| Autor: |
Eugenio Nobile, Maurizio Troiano, Fabio Mangini, Marco Mastrogiuseppe, Jacob Vardi, Fabrizio Frezza, Cecilia Conati Barbaro, Avi Gopher |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-77184-1 |
| Popis: |
Abstract This study was aimed at introducing a new method for predicting the original metrics of fragmented standardized artifacts, specifically of flint blades from the Middle Pre-Pottery Neolithic B (10,200/100–9,500/400 cal B.P.) in the Southern Levant. The excessive re-use of these artifacts or poor preservation conditions often prevent a complete set of metric data from being obtained. Our suggested approach is based on readily accessible machine learning (artificial intelligence) and neural network analysis. These are performed in a multi-paradigm programming language and numeric computing environment, with parameters represented by a rapid measurement system based on the technological features shared by all lithic artifacts of the studied assemblages. This method can be applied to various chronologies and/or contexts. A full set of metric data, including potential typological and functional elements of the assemblages studied, may provide a better understanding of the lithic technology involved; highlight cultural aspects related to the chaîne opératoire of the studied lithic production; and address issues related to cultural sub-divisions in larger-scale applications. Herein, neural network analysis was performed on blade samples from Middle Pre-Pottery Neolithic B sites from the Southern Levant specifically Nahal Yarmuth 38, Motza, Yiftahel, and Nahal Reuel. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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