Wiese, Lena

[["dc.bibliographiccitation.artnumber","e12465"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Expert Systems"],["dc.bibliographiccitation.volume","37"],["dc.contributor.author","Bahra, Guryash"],["dc.contributor.author","Wiese, Lena"],["dc.date.accessioned","2020-05-25T14:01:00Z"],["dc.date.available","2020-05-25T14:01:00Z"],["dc.date.issued","2019"],["dc.description.abstract","Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set‐up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency."],["dc.identifier.doi","10.1111/exsy.12465"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16837"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65966"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1468-0394"],["dc.relation.issn","0266-4720"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0"],["dc.title","Parameterizing neural networks for disease classification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Cloud Computing"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Wiese, Lena"],["dc.date.accessioned","2019-07-09T11:40:37Z"],["dc.date.available","2019-07-09T11:40:37Z"],["dc.date.issued","2014"],["dc.description.abstract","One feature of cloud storage systems is data fragmentation (or sharding) so that data can be distributed over multiple servers and subqueries can be run in parallel on the fragments. On the other hand, flexible query answering can enable a database system to find related information for a user whose original query cannot be answered exactly. Query generalization is a way to implement flexible query answering on the syntax level. In this paper we study a clustering-based fragmentation for the generalization operator Anti-Instantiation with which related information can be found in distributed data. We use a standard clustering algorithm to derive a semantic fragmentation of data in the database. The database system uses the derived fragments to support an intelligent flexible query answering mechanism that avoids overgeneralization but supports data replication in a distributed database system. We show that the data replication problem can be expressed as a special Bin Packing Problem and can hence be solved by an off-the shelf solver for integer linear programs. We present a prototype system that makes use of a medical taxonomy to determine similarities between medical expressions."],["dc.format.extent","15"],["dc.identifier.doi","10.1186/s13677-014-0018-0"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11159"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58218"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2192-113X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Clustering-based fragmentation and data replication for flexible query answering in distributed databases"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]