Application of Chemometrics and Machine Learning in Cultural Heritage Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 7183
Special Issue Editors
Interests: neutron and X-ray techniques for cultural heritage; imaging; diffraction; gamma spectroscopy; FTIR spectroscopy; Raman spectroscopy; XRF spectroscopy; chemometrics; machine learning; instrument development
Special Issues, Collections and Topics in MDPI journals
Interests: data analysis (machine learning, computing, modelling); physics applied: spectroscopy (optical and neutron); analytical and environmental chemistry (air quality, sensors, environmental monitoring); molecular science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cultural Heritage comprises artwork and manufacts often made of multi-component materials in which several variables relating to interlinked chemical, physical, and biological processes are combined. Studying all these parameters in a synergic way provides new perspectives for understanding raw materials and all the phenomena linked with the history and current state of conservation of the objects.
The use of Chemometrics and Machine Learning (ML) techniques opens up new opportunities and challenges in Cultural Heritage (CH) Analysis, allowing us to classify materials, analyse manufacturing processes, and predict damages. To this end, it is key to identify markers or benchmarks able to discriminate between those fingerprints that describe a certain object or phenomenon in its uniqueness while also finding distinct ways to optimize data acquisition and improve the data processing phase.
Supervised learning techniques such as Support Vector Machines (SVM) within CH are still limited, nonetheless, classification and regression techniques have shown unique potential in this domain. Recent developments and research approaches are looking into the identification and study of handcrafted features, detection and recognition of iconographic artworks, pigments classification, determination of maximum firing temperatures of ancient ceramics, optimisation of iterative procedures for large datasets.
We are pleased to invite you to contribute to the present Special Issue on “Application of Chemometrics and Machine Learning in Cultural Heritage Analysis” which aims to offer researchers an opportunity to share findings and new developments in the field of heritage science as well as to present statistical approaches. This Special issue, in particular, also aims to address specific challenges related to data analysis such as pre-processing approaches, new insights in the specific frameworks for CH, application of chemometrics and Machine Learning tools on different matrices/objects as well as to assess the development of new methods and benchmarks in the identification and classification of CH assets.
Dr. Giulia Festa
Dr. Claudia Scatigno
Guest Editors
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Keywords
- chemometrics
- machine learning
- data science for cultural heritage
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