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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by

Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization.

¹Shanghai Donghai Vocational & Technical College, Shanghai 200241, China.

Computational Intelligence and Neuroscience

|July 11, 2022

Summary

This summary is machine-generated.

This study introduces a data mining and deep learning approach using multichannel convolutional neural networks (MCNN) for predicting student academic performance. It optimizes network topology visualization for better data interaction and real-time data processing.

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Related Experiment Videos

Last Updated: Sep 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Area of Science:

Data Science
Machine Learning
Deep Learning

Background:

Effective data interaction models are crucial for analyzing complex datasets.
Predicting student academic performance requires robust data mining and neural network techniques.
Visualizing network topology in unstable regions presents a significant challenge in model design.

Purpose of the Study:

To design a data interaction process model using data mining and topology visualization.
To apply and optimize a multichannel convolutional neural network (MCNN) for predicting student academic performance.
To develop a novel method for visualizing network topology in unstable regions.

Main Methods:

Data preprocessing including filtering and cleaning.
Application and hyperparameter tuning of a multichannel convolutional neural network (MCNN).
A novel technique transforming network topology layout into a circular topology diffusion problem within a convex polygon.
Construction of a real-time data interaction model using JSON, database triggers, and message queues.

Main Results:

Optimized CNN network topology for improved model performance.
A uniform and aesthetically pleasing network topology layout in specified areas.
A real-time data interaction solution ensuring accuracy, security, and reliability.
Successful prediction of student academic performance using the MCNN model.

Conclusions:

The proposed methods enhance data interaction and model performance in educational data analysis.
The novel topology visualization technique effectively addresses layout challenges in unstable regions.
The real-time data interaction solution meets platform requirements for accuracy, security, and reliability.