Result for 1E6258E555AE15CCD34D724606ABFF0701006B34

Query result

Key	Value
CRC32	3155F7D1
FileName	CHANGELOG-3-3-5
FileSize	26175
MD5	BC41E3C968FEC99E828C65E5E55343E4
OpSystemCode	{'MfgCode': '1006', 'OpSystemCode': '362', 'OpSystemName': 'TBD', 'OpSystemVersion': 'none'}
ProductCode	{'ApplicationType': 'Data', 'Language': 'English', 'MfgCode': '80467', 'OpSystemCode': '884', 'ProductCode': '13571', 'ProductName': 'Weka', 'ProductVersion': '3.6'}
SHA-1	1E6258E555AE15CCD34D724606ABFF0701006B34
SHA-256	D46102C625FD29FCA892900876A8ECC6DEF42E09F17FD5EEE122FE3D1FB422E0
SSDEEP	384:6k3HnZftDBh0rKtMBiaTarg1amd168vFjTERR32:T3HnZftDgrKSia2rg1amdljTERRG
SpecialCode
TLSH	T1C0C26314DF43AE3139A746B115AE978924B3413EEF34B29E36F4800F850661CB7F5AE9
db	nsrl_modern_rds
insert-timestamp	1646990068.9255476
source	NSRL
hashlookup:parent-total	31
hashlookup:trust	100

Network graph view

Parents (Total: 31)

The searched file hash is included in 31 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	AF0A80E55F0A2DD9AF82748F486D8147
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	5.fc15
PackageVersion	3.6.2
SHA-1	05C95A1E4C199FFD0AF520272D66C5C259D5C690
SHA-256	7032BCD9F1F36009185E154D702650590D725E2F634F34CA12F61B7868F22BDA

Key	Value
MD5	FB0FDC148881D2057ED6FD3B3B86138D
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	1.fc18
PackageVersion	3.6.8
SHA-1	0B8CB68B514D3C4025099E0DB6A462AAED99D026
SHA-256	3D35131B632B13C0EEE5EE12D6A525DE6C4F90FEF6ED1F3839056C2AA131FB6A

Key	Value
MD5	6CC4BB080A104D66E2BD639683C06D63
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	6.fc17
PackageVersion	3.6.2
SHA-1	1E29FADF4F5D8F231B0A3A4B67F1025E40B3D15B
SHA-256	2904DDA4EF487E5F2E7BCA470D978EAE0E90DEA812ADE60D3BB368A43EC0BCDC

Key	Value
MD5	59655A970FA1B2742F2C7F6971433C89
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	5.fc15
PackageVersion	3.6.2
SHA-1	2737AAE6672EE0E774A4945C9559956FE8D22B98
SHA-256	FE1E281FD02276E615A9A2E09039F8FC5915E4BE721AA0C18BC9C13CCA242496

Key	Value
MD5	3E729A70F089D3A2D172340F3C5596CE
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	1.fc18
PackageVersion	3.6.8
SHA-1	38CE31307DA5279F71EEB195D526690BA3B04754
SHA-256	88FD166B8C2DDA537315061B89D2E28612147FB37D805110A0F6F829CFC1069C

Key	Value
MD5	5C59A92767C1A29798188DA0EA0BF204
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	4.fc21
PackageVersion	3.6.8
SHA-1	3B0CF102EBA4973CCD9581B0EDDBC5F3452E36F7
SHA-256	9C1B0DBE449D56866C95B290A7B1DB976F0CC1F5D6B3D893A64920AFECC378FA

Key	Value
MD5	F8ECCCB2C60182DEE962C708AD1428D3
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	5.fc23
PackageVersion	3.6.8
SHA-1	490362BB050D4F4F4099C962EC3664270397157F
SHA-256	0B77E79ACB6EB167DDE70BDD25D8EABA1BF085BCFCD510A45476E0A30E02FB5E

Key	Value
MD5	AC061703A460E873D11A166676578EA0
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	3.fc11
PackageVersion	3.6.0
SHA-1	4C53147CAF0564A176B1EA678D6555E8FB02BEE4
SHA-256	6B0197661494866799DA8F0516EBC13207C9B9549C0FECDF3BEA882963B174EA

Key	Value
MD5	DEE0416F1504D1F2BEB0692C852E1F9F
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	5.fc15
PackageVersion	3.6.2
SHA-1	4EE479F4FB9A53BF7B807DE5DD4832B71F176447
SHA-256	8D2F96F8829D83D255163B6632245FB882E2A27793DCB6348C0BDB546011FD6B

Key	Value
MD5	FFE5F74D9B859E0DE0FD6CE84F8ABB78
PackageArch	noarch
PackageDescription	Weka 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.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	4.fc21
PackageVersion	3.6.8
SHA-1	51D4E7F1F480CF75BF26A21EEC6735A477EA8C58
SHA-256	9163B9492801A833EB068B42725FE300FB9994DE3BAA416F71890B51C5A76C5B