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Shadi Shahoud
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2020 – today
- 2023
- [b1]Shadi Shahoud:
Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments. Karlsruhe Institute of Technology, Germany, 2023 - 2022
- [c8]Hatem Khalloof, Sergen Ciftci, Shadi Shahoud, Clemens Düpmeier, Kevin Förderer, Veit Hagenmeyer:
Facilitating the hybridization of parallel evolutionary algorithms in cluster computing environments. GECCO Companion 2022: 2001-2008 - 2021
- [j3]Shadi Shahoud
, Moritz Winter, Hatem Khalloof, Clemens Düpmeier
An extended Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualizationz Technologies. Internet Things 16: 100432 (2021) - [j2]Hatem Khalloof
A generic flexible and scalable framework for hierarchical parallelization of population-based metaheuristics. Internet Things 16: 100433 (2021) - 2020
- [j1]Shadi Shahoud, Sonja Gunnarsdottir, Hatem Khalloof, Clemens Düpmeier, Veit Hagenmeyer:
Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments Using Web and Microservice Technologies. Trans. Large Scale Data Knowl. Centered Syst. 45: 132-171 (2020) - [c7]Shadi Shahoud, Hatem Khalloof, Clemens Düpmeier, Veit Hagenmeyer:
Descriptive Statistics Time-based Meta Features (DSTMF): Constructing a better Set of Meta Features for Model Selection in Energy Time Series Forecasting. APPIS 2020: 25:1-25:6 - [c6]Shadi Shahoud, Hatem Khalloof, Moritz Winter, Clemens Düpmeier, Veit Hagenmeyer:
A Meta Learning Approach for Automating Model Selection in Big Data Environments using Microservice and Container Virtualization Technologies. MEDES 2020: 84-91 - [c5]Hatem Khalloof, Mohammad Mohammad, Shadi Shahoud, Clemens Düpmeier, Veit Hagenmeyer:
A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics. MEDES 2020: 124-131 - [i1]Hatem Khalloof, Wilfried Jakob, Shadi Shahoud, Clemens Düpmeier, Veit Hagenmeyer:
A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics. CoRR abs/2002.07505 (2020)
2010 – 2019
- 2019
- [c4]Hatem Khalloof, Phil Ostheimer, Wilfried Jakob, Shadi Shahoud, Clemens Düpmeier, Veit Hagenmeyer:
Superlinear Speedup of Parallel Population-Based Metaheuristics: A Microservices and Container Virtualization Approach. IDEAL (1) 2019: 386-393 - [c3]Shadi Shahoud, Sonja Gunnarsdottir, Hatem Khalloof, Clemens Düpmeier, Veit Hagenmeyer:
Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments using Web and Microservice Technologies. MEDES 2019: 80-87 - [c2]Hatem Khalloof, Phil Ostheimer, Wilfried Jakob, Shadi Shahoud, Clemens Düpmeier, Veit Hagenmeyer:
A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based Metaheuristics. PPAM (1) 2019: 432-444 - 2018
- [c1]Hatem Khalloof, Wilfried Jakob, Jianlei Liu, Eric Braun, Shadi Shahoud, Clemens Düpmeier, Veit Hagenmeyer:
A generic distributed microservices and container based framework for metaheuristic optimization. GECCO (Companion) 2018: 1363-1370
Coauthor Index
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