The Use of Artificial Neural Networks in Projectile Point Typology

Nash, Brendan; Prewitt, Elton R.

doi:10.1080/01977261.2016.1184876

Cited by 14 publications

(15 citation statements)

References 24 publications

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“…Where such methods have been applied, they have generally been used to search for patterns in 2-dimensional image data derived via remote sensing [49,50] or during material provenance analysis [51]. With the exception of Nash and Prewitt's [52] pioneering study of Texas projectile point typology, we are aware of no other published research using ANNs to classify archaeological lithic assemblages.…”

Section: Plos Onementioning

confidence: 99%

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

Grove

Blinkhorn

2020

PLoS ONE

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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 material use and decreasing artefact sizes, changes in artefact types are commonly employed to differentiate Middle Stone Age (MSA) and Later Stone Age (LSA) assemblages. The current paper employs a quantitative analytical framework based upon the use of neural networks to examine changing constellations of technologies between MSA and LSA assemblages from eastern Africa. Network ensembles were trained to differentiate LSA assemblages from Marine Isotope Stage 3&4 MSA and Marine Isotope Stage 5 MSA assemblages based upon the presence or absence of 16 technologies. Simulations were used to extract significant indicator and contra-indicator technologies for each assemblage class. The trained network ensembles classified over 94% of assemblages correctly, and identified 7 key technologies that significantly distinguish between assemblage classes. These results clarify both temporal changes within the MSA and differences between MSA and LSA assemblages in eastern Africa.

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Section: Plos Onementioning

confidence: 99%

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

Grove

Blinkhorn

2020

PLoS ONE

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“…Moreover, the ability to quantify patterns of morphological variation, even in minor and somewhat idiosyncratic aspects of the morphology, was generally held to be superior to more holistic qualitative "analyses" that relied on the visual inspection of aspects of the morphologies in question, performed by highly trained and experienced morphologists, but which were not susceptible to probabilistic hypothesis testing. With the advent of widespread image digitization, the increase in the power of even modestly priced computer platforms, and most importantly the development of more generalized computer vision-based morphological analysis algorithms, an effective synthesis between the qualitative Gestalt and quantitative morphometric approaches to morphological analysis has now been achieved [30][31][32][33][34][35][36][37]73]. Our results, along with others published recently (e.g., [32,[34][35][36][37]), illustrate how new and generalized approaches to morphological analyses make it possible to assess patterns of variation in any aspect of an organism's morphology, in any sample for which images can be obtained, irrespective of complexity and without forcing the analyst to make a priori decisions regarding which aspects of the morphologies in question to measure, compare, model, or interpret.…”

Section: Discussionmentioning

confidence: 99%

“…In this investigation, we conducted a search for sexually dimorphic form differences using a small sample of modern gray wolf crania collected from northern Israel and the Golan Heights (henceforth “Israeli wolves”) for the purpose of determining whether recent developments in the field of geometric morphometrics and computer vision (e.g., [ 30 – 33 ]) could support more exploratory and confirmatory approaches to the analysis of sexual dimorphism in carnivore skeletons. Previous applications of this approach have proved useful in social media [ 34 ] and the scientific fields of entomology, where they have, in part, been employed to discover an unexpectedly strong set of sexually dimorphic differences in the morphology of fly wings [ 35 ], in the study of Mullerian mimicry in butterflies [ 36 ], and in archeology where they have been used to assess both temporal [ 32 ] and regional geographic differences [ 37 ] in the forms of lithic artifacts.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania

MacLeod

Horwitz

2020

BMC Biol

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Background: Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. Results: Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sexmediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shapebased sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. Conclusion: Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses.

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“…Age group distribution was as follows: most respondents (47) fall into age group 15-30 years old, 25 respondents are between 30 and 45 years old, eleven were over 45 years old, the rest were under 15 years old. The education distribution of the respondents is as follows: primary school (9), middle school (30), high school (11) and university education (24). The last two questions in the general section of the survey were about TV watching habits.…”

Section: Methodsmentioning

confidence: 99%

“…Lee [29] defined class sensitivity and specificity for multiclass classification problem. Average class sensitivity/specificity is then used as classification quality measure [30]. ROC AUC plots (the true positive versus false positive rate) as the classifier's performance measure also have to be generalized for multiclass problems [31].…”

Section: Methodsmentioning

confidence: 99%

Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

2019

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In today's hyper-connected world, rich social data feeds can be obtained from various sources, including the data exhaust of many commonly used systems. In this paper, we analyze the social pulse, obtained from viewer activity in an IPTV network-we attempt to validate a framework for determining public opinion and public interest through implicit feedback of IPTV viewers. First, we address the hypothesis that implicit viewer feedback in the form of channel change events, paired with the content metadata, can be used to model viewers' opinion and interest. For this, we design a controlled experiment to collect explicit feedback by rating a set of general-interest news clips. In addition to collecting demographic information, we also survey viewers' opinion, interest, and the probability of channel change during each clip. Furthermore, we extract weighted feature vectors from the closed captions of the video; this data, combined with the reported probability of channel change, is used to build a model that classifies opinion in five categories based on the probability of channel change and content. Next, we build a simplified model that classifies opinion in five categories based on the interest, which shows a linear relationship, but further consideration of content, in this case, provides better accuracy and possibility to analyze anomalous cases. Finally, we discuss and analyze the applications of such models in large systems and the necessary modifications to scale the system and to ensure the adequate performance on massive IPTV event data streams. INDEX TERMS Implicit user feedback, opinion modeling, interest modeling, viewership profiling, IPTV.

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The Use of Artificial Neural Networks in Projectile Point Typology

Cited by 14 publications

References 24 publications

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

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

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania

Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

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