The Application of Machine Learning to Archaeology: A Paradigm Shift?
DOI:
https://doi.org/10.51349/veg.2023.1.06Keywords:
Machine Learning, Archaeology, Methodology, Bayesian Networks, Benefits and LimitationsAbstract
Despite initial attempts to apply machine learning to archaeology dating back to the late 1990s, it was not until 2019 that its use began to become widespread. What advantages does this methodology have over previous methods? Can it be applied to all relevant fields of study? This article aims to answer these questions through an exhaustive review of archaeological studies that employ this methodology and by developing a model with a specific algorithm, based on Bayesian networks, to explore its benefits and limitations.
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ABITBOL, R.; SHIMSHONI, I.; BEN-DOV, J. (2021): Machine Learning Based Assembly of Fragments of Ancient Papyrus, Journal on Computing and Cultural Heritage (JOCCH), 14 (3): 1-21.
AGAPIOU, A.; VIONIS, A.; PAPANTONIOU, G. (2021): Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries, Land, 10 (12): 1365.
AHEDO, V.; ZURRO, D.; CARO, J.; GALÁN, J.M. (2021): Let’s go fishing: A quantitative analysis of subsistence choices with a special focus on mixed economies among small-scale societies, PLoS ONE, 16 (8): e0254539.
AHEDO, V.; CARO, J.; BORTOLINI, E.; ZURRO, D.; MADELLA, M.; GALÁN, J.M. (2019): Quantifying the relationship between food sharing practices and socio-ecological variables in small-scale societies: A cross-cultural multi-methodological approach, PLoS ONE, 14 (5): e0216302.
ALBERTI, G. (2014): Modeling group size and scalar stress by logistic regression from an archaeological perspective, PLoS ONE, 9 (3): e91510.
ALBRECHT, C.M.; FISHER, C.; FREITAG, M.; HAMANN, H.F.; PANKANTI, S.; PEZZUTTI, F.; ROSSI, F. (2019): Learning and Recognizing Archeological Features from LiDAR Data, en Proceedings IEEE International Conference on Big Data, Los Angeles: 5630-5636.
ALLOGHANI, M.; AL-JUMEILY, D.; MUSTAFINA, J.; HUSSAIN, A.; ALJAAF, A.J. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science, en M. BERRY; A. MOHAMED y B. YAP (eds.), Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning, Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1
AMMERMAN, A.J.; CAVALLI-SFORZA, L.L. (1979): The wave of advance model for the spread of agriculture in Europe, en C. RENDREW y K.L. COOKE (eds.), Transformations: Mathematical Approaches to Culture Change, Academic Press: 275-293. https://doi.org/10.1016/C2013-0-11358-X
AMMERMAN, A.J.; CAVALLI-SFORZA, L.L. (2014): The Neolithic transition and the genetics of populations in Europe, vol. 836, Princeton University Press.
ANZANO, J.; SANGÜESA, S.; CASAS-GONZÁLEZ, J.; MAGALLÓN, M.Á.; ESCUDERO, M.; ANWAR, J.; SHAFIQUE, U. (2015): Analysis of Roman-Hispanic archaeological ceramics using laser-induced breakdown spectroscopy, Analytical Letters, 48 (10): 1638-1643.
ALTAWEEL, M.; KHELIFI, A.; LI, Z.; SQUITIERI, A.; BASMAJI, T.; GHAZAL, M. (2022): Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results, Remote Sensing, 14 (3): 553.
ARNAY, R.; HERNÁNDEZ-ACEITUNO, J.; MALLOL, C. (2021): Soil micromorphological image classification using deep learning: The porosity parameter, Applied Soft Computing, 102: 107093.
ASSAEL, Y.; SOMMERSCHIELD, T.; SHILLINGFORD, B.; BORDBAR, M.; PAVLOPOULOS, J.; CHATZIPANAGIOTOU, M.; ANDROUTSOPOULOS, I.; PRAG, J.; DE FREITAS, N. (2022): Restoring and attributing ancient texts using deep neural networks, Nature, 603 (7900): 280-283.
ATIENZA, D.; BIELZA, C.; LARRAÑAGA, P. (2022): Semiparametric Bayesian networks, Information Sciences, 584: 564-582. https://doi.org/10.1016/j.ins.2021.10.074
ATKINSON, Q.D.; WHITEHOUSE, H. (2011): The cultural morphospace of ritual form, Evolution and Human Behavior, 32: 50-62. https://doi.org/10.1016/j.evolhumbehav.2010.09.002.
BARCELO, J.A. (2008): Computational intelligence in archaeology, IGI Global, Hershey, New York.
