Iban Berganzo-Besga
Biographic Data
| ID | 4896704 |
|---|---|
| NAME | Iban Berganzo-Besga |
| GIVEN NAMES | Iban |
| FAMILY NAME | Berganzo-Besga |
| SIGNATURE | BERGANZO-BESGA I |
| AFFILIATIONS | Barcelona Supercomputing Center |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Experts against automation? Comparing artificial intelligence and human identifications of multi-cell cereal husk phytoliths
This study compares the efficacy of a deep learning-based computer vision model against manual expert identification of multi-cell husk phytoliths from wheat ( Triticum boeticum/dicoccoides ), barley ( Hordeum spontaneum ), and oats ( Avena sativa ). Conducted via an online survey in 2024 with twelve respondents (92% completion rate, one respondent did not finish), the survey presented 18 phytolith images selected based on: 1) deep learning perfo…
Theory and practice of artificial intelligence in archaeology
During the last decade, Machine Learning (ML) approaches have been increasingly adapted to archaeology from related fields, supported by the expansion of open repositories, advances in computational infrastructures, and the fast development of detection, classification and segmentation algorithms. However, the multiplication of ML-based papers can give a false impression of methodological efficiency and general applicability, while masking fundam…
Archaeological Classification of Small Datasets Using Meta- and Transfer Learning Methods
In recent years, the increasing application of machine learning (ML) techniques to solve archaeological problems has garnered substantial interest. The relevant issue is that limited-sample, heterogeneous, and non-standard structures of datasets in archaeology affect the quality of deep learning (DL) model efficiency in training and testing. This study explores the effectiveness of artificial neural networks (ANNs) in addressing the unique challe…
Hidden in Plain Sight
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms
Experts against automation? Comparing artificial intelligence and human identifications of multi-cell cereal husk phytoliths
This study compares the efficacy of a deep learning-based computer vision model against manual expert identification of multi-cell husk phytoliths from wheat ( Triticum boeticum/dicoccoides ), barley ( Hordeum spontaneum ), and oats ( Avena sativa ). Conducted via an online survey in 2024 with twelve respondents (92% completion rate, one respondent did not finish), the survey presented 18 phytolith images selected based on: 1) deep learning perfo…
Theory and practice of artificial intelligence in archaeology
During the last decade, Machine Learning (ML) approaches have been increasingly adapted to archaeology from related fields, supported by the expansion of open repositories, advances in computational infrastructures, and the fast development of detection, classification and segmentation algorithms. However, the multiplication of ML-based papers can give a false impression of methodological efficiency and general applicability, while masking fundam…
Archaeological Classification of Small Datasets Using Meta- and Transfer Learning Methods
In recent years, the increasing application of machine learning (ML) techniques to solve archaeological problems has garnered substantial interest. The relevant issue is that limited-sample, heterogeneous, and non-standard structures of datasets in archaeology affect the quality of deep learning (DL) model efficiency in training and testing. This study explores the effectiveness of artificial neural networks (ANNs) in addressing the unique challe…
Hidden in Plain Sight
Archaeology and ancient environmental studies (3 works) · Image Processing and 3D Reconstruction (3 works) · Deep learning (2 works) · Silicon Effects in Agriculture (2 works) · Algorithm (1 work) · Amazonian Archaeology and Ethnohistory (1 work) · Applications of artificial intelligence (1 work) · Archaeological Research and Protection (1 work) · Archaeological theory (1 work) · Artificial Intelligence (1 work)