2, pp. 398-408, 2010.; L. 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Huang, "A hybrid stock selection model using genetic algorithms and support vector regression," Applied Soft Computing, vol. 12, p. 807–81, 2012.; E. Levy, "Genetic algorithms and deep learning for automatic painter classification," in conference on Genetic and evolutionary computation - GECCO ’14 , New York, New York, USA, 2014.; E. Levy, "Genetic algorithms and deep learning for automatic painter classification," in Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO ’14 , New York, New York, USA, 2014.; S. Whiteson, "Evolutionary Function Approximation for Reinforcement Learning," Journal of Machine Learning Research, vol. 8, pp. 877-917, 2006.; R. B. Greve, "Evolving Neural Turing Machines for Reward-based Learning," in Genetic and Evolutionary Computation Conference (GECCO), Denver, Colorado, USA, 2016.; A. Santoro, "One-shot Learning with Memory-Augmented Neural Networks," Cornell University , Ithaca, NY, USA, 2016.; D. Izzo, "The generalized isalnd model," Studies in Computational Intelligence, vol. 415, pp. 151-169, 2012.; G. Folino, "P-CAGE: An Environment for Evolutionary Computation in Peer-to-Peer Systems," in European Conference on Genetic Programming, Berlin, 2006.; A. L. Ian Scriven, "Decentralised distributed multiple objective particle swarm optimisation using peer to peer networks," in IEEE World Congress on Evolutionary Computation, 2008. CEC 2008, 2008.; A. L. C. Fernando Silva, "odNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers," Evolutionary Computation , vol. 23, no. 3, pp. 421-449, 2015.; D. Jakobović, "ECF - Evolutionary Computation Framework," University of Zagreb,; M. A. García-Sánchez P., "A Methodology to Develop Service Oriented Evolutionary Algorithms," Intelligent Distributed Computing VIII, vol. 570, 2015.; S. Cahon, "ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics," Journal of Heuristics, vol. 10, no. 3, pp. 357-380, 2004.; D. R. White, "Software review: the ECJ toolkit," Genetic Programming and Evolvable Machines, vol. 13, no. 1, pp. 65-67, 2011.; M. G. Arenas, "A Framework for Distributed Evolutionary Algorithms," Lecture Notes on Computer Science, vol. 2439, pp. 665-675, 2002.; J. L. J. Laredo, "Resilience to churn of a peer-to-peer evolutionary algorithm," International Journal of High Performance Systems Architecture, vol. 1, no. 4, pp. 260-268, 2008.; C. Rohrs, "Query Routing for the Gnutella Network," Lime Wire LLC, 2002.; R. Fielding, "Chapter 5: Representational State Transfer (REST)," in Architectural Styles and the Design of Network-based Software Architectures, Irvine, Ca, University of California, 2000.; P. C. R. K. Len Bass, Software Architecture in Practice - Second Edition, Addison Wesley, 2003.; S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash Syste," bitcoin.org, vol. Retrieved 28 April 2016.; H. C. M. Kalodner, "An empirical study of Namecoin and lessons for decentralized namespace design," in Workshop on the oconomics of information security, Delft, The Netherlands, 2015.; D. Carboni, "Feedback based Reputation on top of the Bitcoin Blockchain," Retrieved from; E. Foundation, "Ethereum's white paper," Ethereum Foundation, 2014.; G. N. O. Zyskind, "Decentralizing Privacy: Using Blockchain to Protect Personal Data," IEEE Security and Privacy Workshops , p. 180–184, 2015.; S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System," in Web. bitcoin.org. Retrieved 28 April 2014, 2008.; S. King, "Primecoin: Cryptocurrency with Prime Number Proof-of-Work," 2013 .; A. R. Marshall Ball, "Proofs of Useful Work," Cryptology ePrint Archive, 2017.; R. A. G. Barto, Reinforcement Learning: An Introduction, Cambridge, Massachusetts: The MIT Press, 2005.; M. S. J. 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CHABOUD, "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of the American Finance Association, vol. 69, no. 5, p. 2045–2084 , 201.; M. A. H. Dempster, "An automated FX trading system using adaptive reinforcement learning," Expert Systems with Applications, vol. 30, no. 3, pp. 543-552, 2006.; HistData.com, "Free Forex Historical Data Repository,"; R. T. Fielding, "Chapter 5: Representational State Transfer (REST)," in Architectural Styles and the Design of Network-based Software Architectures, Irvine, University of California, 2000.; H. W. Group, " SOAP: Simple Object Access Protocol," [Online]. Available:; e. a. Don Box, "Simple Object Access Protocol (SOAP) 1.1," W3C, 8 May 2000. [Online]. Available:; J.-R. W. Group, "JSON-RPC 2.0 Specification," JSON-RPC Working Group, 04 01 2013. [Online]. Available:; R. Mikkulainen and J. e. a. Liang, "Evolving Deep Neural Netwokrs," eprint arXiv:1703.00548, 2017.; Reponame:Vitela: Repositorio Institucional PUJ; Instname:Pontificia Universidad Javeriana Cali

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40

First principle implementation of the FDTD method for computationally modeling electrodynamics using C++ and Python
Collin A. Bond ; Andres H. La Rosa
Journal of Physics: Conference Series. 3052:012002

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