BARCELÓ, J.A.; DEL CASTILLO, M.F.; MAMELI, L. (2015): The probabilities of prehistoric events: a Bayesian network approach, en J.A. BARCELÓ y I. BOGDANOVIC (eds.), Mathematics and Archaeology, CRC Press: 464-484.
BARTON, C.M.; ULLAH, I.I.T.; BERGIN, S.M.; MITASOVA, H.; SARJOUGHIAN, H. (2012): Looking for the future in the past: Long-term change in socioecological systems, Ecological Modelling, 241: 42-53.
BARTON, C.M.; ULLAH, I.; MITASOVA, H. (2010): Computational Modeling and Neolithic Socioecological Dynamics: A Case Study from Southwest Asia, American Antiquity, 75(2): 364-386.
BAUM, T. (2016): Simulating Land Use of Prehistoric Wetland Settlements: Did Excessive Resource Use Necessitate a Highly Dynamic Settlement System?, en J.A. BARCELÓ y F. DEL CASTILLO (eds.), Simulating Prehistoric and Ancient Worlds, Springer International Publishing: Cham: 255-279.
BAUM, T.; NENDEL, C.; JACOMET, S.; COLOBRAN, M.; EBERSBACH, R. (2016): “Slash and burn” or “weed and manure”? A modelling approach to explore hypotheses of late Neolithic crop cultivation in pre-alpine wetland sites, Vegetation History Archaeobotany, 25 (6): 611-27.
BELL, S.; CROSON, C. (1998): Artificial neural networks as a tool for archaeological data analysis, Archaeometry, 40 (1): 139-151.
BERGANZO-BESGA, I.; ORENGO, H.A.; LUMBRERAS, F.; CARRERO-PAZOS, M.; FONTE, J.; VILAS-ESTÉVEZ, B. (2021): Hybrid MSRM-based deep learning and multitemporal sentinel 2-based machine learning algorithm detects near 10k archaeological tumuli in North-Western Iberia, Remote Sensing, 13: 41-81.
BICKLER, S.H. (2021): Machine Learning Arrives in Archaeology, Advances in Archaeological Practice, 9 (2): 186-191.
BICKLER, S.H. (2018): Machine learning identification and classification of historic ceramics, en B. PETCHEY; K. HIL; S. KINASTON y A. KELLY (eds.), Archaeology in New Zealand, New Zealand Archaeological Association, 61 (2): 20-32.
BONHAGE, A.; ELTAHER, M.; RAAB, T.; BREUß, M.; RAAB, A.; SCHNEIDER, A. (2021): A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland, Archaeological Prospection, 28 (2): 177-186.
BRANDSEN, A.; VERBERNE, S.; WANSLEEBEN, M.; LAMBERS, K. (2020): Creating a dataset for named entity recognition in the archaeology domain, en Conference Proceedings LREC 2020, The European Language Resources Association: 4573-4577.
BROZOU, A.; FULLER, B.T.; GRIMES, V.; VAN BIESEN, G.; MA, Y.; BOLDSEN, J.L.; MANNINO, M.A. (2022): Aquatic resource consumption at the Odense leprosarium: Advancing the limits of palaeodiet reconstruction with amino acid δ13C measurements, Journal of Archaeological Science, 141: 105578.
BUCHANAN, B.; WALKER, R.S.; HAMILTON, M.J.; STORY, B.; BEBBER, M.; WILCOX, D.; EREN, M.I. (2022): Experimental assessment of lanceolate projectile point and haft robustness, Journal of Archaeological Science: Reports, 42: 103399.
BUNDZEL, M.; JAŠČUR, M.; KOVÁČ, M.; LIESKOVSKÝ, T.; SINČÁK, P.; TKÁČIK, T. (2020): Semantic segmentation of airborne lidar data in maya archaeology, Remote Sensing, 12: 3685.
BURRY, L.S.; MARCONETTO, B.; SOMOZA, M.; PALACIO, P.; TRIVI, M.; D'ANTONI, H. (2018): Ecosystem modeling using artificial neural networks: An archaeological tool, Journal of Archaeological Science: Reports, 18: 739-746.
BYEON, W.; DOMÍNGUEZ-RODRIGO, M.; ARAMPATZIS, G.; BAQUEDANO, E.; YRAVEDRA, J.; MATÉ-GONZÁLEZ, M.A.; KOUMOUTSAKOS, P. (2019): Automated identification and deep classification of cut marks on bones and its paleoanthropological implications, Journal of Computational Science, 32: 36-43.
CASPARI, G. y CRESPO, P. (2019): Convolutional neural networks for archaeological site detection–Finding “princely” tombs, Journal of Archaeological Science, 110: 104998.
