AIMC Topic: Archaeology

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Understanding the spread of agriculture in the Western Mediterranean (6th-3rd millennia BC) with Machine Learning tools.

Nature communications
The first Neolithic farmers arrived in the Western Mediterranean area from the East. They established settlements in coastal areas and over time migrated to new environments, adapting to changing ecological and climatic conditions. While farming prac...

A framework of evolutionary optimized convolutional neural network for classification of shang and chow dynasties bronze decorative patterns.

PloS one
As a UNESCO World Cultural Heritage, the aesthetic value of bronze artifacts from the Shang and Chow Dynasties has had a profound influence on Chinese traditional culture and art. To facilitate the digital preservation and protection of these Shang a...

Unstructured satellite survey detects up to 20% of archaeological sites in coastal valleys of southern Peru.

PloS one
Satellite survey is widely used for archaeological site discovery, but the efficacy of the method has received little systematic investigation. In this analysis, twelve study participants of different experience levels performed an unstructured remot...

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

PloS one
Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifac...

Restoring and attributing ancient texts using deep neural networks.

Nature
Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations. However, over the centuries, many inscriptions have been dama...

A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.

PloS one
The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of differ...

Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications.

Scientific reports
Bone surface modifications are foundational to the correct identification of hominin butchery traces in the archaeological record. Until present, no analytical technique existed that could provide objectivity, high accuracy, and an estimate of probab...

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument.

PloS one
Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lac...

Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa.

PloS one
The Middle to Later Stone Age transition marks a major change in how Late Pleistocene African populations produced and used stone tool kits, but is manifest in various ways, places and times across the continent. Alongside changing patterns of raw ma...