scholarly journals Author Correction: Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manuel Domínguez‑Rodrigo ◽  
Gabriel Cifuentes‑Alcobendas ◽  
Blanca Jiménez‑García ◽  
Natalia Abellán ◽  
Marcos Pizarro‑Monzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Surface Modifications ◽  
Bone Surface ◽  
Ancient Bone

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Domínguez-Rodrigo ◽  
Gabriel Cifuentes-Alcobendas ◽  
Blanca Jiménez-García ◽  
Natalia Abellán ◽  
Marcos Pizarro-Monzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Surface Modifications ◽  
High Accuracy ◽  
Correct Identification ◽  
Archaeological Record ◽  
Bone Surface ◽  
Analytical Technique ◽  
Ancient Bone

Abstract 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 probability in the identification of multiple structurally-similar and dissimilar marks. Here, we present a major methodological breakthrough that incorporates these three elements using Artificial Intelligence (AI) through computer vision techniques, based on convolutional neural networks. This method, when applied to controlled experimental marks on bones, yielded the highest rate documented to date of accurate classification (92%) of cut, tooth and trampling marks. After testing this method experimentally, it was applied to published images of some important traces purportedly indicating a very ancient hominin presence in Africa, America and Europe. The preliminary results are supportive of interpretations of ancient butchery in some places, but not in others, and suggest that new analyses of these controversial marks should be done following the protocol described here to confirm or disprove these archaeological interpretations.

scholarly journals Deep classification of cut-marks on bones from Arroyo del Vizcaíno (Uruguay)

2021 ◽  
Vol 288 (1954) ◽  
pp. 20210711
Author(s):  
Manuel Domínguez-Rodrigo ◽  
Enrique Baquedano ◽  
Luciano Varela ◽  
P. Sebastián Tambusso ◽  
María Julia Melián ◽  
...  
Keyword(s):  
Surface Modifications ◽  
Stone Tools ◽  
Bone Surface ◽  
Faunal Remains ◽  
American Continent ◽  
Cut Marks ◽  
Learning Techniques ◽  
The Last Glacial Maximum ◽  
The Last Glacial

The earliest widely accepted presence of humans in America dates to approximately 17.5 cal kyr BP, at the end of the Last Glacial Maximum (LGM). Among other evidence, this presence is attested by stone tools and associated cut-marks and other bone surface modifications (BSM), interpreted as the result of the consumption of animals by humans. Claims of an older human presence in the continent have been made based on the proposed anthropogenic modification of faunal remains; however, these have been controversial due to the highly subjective nature of the interpretations. Here, we employ advanced deep learning algorithms to objectively increase the accuracy of BSM identification on bones. With several models that exhibit BSM classification accuracies greater than 94%, we use ensemble learning techniques to robustly classify a selected sample of BSM from the approximately 30 kyr BP site of Arroyo del Vizcaíno, Uruguay. Our results confidently show the presence of cut-marks imparted by stone tools on bones at the site. This result supports an earlier presence of humans in the American continent, expanding additional genetic and archaeological evidence of a human LGM and pre-LGM presence in the continent.

scholarly journals Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

2020 ◽  
Vol 17 (168) ◽  
pp. 20200446 ◽  
Author(s):  
Blanca Jiménez-García ◽  
José Aznarte ◽  
Natalia Abellán ◽  
Enrique Baquedano ◽  
Manuel Domínguez-Rodrigo
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Surface Modifications ◽  
High Accuracy ◽  
Dental Morphology ◽  
Bone Surface ◽  
Bone Accumulation ◽  
Tooth Marks

Taphonomists have long struggled with identifying carnivore agency in bone accumulation and modification. Now that several taphonomic techniques allow identifying carnivore modification of bones, a next step involves determining carnivore type. This is of utmost importance to determine which carnivores were preying on and competing with hominins and what types of interaction existed among them during prehistory. Computer vision techniques using deep architectures of convolutional neural networks (CNN) have enabled significantly higher resolution in the identification of bone surface modifications (BSM) than previous methods. Here, we apply these techniques to test the hypothesis that different carnivores create specific BSM that can enable their identification. To make differentiation more challenging, we selected two types of carnivores (lions and jaguars) that belong to the same mammal family and have similar dental morphology. We hypothesize that if two similar carnivores can be identified by the BSM they imprint on bones, then two more distinctive carnivores (e.g. hyenids and felids) should be more easily distinguished. The CNN method used here shows that tooth scores from both types of felids can be successfully classified with an accuracy greater than 82%. The first hypothesis was successfully tested. The next step will be to differentiate diverse carnivore types involving a wider range of carnivore-made BSM. The present study demonstrates that resolution increases when combining two different disciplines (taphonomy and artificial intelligence computing) in order to test new hypotheses that could not be addressed with traditional taphonomic methods.

scholarly journals Classification of artificial intelligence technologies to determine the civil liability

2021 ◽  
Vol 1794 (1) ◽  
pp. 012001
Author(s):  
A Alekseev ◽  
O Erakhtina ◽  
K Kondratyeva ◽  
T Nikitin
Keyword(s):  
Artificial Intelligence ◽  
Civil Liability

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

scholarly journals PATH-46. AUTOMATED HISTOPATHOLOGIC CLASSIFICATION OF BRAIN TUMORS USING ARTIFICIAL INTELLIGENCE

2017 ◽  
Vol 19 (suppl_6) ◽  
pp. vi181-vi181
Author(s):  
Quin Xie ◽  
Dominick Han ◽  
Kevin Faust ◽  
Kenneth Aldape ◽  
Gelareh Zadeh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Brain Tumors
Sign in / Sign up

Export Citation Format

Share Document