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 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 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.

Implementation of Mail Classification Using Neural Networks of the Second Type Spline Weight Functions

2014 ◽  
Vol 513-517 ◽  
pp. 687-690 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Lei Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recall Rate ◽  
High Accuracy ◽  
Weight Functions ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Sorting Process ◽  
E Mail

How to effectively filter out spam is a topic worthy of further study for the growing proliferation of spam. The main purpose of this paper is to apply a new neural network algorithm to the classification of spam. In this paper, we introduce a second type of spline weight function neural network algorithm, as well as e-mail feature extraction and vectorization, and then introduced the mail sorting process. Experiments show that it can get a relatively high accuracy and recall rate on the spam classification. Therefore, with this new algorithm, we can achieve better classification results.

scholarly journals Diagnosis of Plant Diseases using Artificial Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 1464-1467
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Plant Diseases ◽  
Economic Losses ◽  
Fruit Crops ◽  
Agricultural Business ◽  
Complex Foundation

Pomegranate is one of India's most commonly cultivated fruit crops. manual expert observations are being used to detect leaf diseases that take longer time for further prevention. Fruit diseases are causing devastating disadvantages in worldwide agricultural business economic losses in production .in this journal, the answer is proposed and valid by experiment for the identification and classification of fruit disorders. The objective of proposed work is to analyze the illness utilizing picture preparing and artificial intelligence techniques on pictures of pomegranate plant leaf. In the proposed framework, pomegranate leaf picture with complex foundation is taken as input. Then pomegranate leaf ailment division is finished utilizing K-means clustering. The infected segment from portioned pictures is recognized. Best results have been seen when neural networks with a RBFN is used for a classification.

scholarly journals A NEW APPROACH TO ASSESSING THE OPERATIONAL SAFETY OF COMPOSITE MATERIALS AND PARTS OF COMPLEX STRUCTURES BASED ON ARTIFICIAL INTELLIGENCE METHODS AS A PART OF NEURAL NETWORKS AND DEEP RESULTS OF MULTI-CRITERIA COMPLEX NON-DESTRUCTIVE TESTING

2020 ◽  
pp. 18-27
Author(s):  
D. A. Akimov ◽  
A. D. Kleymenov ◽  
S. O. Kozelskaya ◽  
O. N. Budadin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Complex Structures ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
New Approach ◽  
Heterogeneous Information ◽  
Operational Safety ◽  
Non Destructive

The article proposes a new approach to assessing the operational safety of materials and parts of complex structures based on artificial intelligence methods based on artificial neural networks and multi-criteria complex non-destructive testing, and special mathematical and algorithmic support for systems for evaluating operational safety and predicting residual life under external influences. A method of morphological analysis of the procedures for using measurement tools for heterogeneous information with different a priori information, both about the type of characteristics and the distribution of errors in the input and output signals, has been developed. The classification of problems of measuring parameters for the integration of heterogeneous information is proposed. A macromodel of error is obtained that can be used for research purposes to minimize errors in the developed equipment or for the purpose of correcting errors during operation. A classification of methods for measuring heterogeneous information from the standpoint of probability distribution theory is proposed. Experimental testing of developed algorithms tailored aggregation of information non-destructive testing and adaptation to poorly formalized parameters, which confirmed the effectiveness of the developed methods and algorithms for assessment of structures and resource forecasting their operational reliability was carried out.

scholarly journals On the (Im)Practicality of Adversarial Perturbation for Image Privacy

2021 ◽  
Vol 2021 (1) ◽  
pp. 85-106
Author(s):  
Arezoo Rajabi ◽  
Rakesh B. Bobba ◽  
Mike Rosulek ◽  
Charles V. Wright ◽  
Wu-chi Feng
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Family Members ◽  
High Accuracy ◽  
Automated Recognition ◽  
Privacy Concerns ◽  
Classification Of Images

AbstractImage hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy.Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation-based approaches as a privacy defense against automated face recognition. Specifically, we first identify practical requirements for such approaches and then propose two practical adversarial perturbation approaches – (i) learned universal ensemble perturbations (UEP), and (ii) k-randomized transparent image overlays (k-RTIO) that are semantic adversarial perturbations. We demonstrate how users can generate effective transferable perturbations under realistic assumptions with less effort.We evaluate the proposed methods against state-of-theart online and offline face recognition models, Clarifai.com and DeepFace, respectively. Our findings show that UEP and k-RTIO respectively achieve more than 85% and 90% success against face recognition models. Additionally, we explore potential countermeasures that classifiers can use to thwart the proposed defenses. Particularly, we demonstrate one effective countermeasure against UEP.

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.

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