CASTIELLO, M.E.; TONINI, M. (2019): An innovative approach for risk assessment in archaeology based on machine learning. A Swiss case study. Quantitative approaches, spatial statistics and socioecological modelling, en International Colloquium on Digital Archaeology in Bern (DAB), University of Bern, Switzerland.
CHARACTER, L.; ORTIZ JR, A.; BEACH, T.; LUZZADDER-BEACH, S. (2021): Archaeologic machine learning for shipwreck detection using lidar and sonar, Remote Sensing, 13 (9): 1759.
CHEN, F.; ZHOU, R.; VAN DE VOORDE, T.; CHEN, X.; BOURGEOIS, J.; GHEYLE, W.; GOOSSENS, R.; YANG, J.; XU, W. (2021): Automatic detection of burial mounds (kurgans) in the Altai Mountains, ISPRS Journal of Photogrammetry and Remote Sensing, 177: 217-237.
CHETOUANI, A.; TREUILLET, S.; EXBRAYAT, M.; JESSET, S. (2020): Classification of engraved pottery sherds mixing deep-learning features by compact bilinear pooling, Pattern Recognition Letters, 131: 1-7.
CHOBTHAM, K.; CONSTANTINOU, A.C. (2020): Bayesian network structure learning with causal effects in the presence of latent variables, en International Conference on Probabilistic Graphical Models, PMLR: 101-112.
CHOWDHURY, M.P.; CHOUDHURY, K.D.; BOUCHARD, G.P.; RIEL-SALVATORE, J.; NEGRINO, F.; BENAZZI, S.; SLIMAK, L.; FRASIER, B.; SZABO, V.; HARRISON, R.; HAMBRECHT, G.; KITCHENER, A.C.; WOGELIUS; R.A.; BUCKLEY, M. (2021): Machine learning ATR-FTIR spectroscopy data for the screening of collagen for ZooMS analysis and mtDNA in archaeological bone, Journal of Archaeological Science, 126: 105311.
CIFUENTES-ALCOBENDAS, G.; DOMÍNGUEZ-RODRIGO, M. (2019): Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks, Scientific reports, 9 (1): 1-12.
CINTAS, C.; LUCENA, M.; FUERTES, J.M.; DELRIEUX, C.; NAVARRO, P.; GONZÁLEZ-JOSÉ, R.; MOLINOS, M. (2020): Automatic feature extraction and classification of Iberian ceramics based on deep convolutional networks, Journal of Cultural Heritage, 41: 106-112.
CLARIVATE ANALYTICS (2022). Web of Science. [https://www-webofscience-com]
COLMENERO-FERNÁNDEZ, A.; FEITO, F. (2021): Image processing for graphic normalisation of the ceramic profile in archaeological sketches making use of deep neuronal net (DNN), Digital Applications in Archaeology and Cultural Heritage, 22: e00196.
COURTENAY, L.A.; YRAVEDRA, J.; HUGUET, R.; ARAMENDI, J.; MATÉ-GONZÁLEZ, M.Á.; GONZÁLEZ-AGUILERA, D.; ARRIAZA, M.C. (2019): Combining machine learning algorithms and geometric morphometrics: a study of carnivore tooth marks, Palaeogeography, Palaeoclimatology, Palaeoecology, 522: 28-39.
CRAWFORD, K.; DOBBE, R.; DRYER, T.; FRIED, F.; GREEN, B.; KAZIUNAS, E.; KAK, A.; MATHUR, V.; MCELROY, E.; NILL SÁNCHEZ, A.; RAJI, D.; RANKIN, J. L.; RICHARDSON, R.; SCHULTZ, J.; WEST, S.M.; WHITTAKER, M. (2019): AI now 2019 report, AI Now Institute, New York.
DAVIS, D.S. (2020a): Defining what we study: The contribution of machine automation in archaeological research, Digital Applications in Archaeology and Cultural Heritage, 18: e00152.
DAVIS, D.S. (2020b): Studying human responses to environmental change: Trends and trajectories of archaeological research, Environmental Archaeology, 25 (4): 367-380.
DAVIS, D.S.; CASPARI, G.; LIPO, C.P.; SANGER, M.C. (2021): Deep learning reveals extent of Archaic Native American shell-ring building practices, Journal of Archaeological Science, 132: 105433.
DAVIS, D.; DOUGLASS, K. (2021): Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Societies, Frontiers in Ecology and Evolution, 9: 689399. https://doi.org/10.26207/zmsr-tc92.
DESMOND, M. (2014): Relational ethnography, Theory and Society, 43: 547–579.
DHALL, D.; KAUR, R.; JUNEJA, M. (2020): Machine learning: a review of the algorithms and its applications, en Proceedings of ICRIC, 2019: 47-63.
