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

Force Classification01:22

Force Classification

1.3K
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,...
1.3K
Aggregates Classification01:29

Aggregates Classification

356
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...
356
Classification of Signals01:30

Classification of Signals

581
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...
581
Classification of Systems-I01:26

Classification of Systems-I

236
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:
236
Classification of Systems-II01:31

Classification of Systems-II

194
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,
194
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.7K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
56.7K

You might also read

Related Articles

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

Sort by
Same author

Patched-Based Swin Transformer Hyperprior for Learned Image Compression.

Journal of imaging·2026
Same author

Enhancing YOLOv5 for Autonomous Driving: Efficient Attention-Based Object Detection on Edge Devices.

Journal of imaging·2025
Same author

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos.

Sensors (Basel, Switzerland)·2022
Same author

Spatio-Temporal Scale Coded Bag-of-Words.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Aug 5, 2025

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

1.2K

Road-Type Classification with Deep AutoEncoder.

Mohale E Molefe1, Jules R Tapamo1

  • 1School of Engineering, University of KwaZulu Natal, Durban, South Africa.

Computational Intelligence and Neuroscience
|March 24, 2023
PubMed
Summary

This study introduces a deep autoencoder for road network classification, outperforming existing graph embedding methods. The novel approach directly embeds road segment vectors for improved traffic information systems.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Aug 5, 2025

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

1.2K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Intelligent road networks rely on machine learning for safe and efficient traffic management.
  • Classifying road network types is crucial for providing valuable traffic information to users.
  • Existing graph embedding methods often rely on neighboring segments for road segment embedding.

Purpose of the Study:

  • To propose a novel deep autoencoder model for representation learning to classify road network types.
  • To perform embedding directly on road segment vectors, bypassing reliance on neighboring segments.
  • To evaluate the proposed method against state-of-the-art graph embedding techniques.

Main Methods:

  • A deep autoencoder model was developed for representation learning.
  • Each road segment node was represented as a feature vector.
  • Embedding was performed directly on individual road segment vectors.

Main Results:

  • The proposed deep autoencoder model demonstrated superior performance in classifying road network types.
  • It outperformed Graph Convolution Networks, GraphSAGE-MEAN, Graph Attention Networks, and Graph Isomorphism Networks.
  • Comparable performance was achieved with GraphSAGE-MAXPOOL.

Conclusions:

  • The deep autoencoder model offers an effective approach for road network type classification.
  • Direct embedding of road segment vectors is a viable alternative to neighbor-based methods.
  • This research contributes to the advancement of intelligent road network systems through improved traffic information delivery.