Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Omer Kaspi"'
Publikováno v:
Journal of Cheminformatics, Vol 9, Iss 1, Pp 1-15 (2017)
Abstract An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive
Externí odkaz:
https://doaj.org/article/61e154fc91284dcd94accda3886feda8
Autor:
Omer Kaspi, Osnat Israelsohn-Azulay, Zidon Yigal, Hila Rosengarten, Matea Krmpotić, Sabrina Gouasmia, Iva Bogdanović Radović, Pasi Jalkanen, Anna Liski, Kenichiro Mizohata, Jyrki Räisänen, Zsolt Kasztovszky, Ildikó Harsányi, Raghunath Acharya, Pradeep K. Pujari, Molnár Mihály, Mihaly Braun, Nahum Shabi, Olga Girshevitz, Hanoch Senderowitz
Publikováno v:
Journal of Chemical Information and Modeling. 63:87-100
Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by us
Autor:
Malkeet Singh Bahia, Omer Kaspi, Meir Touitou, Idan Binayev, Seema Dhail, Jacob Spiegel, Netaly Khazanov, Abraham Yosipof, Hanoch Senderowitz
Publikováno v:
Molecular Informatics. 42
QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e.g., for modeling ligand-pro
Publikováno v:
Talanta. 234
This paper presents a structured workflow for glass fragment analysis based on a combination of Elemental Analysis using PIXE and Machine Learning tools, with the ultimate goal of standardizing and helping forensic efforts. The proposed workflow was
Autor:
Omer Kaspi, Osnat Israelsohn-Azulay, Yigal Zidon, Hila Rosengarten, Matea Krmpotić, Sabrina Gouasmia, Iva Bogdanović Radović, Pasi Jalkanen, Anna Liski, Kenichiro Mizohata, Jyrki Räisänen, Olga Girshevitz, Hanoch Senderowitz
Publikováno v:
Forensic Science International. 333:111216
The International Atomic Energy Agency (IAEA) has coordinated a research project titled ”Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensics Sciences” (CRP F11021) with the aim of empowering accelerator and reactor based techni
Publikováno v:
Journal of Cheminformatics, Vol 9, Iss 1, Pp 1-15 (2017)
Journal of Cheminformatics
Journal of Cheminformatics
An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions ISBN: 9783030304928
ICANN (Workshop)
ICANN (Workshop)
Material informatics is engaged with the application of informatics tools, frequently in the form of machine learning algorithms, to gain insight into structure properties relationships of materials and to design new materials with desired properties
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87aa5a8b5d46796135a54227637215af
https://doi.org/10.1007/978-3-030-30493-5_70
https://doi.org/10.1007/978-3-030-30493-5_70
Publikováno v:
Journal of chemical information and modeling. 58(12)
Visualizing high-dimensional data by projecting them into a two- or three-dimensional space is a popular approach in many scientific fields, including computer-aided drug design and cheminformatics. In contrast, dimensionality reduction techniques ha
Publikováno v:
Molecular Informatics. 35:622-628
Material informatics may provide meaningful insights and powerful predictions for the development of new and efficient Metal Oxide (MO) based solar cells. The main objective of this paper is to establish the usefulness of data reduction and visualiza
Publikováno v:
Molecular informatics. 37(9-10)
This work describes the integration of several data mining and machine learning tools for researching Photovoltaic (PV) solar cells libraries into a unified workflow embedded within a GUI-supported Decision Support System (DSS), named PV Analyzer. Th