DIA, K.; COLI, V.; BLANC-FÉRAUD, L.; LEBLOND, J.; GOMART, L.; BINDER, D. (2021): Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing, en II International Conference on Deep Learning Theory and Applications, SCITEPRESS-Science and Technology Publications: 109-116.
DOMÍNGUEZ-RODRIGO, M.; BAQUEDANO, E. (2018): Distinguishing butchery cut marks from crocodile bite marks through machine learning methods, Scientific Reports, 8: 5786. https://doi.org/10.1038/s41598-018-24071-1.
DUCKE, B. (2003): Archaeological predictive modelling in intelligent network structures, en Proceedings of the 29th CAA Conference, Loughborough Univeristy: 267-272.
EDEH, M.O.; UGORJI, C.C.; NDUANYA, U.I.; ONYEWUCHI, C.; OHWO, S.O.; IKEDILO, O.E. (2021): Prospects and Limitations of Machine Learning in Computer Science Education Benin Journal of Educational Studies, 27 (1): 48-62.
ELSEVIER. (2004): Scopus [https://www.elsevier.com/scopus]
ENGEL, A.; VAN DEN BROECK, C. (2001): Statistical mechanics of learning, Cambridge University Press.
FABRICIUS TEAM (2022). Arts experiments [https://artsexperiments.withgoogle.com/fabricius/en/about].
FAN, L.; ZHANG, M.; YIN, J.; ZHANG, J. (2022). Impacts of dynamic inspection records on port state control efficiency using Bayesian network analysis, Reliability Engineering & System Safety, 228: 108753. https://doi.org/10.1016/j.ress.2022.108753
FANIEL, I.; KANSA, E.; WHITCHER KANSA, S.; BARRERA-GOMEZ, J.; YAKEL, E. (2013): The challenges of digging data: a study of context in archaeological data reuse, en Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, New York: 295-304.
FERRARO, P.J.; SANCHIRICO, J.N.; SMITH, M.D. (2019): Causal inference in coupled human and natural systems, PNAS, 116: 5311–8. https://doi.org/10.1073/pnas.1805563115.
FORT, J. (2022): Dispersal distances and cultural effects in the spread of the Neolithic along the northern Mediterranean coast, Archaeological and Anthropological Sciences, 14: 153. https://doi.org/10.1007/s12520-022-01619-x
FRIGGENS, M.M.; LOEHMAN, R.A.; CONSTAN, C.I.; KNEIFEL, R.R. (2021): Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA, Fire Ecology, 17 (1): 1-19.
GANDHI, I.R.; PONNAVAIKKO, M. (2020): Copper plate image character recognition system using complex extreme deep learning machine, International Journal of Advanced Science and Technology, 29 (7): 359-268.
GANTLEY, M.; WHITEHOUSE, H.; BOGAARD, A. (2018): Material correlates analysis (MCA): An innovative way of examining questions in archaeology using ethnographic data, Advances in Archaeological Practice, 6: 328–341.
GINAU, A.; STEINIGER, D.; HARTMANN, R.; HARTUNG, U.; SCHIESTL, R.; ALTMEYER, M.; WUNDERLICH, J. (2020): What settlements leave behind—pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning, Palaeogeography, Palaeoclimatology, Palaeoecology, 546: 109666.
GRIMM, V.; RAILSBACK, S.F. (2011): Designing, formulating, and communicating agent-based models, en A.J. HEPPENSTALL; L.M. SEE y M. BATTY (eds.), Agent-based models of geographical Systems, Springer Science & Business Media, Dordrecht: 361-377.
GROVE, M.; BLINKHORN, J. (2020): Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa, PLoS ONE, 15(8): e0237528.
GUALANDI, M.L.; GATTIGLIA, G.; ANICHINI, F. (2021): An Open System for collection and automatic recognition of pottery through Neural Network Algorithms, Heritage, 4 (1): 140-159.
GUYOT, A.; HUBERT-MOY, L.; LORHO, T. (2018): Detecting Neolithic burial mounds from LiDAR-derived elevation data using a multi-scale approach and machine learning techniques, Remote Sensing, 10 (2):225.
GUYOT, A.; LENNON, M.; HUBERT-MOY, L. (2021): Objective comparison of relief visualization techniques with deep CNN for archaeology, Journal of Archaeological Science: Reports, 38: 103027.
HALIASSOS, A.; BARMPOUTIS, P.; STATHAKI, T.; QUIRKE, S.; CONSTANTINIDES, A. (2020): Classification and detection of symbols in ancient papyri, en F. LIAROKAPIS; A. VOULODIMOS; N. DOULAMIS; A. DOULAMIS, Visual computing for cultural heritage, Springer: 121-140.
