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

Aggregates Classification01:29

Aggregates Classification

1.2K
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...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

712
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:
712
Survival Tree01:19

Survival Tree

504
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
504
Classification of Systems-II01:31

Classification of Systems-II

585
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,
585
Force Classification01:22

Force Classification

2.7K
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,...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation.

Computers in biology and medicine·2026
Same author

Impact of simulated glasses noise on facial emotion recognition with deep learning models.

Scientific reports·2026
Same author

Design and implementation of a hybrid machine learning framework for predicting heart rate status.

Scientific reports·2026
Same author

Multi-task procedural content generation with reinforcement learning.

Scientific reports·2026
Same author

A deep generative approach to personalized super mario level design.

Scientific reports·2026
Same author

Developing appropriateness criteria for arterial catheters in adult intensive care unit patients.

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses·2026
Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026
Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026
Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026
Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026
Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026
Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026
See all related articles

Related Experiment Videos

A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter

Kavyan Maleknya1, Roohallah Alizadehsani2, Siamak Pedrammehr3

  • 1Department of Civil and Environmental Engineering at Politecnico di Milano, Milan, Italy.

Scientific Reports
|April 19, 2026
PubMed
Summary

No abstract available in PubMed .

Keywords:
Active learningData augmentationDeep reinforcement learningHyperparameter optimizationSolid waste management

Related Experiment Videos