Result for 24B49E9DDA1FE521B2FD74FF83A43707E8DB9713

Query result

Key Value
FileName./usr/share/applications/weka.desktop
FileSize402
MD59F5A89EECA8A768A8B27A7DE8E0B8970
SHA-124B49E9DDA1FE521B2FD74FF83A43707E8DB9713
SHA-256E47D38DDD961D429F2FD5E83CA00EC69FD04159873F0A218D67E3A05F234A6A8
SSDEEP12:jqMJLMpdxSuwQ4UuoR7JYq1BsDh2Kj9t0uLh:jqMJIpdgw4UlaQsV2elh
TLSHT190E0F1C5175056F5C307D914DE06EFFEAF3A5706C8805404CCC6500D52449C885E3D6C
hashlookup:parent-total3
hashlookup:trust65

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Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5800755460080DBAEE0D3333903BD6E62
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease2.fc13
PackageVersion3.6.2
SHA-1D612A4A0A2BBE2F929EC792AEA9857E9D93C7950
SHA-256A7A8D2BD7EB21B107CC923CC34C8341121180B38DCE0E12565411075212C51A5
Key Value
MD5AC061703A460E873D11A166676578EA0
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease3.fc11
PackageVersion3.6.0
SHA-14C53147CAF0564A176B1EA678D6555E8FB02BEE4
SHA-2566B0197661494866799DA8F0516EBC13207C9B9549C0FECDF3BEA882963B174EA
Key Value
MD5A1904AF3DDF485ACCA318BAB2FEB70DB
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease4.fc12
PackageVersion3.6.0
SHA-19E36553C85D899DB44931A39F61F817BAD9F44D5
SHA-256C6806DD8B7140A3E6A8E0DA308178B1BD14B570F09305B8CDA27670430CC30AB