Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reinforcement Schedules

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

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

Sort by

Same author

Lightweight Deep Learning Approaches on Edge Devices for Fetal Movement Monitoring.

Biosensors·2025

Same author

An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors.

Sensors (Basel, Switzerland)·2025

Same author

8-port MIMO antenna at 27 GHz for n261 band and exploring for body centric communication.

PloS one·2024

Same author

Investigation of a Circularly Polarized Metasurface Antenna for Hybrid Wireless Applications.

Micromachines·2023

Same author

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

Sensors (Basel, Switzerland)·2023

Same author

A Miniaturized Tri-Band Implantable Antenna for ISM/WMTS/Lower UWB/Wi-Fi Frequencies.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Sep 5, 2025

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

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

Published on: December 15, 2023

A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm.

Nagaiah Mohanan Balamurugan¹, Malaiyalathan Adimoolam², Mohammed H Alsharif³

¹Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

This summary is machine-generated.

This study introduces an enhanced deep reinforcement learning (EDRL) algorithm for superior network traffic analysis and prediction. EDRL significantly improves accuracy and precision in identifying network traffic patterns compared to traditional methods.

Keywords:

deep learning internet traffic machine learning network traffic reinforcement learning traffic prediction

Related Experiment Videos

Last Updated: Sep 5, 2025

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

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

Published on: December 15, 2023

Area of Science:

Computer Science
Artificial Intelligence
Network Engineering

Background:

Network data traffic is rapidly increasing across diverse applications, necessitating advanced analysis techniques.
Accurate network traffic pattern identification is crucial for optimizing Quality of Service (QoS) and managing network resources.
Existing machine and deep learning approaches have limitations in precise network traffic prediction.

Purpose of the Study:

To develop and evaluate a novel Enhanced Deep Reinforcement Learning (EDRL) algorithm for improved network traffic analysis and prediction.
To address network management challenges through intelligent, data-driven traffic forecasting.
To enhance the accuracy and precision of network traffic analysis, reducing false positive and negative rates.

Main Methods:

Implementation of a new Enhanced Deep Reinforcement Learning (EDRL) algorithm for network traffic analysis.
Utilizing Convolutional Neural Network (CNN) and other deep learning algorithms as benchmarks for comparison.
Conducting experiments to evaluate EDRL performance against CNN on various traffic types (text-based, video-based, encrypted/unencrypted data).

Main Results:

The EDRL algorithm demonstrated superior performance over the CNN algorithm in network traffic prediction.
EDRL achieved a mean Accuracy of 97.20% and a mean Precision of 97.343%.
EDRL significantly reduced errors, with a mean false positive rate of 2.657% and a mean false negative rate of 2.527%.

Conclusions:

The proposed EDRL algorithm offers a significant advancement in network traffic analysis and prediction capabilities.
EDRL effectively enhances network management by providing more accurate traffic insights.
This research contributes a robust, intelligent solution to the growing complexities of network data traffic.