HANSEN, J.; NEBEL, M. (2020): Prioritizing Archaeological Inventory and Protection with Predictive Probability Models at Glen Canyon National Recreation Area, USA, Journal of Southwestern Anthropology and History, 86 (1): 1-23.
HEIN, I.; ROJAS-DOMÍNGUEZ, A.; ORNELAS, M.; D'ERCOLE, G.; PELOSCHEK, L. (2018): Automated classification of archaeological ceramic materials by means of texture measures, Journal of Archaeological Science: Reports, 21: 921-928.
HLAD, M.; VESELKA, B.; STEADMAN, D.W.; HERREGODS, B.; ELSKENS, M.; ANNAERT, R.; BOUDIN, M.; CAPUZZO, G.; DALLE, S.; DE MULDER, G.; SABAUX, C.; SALESSE, K. SENGELØV, A.; STAMATAKI, E.; VERCAUTEREN, M.; WARMENBOL, E.; TYS, D.; SNOECK, C. (2021): Revisiting metric sex estimation of burnt human remains via supervised learning using a reference collection of modern identified cremated individuals (Knoxville, USA), American Journal of Physical Anthropology, 175 (4): 777-793.
HØJSGAARD, S. (2012): Graphical Independence Networks with the gRain Package for R, Journal of Statistical Software, 46 (10): 1-26.
HORN, C.; IVARSSON, O.; LINDHÉ, C.; POTTER, R.; GREEN, A.; LING, J. (2022): Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images, Journal of Archaeological Method and Theory, 29: 188-213. https://doi.org/10.1007/s10816-021-09518-6
HÖRR, C.; LINDINGER, E.; BRUNNETT, G. (2014): Machine learning based typology development in archaeology, Journal on Computing and Cultural Heritage, 7 (1): 1-23.
HUFFER, D.; GRAHAM, S. (2018): Fleshing out the bones: Studying the human remains trade with Tensorflow and Inception, Journal of Computer Applications in Archaeology, 1 (1): 55-63.
HUGGETT, J. (2018): Reuse Remix Recycle: Repurposing Archaeological Digital Data, Advances in Archaeological Practice, 6 (2): 93-104. https://doi.org/doi:10.1017/aap.2018.1
HYAFIL, A.; BAUMARD, N. (2022): Evoked and Transmitted Culture models: Using bayesian methods to infer the evolution of cultural traits in history, PLoS ONE, 17 (4): e0264509.
ISERN, N.; ZILHÃO, J.; FORT, J.; AMMERMAN, A.J. (2017): Modeling the role of voyaging in the coastal spread of the Early Neolithic in the West Mediterranean, Proceedings of the National Academy of Sciences, 114 (5): 897-902.
JEPPSON, P.L.; MUSCHIO, G.; LEVIN, J. (2019): Computational Science, Convergence Culture, and the Creation of Archaeological Knowledge and Understanding, en J.H. JAMESON y S. MUSTEATA (eds.), Transforming Heritage Practice in the 21st Century, Springer International Publishing: 431-446.
KIRBY, K.R.; GRAY, R.D.; GREENHILL, S.J.; JORDAN, F.M.; GOMES-NG, S.; BIBIKO, H.J.; BLASI, D. E.; BOTERO, C. A.; BOWERN, C.; EMBER, C. R.; LEEHR, D.; LOW, B. S.; MCCARTER, J.; DIVALE, W.; GAVIN, M. C. (2016): D-PLACE: A global database of cultural, linguistic and environmental diversity, PLoS ONE, 11 (7): e0158391.
KOGOU, S.; SHAHTAHMASSEBI, G.; LUCIAN, A.; LIANG, H.; SHUI, B.; ZHANG, W.; SU, B.; VAN SCHAIK, S. (2020): From remote sensing and machine learning to the history of the Silk Road: large scale material identification on wall paintings, Scientific Reports, 10 (1): 1-14.
KOHLER, T.A.; VARIEN, M.D. (2012): Emergence and collapse of early villages: models of Central Mesa Verde archaeology, University of California Press.
KOHONEN, T. (2001): Self-Organizing Maps (Third Edition), Springer, Berlin.
KOLLER, D.; FRIEDMAN, N. (2009): Probabilistic graphical models: principles and techniques, MIT Press.
KLASSEN, S.; WEED, J.; EVANS, D. (2018): Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia, PLoS ONE, 13 (11): e0205649.
LAMBERS, K.; VERSCHOOF-VAN DER VAART, W.B.; BOURGEOIS, Q.P. (2019): Integrating remote sensing, machine learning, and citizen science in Dutch archaeological prospection, Remote Sensing, 11 (7): 794.
