Alex Brandsen
Biographic Data
| ID | 6451267 |
|---|---|
| NAME | Alex Brandsen |
| GIVEN NAMES | Alex |
| FAMILY NAME | Brandsen |
| SIGNATURE | BRANDSEN A |
| AFFILIATIONS | Leiden University |
| ORCID | 0000-0003-1623-1340 |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 7 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 3 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Recommendation of
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Decision Revise
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Using Text Mining to Search for Neolithic Vlaardingen Culture Sites in the Rhine-Meuse-Scheldt Delta
This paper presents a study on Vlaardingen Culture (3400–2500 BCE) sites in the Rhine-Meuse-Scheldt delta using AGNES, an intelligent search engine for Dutch and Flemish archaeological grey literature. The aims of this paper are twofold: 1) to provide an up-to-date overview of Vlaardingen Culture sites; 2) to evaluate the performance of AGNES in searching for period specific sites. Vlaardingen Culture (VLC) sites usually consist of artefact scatt…
A demonstration of the use and finetuning of existing machine learning tools for analysing large complexes of coins
A significant contribution to the problem of unbalanced data in machine learning research in archaeology
A recommendation of: Sebastian Gampe, Karsten Tolle Creating an Additional Class Layer with Machine Learning to counter Overfitting in an Unbalanced Ancient Coin Dataset https://doi.org/10.5281/zenodo.8298077
A step forward in detecting small objects in UAV data for archaeological surveying
A recommendation of: Sharp Kayeleigh, Christofis Brooklyn, Eslamiat Hossein, Nepal Upesh, Osores Mendives Carlos Machine Learning for UAV and Ground-Captured Imagery: Toward Standard Practices https://doi.org/10.5281/zenodo.8307612
Decision Revise
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Debating AI in Archaeology
Artificial Intelligence (AI) is not a recent development. However, with increasing computational capabilities, AI has developed into Natural Language Processing and Machine Learning, technologies particularly good at detecting correlations and patterns, and categorising, predicting, or extracting information. Within archaeology, AI can process big data accumulated over decades of research and deposited in archives. By combining these capabilities…
A burning question – Using an intelligent grey literature search engine to change our views on early medieval burial practices in the Netherlands
This paper presents a case study on Early Medieval burial practices using AGNES, an intelligent search engine for Dutch archaeological grey literature. Traditionally, it is assumed that cremations phased out at the start of the Early Middle Ages, when the inhumation practice became more numerous. However, recent research (Lippok, 2020) shows that cremations might be more prevalent than previously assumed. Due to research efforts being concentrate…
User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain
In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we ident…
Debating AI in Archaeology
Artificial Intelligence (AI) is not a recent development. However, with increasing computational capabilities, AI has developed into Natural Language Processing and Machine Learning, technologies particularly good at detecting correlations and patterns, and categorising, predicting, or extracting information. Within archaeology, AI can process big data accumulated over decades of research and deposited in archives. By combining these capabilities…
A burning question – Using an intelligent grey literature search engine to change our views on early medieval burial practices in the Netherlands
This paper presents a case study on Early Medieval burial practices using AGNES, an intelligent search engine for Dutch archaeological grey literature. Traditionally, it is assumed that cremations phased out at the start of the Early Middle Ages, when the inhumation practice became more numerous. However, recent research (Lippok, 2020) shows that cremations might be more prevalent than previously assumed. Due to research efforts being concentrate…
User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain
In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we ident…
A burning question – Using an intelligent grey literature search engine to change our views on early medieval burial practices in the Netherlands
This paper presents a case study on Early Medieval burial practices using AGNES, an intelligent search engine for Dutch archaeological grey literature. Traditionally, it is assumed that cremations phased out at the start of the Early Middle Ages, when the inhumation practice became more numerous. However, recent research (Lippok, 2020) shows that cremations might be more prevalent than previously assumed. Due to research efforts being concentrate…
A demonstration of the use and finetuning of existing machine learning tools for analysing large complexes of coins
A significant contribution to the problem of unbalanced data in machine learning research in archaeology
A recommendation of: Sebastian Gampe, Karsten Tolle Creating an Additional Class Layer with Machine Learning to counter Overfitting in an Unbalanced Ancient Coin Dataset https://doi.org/10.5281/zenodo.8298077
A step forward in detecting small objects in UAV data for archaeological surveying
A recommendation of: Sharp Kayeleigh, Christofis Brooklyn, Eslamiat Hossein, Nepal Upesh, Osores Mendives Carlos Machine Learning for UAV and Ground-Captured Imagery: Toward Standard Practices https://doi.org/10.5281/zenodo.8307612
Decision Revise
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Debating AI in Archaeology
Artificial Intelligence (AI) is not a recent development. However, with increasing computational capabilities, AI has developed into Natural Language Processing and Machine Learning, technologies particularly good at detecting correlations and patterns, and categorising, predicting, or extracting information. Within archaeology, AI can process big data accumulated over decades of research and deposited in archives. By combining these capabilities…
Recommendation of
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Decision Revise
A recommendation of: Kazutaka Kawano, Masatoshi Itagaki, Haruhiro Fujita, Ryo Yamamoto, Toshiki Takeuchi, Haruhiko Ochiai Statistical image processing (Decorrelation Stretch) and deep learning (CycleGANs) to restore images of faded artworks https://doi.org/10.5281/zenodo.16888802
Using Text Mining to Search for Neolithic Vlaardingen Culture Sites in the Rhine-Meuse-Scheldt Delta
This paper presents a study on Vlaardingen Culture (3400–2500 BCE) sites in the Rhine-Meuse-Scheldt delta using AGNES, an intelligent search engine for Dutch and Flemish archaeological grey literature. The aims of this paper are twofold: 1) to provide an up-to-date overview of Vlaardingen Culture sites; 2) to evaluate the performance of AGNES in searching for period specific sites. Vlaardingen Culture (VLC) sites usually consist of artefact scatt…
Computer Science (8 works) · Image Processing and 3D Reconstruction (8 works) · Artificial Intelligence (7 works) · Archaeology (6 works) · Archaeological Research and Protection (3 works) · Computer vision (3 works) · Decorrelation (3 works) · Geography (3 works) · History (3 works) · Grey literature (2 works)