Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K
Classification of Systems-I01:26

Classification of Systems-I

545
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
545
Classification of Leukocytes01:30

Classification of Leukocytes

4.9K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
4.9K
Classification of Systems-II01:31

Classification of Systems-II

457
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
457
Aggregates Classification01:29

Aggregates Classification

964
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
964

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Aqueous Carbon Capture Using Guanidinium-Functionalized Hollow Fiber Sorbent Contactors.

JACS Au·2026
Same author

Correction: Curated character of the Initial Upper Palaeolithic lithic artefact assemblages in Bacho Kiro Cave (Bulgaria).

PloS one·2026
Same author

New methods on the block: Taxonomic identification of archaeological bones in resin-embedded sediments through paleoproteomics.

PNAS nexus·2025
Same author

The dichotomy of human decision-making: An experimental assessment of stone tool efficiency.

PloS one·2025
Same author

Performance Degradation of Amine-Infused Fiber Sorbents for Direct Air Capture: Mechanisms and Solutions.

Industrial & engineering chemistry research·2025
Same author

Investigation of Moisture Swing Adsorbents for Direct Air Capture by Dynamic Breakthrough Studies.

ACS sustainable chemistry & engineering·2025

Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Classifying polish in use-wear analysis with convolutional neural networks.

Anastasia Eleftheriadou1, Youssef Djellal2,3, Shannon P McPherron2,4

  • 1Interdisciplinary Center for Archaeology and the Evolution of Human Behaviour (ICArEHB), Universidade do Algarve, Faro, Portugal. aeleftheriadou@ualg.pt.

Scientific Reports
|October 22, 2025
PubMed
Summary

Deep learning models show promise for lithic use-wear analysis, effectively identifying polish from bone and hide. Custom models performed best on smaller surface areas, highlighting the need for larger, varied datasets.

Keywords:
Convolutional neural networksDeep learningExperimental archaeologyLithic use-wear analysisPolish classification

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Related Experiment Videos

Last Updated: Jan 14, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Area of Science:

  • Lithic use-wear analysis
  • Archaeological science
  • Machine learning applications

Background:

  • Lithic use-wear analysis studies tool traces from use and deposition.
  • Polish analysis is crucial but benefits from automation via machine learning.
  • Deep learning's potential in use-wear analysis requires further investigation.

Purpose of the Study:

  • To explore deep learning, specifically convolutional neural networks (CNNs), for lithic use-wear analysis.
  • To determine optimal parameters like surface area size and model architecture.
  • To classify experimental polish based on contact material and use intensity.

Main Methods:

  • Convolutional neural networks (CNNs) were employed to classify experimental polish.
  • Classifications were based on contact materials (wood, hide, bone) and use intensity.
  • Custom and pre-trained CNN models were compared using varying image patch sizes.

Main Results:

  • CNNs showed effectiveness in identifying polish from bone and hide, but less so for wood.
  • Models successfully differentiated polish from short-term versus long-term use.
  • Custom models outperformed pre-trained models, especially with smaller image patches.

Conclusions:

  • Deep learning, particularly CNNs, shows potential for automating lithic use-wear analysis.
  • Optimal results depend on model architecture and the scale of surface area analyzed.
  • Expanding datasets and refining CNN workflows are crucial for future advancements.