LEATHWICK, J.R. (2000): Predictive models of archaeological site distributions in New Zealand, Department of Conservation.
LOTKA, A.J. (1920): Analytical note on certain rhythmic relations in organic systems, Proceedings of the National Academy of Sciences, 6 (7): 410-415.
MA, Y.; GRIMES, V.; VAN BIESEN, G.; SHI, L.; CHEN, K.; MANNINO, M.A.; FULLER, B.T. (2021): Aminoisoscapes and palaeodiet reconstruction: new perspectives on millet-based diets in China using amino acid δ13C values, Journal of Archaeological Science, 125: 105289
MACKENZIE, A. (2017): Machine learners: Archaeology of a data practice, MIT Press.
MACLEOD, N. (2018): The quantitative assessment of archaeological artifact groups: Beyond geometric morphometrics, Quaternary Science Reviews, 201: 319-348.
MARCOT, B.G.; HANEA, A.M. (2021): What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?, Computational Statistics, 36: 2009-2031. https://doi.org/10.1007/s00180-020-00999-9
MESANZA-MORAZA, A.; GARCÍA-GÓMEZ, I.; AZKARATE, A. (2020): Machine learning for the built heritage archaeological study, Journal on Computing and Cultural Heritage, 14 (1): 1-21.
MONNA, F.; MAGAIL, J.; ROLLAND, T.; NAVARRO, N.; WILCZEK, J.; GANTULGA, J.O.; Gantulga, J.-O.; Esin, Y.; Granjon, L.; Allard, A.-C.; Chateau-Smith, C. (2020): Machine learning for rapid mapping of archaeological structures made of dry stones –Example of burial monuments from the Khirgisuur culture, Mongolia–, Journal of Cultural Heritage, 43: 118-128.
MÜLLER, B.; BALBI, S.; BUCHMANN, C.M.; DE SOUSA, L.; DRESSLER, G.; GROENEVELD, J.; WEISE, H. (2014): Standardised and transparent model descriptions for agent-based models: Current status and prospects, Environmental Modelling & Software, 55: 156-163.
NASH, B.S.; Prewitt, E.R. (2016): The use of artificial neural networks in projectile point typology, Lithic Technology, 41 (3): 194-211.
NAVARRO, P.; CINTAS, C.; LUCENA, M.; FUERTES, J.M.; DELRIEUX, C.; MOLINOS, M. (2021): Learning feature representation of Iberian ceramics with automatic classification models, Journal of Cultural Heritage, 48: 65-73.
NAVEGA, D.; COELHO, C.; VICENTE, R.; FERREIRA, M.T.; WASTERLAIN, S.; CUNHA, E. (2015): AncesTrees: ancestry estimation with randomized decision trees, International Journal of Legal Medicine, 129 (5): 1145-1153.
NEAPOLITAN, R.E. (2004): Learning bayesian networks, vol. 38, Pearson Prentice Hall, Upper Saddle River.
NENDEL, C; BERG, M.; KERSEBAUM, K.C.; MIRSCHEL, W.; SPECKA, X.; WEGEHENKEL, M.; WENKEL, K.O.; WIELAND, R. (2011): The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics, Ecological Modelling. 222: 1614–25. https://doi.org/10.1016/j.ecolmodel.2011.02.018.
NETO, E.M.D.F.L.; ALBUQUERQUE, U. (2018): Theories of niche construction and optimal foraging: weaknesses and virtues in understanding the early stages of domestication, Ethnobiology and Conservation, 7: 7.
OONK, S.; SPIJKER, J. (2015): A supervised machine-learning approach towards geochemical predictive modelling in archaeology, Journal of Archaeological Science, 59: 80-88.
ORENGO, H.A.; GARCIA-MOLSOSA, A. (2019): A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery, Journal of Archaeological Science, 112: 105013.
OSTERTAG, C.; BEURTON-AIMAR, M. (2020): Matching ostraca fragments using a siamese neural network, Pattern Recognition Letters, 131: 336-340.
PAGNIN, L; BRUNNBAUER, L.; WIESINGER, R.; LIMBECK, A.; SCHREINER, M. (2020): Multivariate analysis and laser-induced breakdown spectroscopy (LIBS): a new approach for the spatially resolved classification of modern art materials, Analytical and Bioanalytical Chemistry, 412: 3187-3198.
PALACIOS, O.; BARCELÓ, J.A.; DELGADO, R. (2022): Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach, PLoS ONE 17 (10): e0276088. https://doi.org/10.1371/journal.pone.0276088
PARGETER, J.; KHREISHEH, N.; STOUT, D. (2019): Understanding stone tool-making skill acquisition: experimental methods and evolutionary implications, Journal of Human Evolution, 133: 146-166.
