Results 211 to 220 of about 142,585 (248)

New idtracker.ai rethinks multi-animal tracking as a representation learning problem to increase accuracy and reduce tracking time. [PDF]

Elife
Torrents J, Costa T, de Polavieja G.
europepmc   +1 more source

MolQuery: Prediction of Lipid Synthesizability Using Active Learning. [PDF]

ACS Omega
Broadbent J, Vymětal J, Moayedpour S, Bailey M, Riahi S, Balsubramani A, Mikochik P, Even L, Gunaganti N, Dasari R, Karmakar S, Deng H, Agarwal V, Bar-Joseph Z, Jager S.   +14 more
europepmc   +1 more source

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning. [PDF]

Nat Commun
Venkatachalam AS, Greenman L, Stallbaumer J, Rudenko A, Rolles D, Lam HVS.   +5 more
europepmc   +1 more source

Integrative transcriptomics and peptidomics approach reveals unexpectedly diverse endogenous secretory peptides in Odorrana grahami frog skin. [PDF]

BMC Biol
Liu J, Zhu W, Chang L, Zhao T, Zhang M, Wang B, Xie F, Jiang J.   +7 more
europepmc   +1 more source

Large-Scale File Fragment Classification via Multi-View Learning

Samuel Hildebrand
openalex   +1 more source

Byte embeddings for file fragment classification

Future Generation Computer Systems, 2021
Abstract In digital forensics, file carving is the process of recovering files on a storage media in part or in whole without any file system information. An important problem in file carving is the identification of fragment types. Many fragment classification studies in the literature employ inflexible and indiscernible feature selection methods ...
Md Enamul Haque, Mehmet Engin Tozal
openalex   +2 more sources

GenSpec: A File Fragment Classification Approach

2020 International Conference on Computing and Information Technology (ICCIT-1441), 2020
Collection and analysis of data are at the heart of digital forensics. However, in real-life situations, the data of interest is often found in files that were partially erased or otherwise tampered with. Consequently, identification of the file types of those fragmented files and the data format of the text contained in each file are deemed necessary.
Mohammed Abdulaziz Alsubhi, Ahmed Nour Moussa, Abdullatif Saleh Alfaqiri, Firas Layth Khaleel   +3 more
openalex   +2 more sources

Neural Networks for File Fragment Classification

2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019
Abstract - File fragment classification is an important step in file forensics in which filetypes are assumed based on their available content fragments. Methods typically used for this task utilize machine learning techniques on features like byte frequency distributions and fragment entropy measures.
Kristijan Vulinović, Lucija Ivković, Juraj Petrović, Kristian Skračić, Predrag Pale   +4 more
openalex   +3 more sources

File Fragment Classification with Focus on OLE and OOXML classes

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 2020
Classification of file fragments is a crucial step in digital forensics and determining file types based on available data fragments. Currently explored file fragment classification methods other than forensic hand-examination rely on machine learning techniques. Those methods most commonly use features based on byte frequency distribution as inputs in
Kristian Skračić, Filip Rukavina, K. Milicic, Juraj Petrović, Predrag Pale   +4 more
openalex   +4 more sources