Hanna Geppert
According to our database1,
Hanna Geppert
authored at least 18 papers
between 2008 and 2024.
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Bibliography
2024
Proceedings of the 32nd IEEE International Requirements Engineering Conference, 2024
2023
Inf. Softw. Technol., July, 2023
A comprehensive modeling approach for the task mapping problem in heterogeneous systems with dataflow processing units.
Concurr. Comput. Pract. Exp., 2023
ILLOD Replication Package: An Open-Source Framework for Abbreviation-Expansion Pair Detection and Term Consolidation in Requirements.
Proceedings of the 31st IEEE International Requirements Engineering Conference, 2023
2022
Proceedings of the Requirements Engineering: Foundation for Software Quality, 2022
Proceedings of the Euro-Par 2022: Parallel Processing Workshops, 2022
2019
Proceedings of the Analysis of Experimental Algorithms - Special Event, 2019
Proceedings of the Joint Proceedings of REFSQ-2019 Workshops, 2019
2018
Proceedings of the International Conference on Military Communications and Information Systems, 2018
2017
Proceedings of the International Conference on Military Communications and Information Systems, 2017
2011
Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential.
J. Chem. Inf. Model., 2011
2010
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation.
J. Chem. Inf. Model., 2010
2009
Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects.
J. Chem. Inf. Model., 2009
Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors.
J. Chem. Inf. Model., 2009
J. Chem. Inf. Model., 2009
Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors.
J. Chem. Inf. Model., 2009
2008
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds.
J. Chem. Inf. Model., 2008