PAWLOWICZ, L.M.; DOWNUM, C.E. (2021): Applications of deep learning to decorated ceramic typology and classification: A case study using Tusayan White Ware from Northeast Arizona, Journal of Archaeological Science, 130: 105375.
PEREGRINE, P.N. (1996): Ethnology versus ethnographic analogy: A common confusion in archaeological interpretation, Cross-Cultural Research, 30: 316–329.
PRASOMPHAN, S.; JUNG, J.E. (2017): Mobile application for archaeological site image content retrieval and automated generating image descriptions with neural network, Mobile Networks and Applications, 22 (4): 642-649.
R CORE TEAM. (2022): R: A Language and environment for statistical computing, R Foundation for Statistical Computing, Wien.
RADFORD, J.; JOSEPH, K. (2020): Theory in, theory out: the uses of social theory in machine learning for social science, Frontiers in Big Data, 3: 18. https://www.doi.org/10.3389/fdata.2020.00018
RAMAZZOTTI, M. (2020): Modelling the past: logics, semantics and applications of neural computing in archaeology, Archeologia e Calcolatori, 31 (2): 169-180.
RAMYA, J.; RAJ KUMAR, G.K.; PENIEL, C.J. (2019): ‘Agaram’–Web Application of Tamil Characters Using Convolutional Neural Networks and Machine Learning, en International Conference on Emerging Current Trends in Computing and Expert Technology, Springer, Cham: 670-680.
REICH, J.; STEINER, P.; BALLMER, A.; EMMENEGGER, L.; HOSTETTLER, M.; STÄHELI, C.; NAUMOV, G.; TANESKI, B.; TODOROSKA, V.; SCHINDLER, K.; HAFNER, A. (2021): A novel structure from motion-based approach to underwayter pile field documentation, Journal of Archaeological Science: Reports, 39: 103-120.
Resler, A.; Yeshurun, R.; Natalio, F.; Giryes, R. (2021): A deep-learning model for predictive archaeology and archaeological community detection, Humanities and Social Sciences Communications, 8 (1): 1-10.
RICHARDS-RISSETTO, H.; NEWTON, D.; AL ZADJALI, A. (2021): A 3d Point Cloud Deep Learning Approach Using LIDAR to Identify Ancient Maya Archaeological Sites», en ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing: 133-139.
SANDERS, D.H. (2018): Neural Networks, AI, Phone-Based VR, Machine Learning, Computer Vision and the CUNAT Automated Translation App—Not Your Father's Archaeological Toolkit, en 3rd Digital Heritage International Congress (DigitalHERITAGE), San Francisco: 1-5.
SCUTARI, M.; DENIS, J.B. (2021): Bayesian Networks: With Examples in R, Chapman and Hall/CRC, New York.
SHARAFI, S.; FOULADVAND, S.; SIMPSON, I.; ALVAREZ, J.A.B. (2016): Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran, Journal of Archaeological Science: Reports, 8: 206-215.
SMITH, B.D. (2015): A comparison of niche construction theory and diet breadth models as explanatory frameworks for the initial domestication of plants and animals, Journal of Archaeological Research, 23 (3): 215-262.
SOROUSH, M.; MEHRTASH, A.; KHAZRAEE, E.; UR, J.A. (2020): Deep learning in archaeological remote sensing: Automated qanat detection in the kurdistan region of Iraq, Remote Sensing, 12 (3): 500.
STOTT, D.; KRISTIANSEN, S.M.; SINDBÆK, S.M. (2019): Searching for viking age fortresses with automatic landscape classification and feature detection, Remote Sensing, 11: 1881.
TEWARI, K.; VANDITA, S.; JAIN, S. (2020). Predictive analysis of absenteeism in MNCs using machine learning algorithm, en Proceedings of ICRIC 2019, Springer, Cham: 3-14.
THABENG, O.L.; MERLO, S.; ADAM, E. (2019): High-resolution remote sensing and advanced classification techniques for the prospection of archaeological sites’ markers: The case of dung deposits in the Shashi-Limpopo Confluence area (southern Africa), Journal of Archaeological Science, 102: 48-60.
TRIER, Ø.D.; COWLEY, D.C.; WALDELAND, A.U. (2019): Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland, Archaeological Prospection, 26 (2): 165-175.
TSIGKAS, G.; SFIKAS, G.; PASIALIS, A.; VLACHOPOULOS, A.; NIKOU, C. (2020): Markerless detection of ancient rock carvings in the wild: rock art in Vathy, Astypalaia, Pattern Recognition Letters, 135: 337-345.
ULLAH I.I.T. (2011): A GIS method for assessing the zone of human-environmental impact around archaeological sites: a test case from the Late Neolithic of Wadi Ziqlâb, Jordan, Journal of Archaeological Science, 38 (3): 623-32.
ULLAH, I.I.T.; BERGIN, S.M. (2012): Modeling the consequences of village site location: Least cost path modeling in a coupled GIS and agent-based model of village agropastoralism in eastern Spain, en D.A. WHITE y S.L. SURFACE-EVANS (eds.), Least cost analysis of social landscapes: Archaeological case studies, University of Utah Press: 155-173.
USHIZIMA, D.; XU, K.; MONTEIRO, P.J. (2020): Materials data science for microstructural characterization of archaeological concrete, MRS Advances, 5 (7): 305-318.
VAHDATI, A.R.; WEISSMANN, J.D.; TIMMERMANN, A.; DE LEÓN, M.S.P.; ZOLLIKOFER, C.P. (2019): Drivers of Late Pleistocene human survival and dispersal: an agent-based modeling and machine learning approach, Quaternary Science Reviews, 221: 105867.
VAN ITTERSUM M.K.; EWERT, F.; HECKELEI, T.; WERY, J.; ALKAN OLSSON, J.; ANDERSEN, E.; BEZLEPKINA, I.; BROUWER, F.; DONATELLI, M.; FLICHMAN, G.; OLSSON, L.; RIZZOLI, A. E.; VAN DER WAL, T.; WIEN, J. E.; WOLF, J. (2008): Integrated assessment of agricultural systems–A component-based framework for the European Union (SEAMLESS), Agricultural Systems, 96: 150–165. https://doi.org/10.1016/j.agsy.2007.07.009.
VANVALKENBURGH, P.; DUFTON, J.A. (2020): Big archaeology: Horizons and blindspots, Journal of Field Archaeology, 45: S1-S7. https://doi.org/10.1080/00934690.2020.1714307
VAUGHN, S.; CRAWFORD, T. (2009) A predictive model of archaeological potential: An example from northwestern Belize, Applied Geography, 29 (4): 542-555.
VERSCHOOF-VAN DER VAART, W.B.; LAMBERS, K.; KOWALCZYK, W.; BOURGEOIS, Q.P. (2020): Combining deep learning and location-based ranking for large-scale archaeological prospection of LiDAR data from the Netherlands, ISPRS International Journal of Geo-Information, 9 (5): 293.
VOLTERRA, V. (1926): Fluctuations in the abundance of a species considered mathematically, Nature, 118 (2972): 558-560.
VON BERTALANFFY, L. (1950): An outline of general system theory, British Journal for the Philosophy of Science, 1 (2): 134-165.
VOS, D.; STAFFORD, R.; JENKINS, E.L.; GARRARD, A. (2021): A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data, PLoS ONE, 16 (3): e0248261.
WÄRMLÄNDER, S.K.; VARUL, L.; KOSKINEN, J.; SAAGE, R.; SCHLAGER, S. (2019): Estimating the temperature of heat‐exposed bone via machine learning analysis of SCI color values: A pilot study, Journal of Forensic Sciences, 64 (1): 190-195.
WATTS, J.; JACKSON, J.C.; ARNISON, C.M.; HAMERSLAG, E.M.; SHAVER, J.; PURZYCKI, B.G. (2022): Building Quantitative Cross-Cultural Databases From Ethnographic Records: Promise, Problems and Principles, Cross-Cultural Research, 56: 62–94. https://doi.org/10.1177/10693971211065720.
WILENSKY, U. (1999): NetLogo, Center for Connected Learning and Computer-Based Modeling, Northwestern University.
XU, L.; WANG, X.; WANG, X. (2019): Shipwrecks detection based on deep generation network and transfer learning with small amount of sonar images, en IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), IEEE, Dali: 638-643.
YAWORSKY, P.M.; VERNON, K.B.; SPANGLER, J.D.; BREWER, S.C.; CODDING, B.F. (2020): Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument, PLOS ONE, 15 (10): e0239424.
ZEDER, M.A. (2017): Domestication as a model system for the extended evolutionary synthesis, Interface Focus, 7 (5): 20160133
ZHENG, M.; TANG, W.; OGUNDIRAN, A.; YANG, J. (2020): Spatial simulation modeling of settlement distribution driven by random forest: consideration of landscape visibility, Sustainability, 12 (11): 4748.
ZHU, B.; WANG, X.; CHU, Z.; YANG, Y.; SHI, J. (2019): Active learning for recognition of shipwreck target in side-scan sonar image, Remote Sensing, 11 (3): 243.
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