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

Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Optimal Foraging00:48

Optimal Foraging

13.8K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.8K
Neural Regulation01:37

Neural Regulation

43.3K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.3K
Optimization Problems01:26

Optimization Problems

62
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
62

You might also read

Related Articles

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

Sort by
Same author

Multi-population Black Hole Algorithm for the problem of data clustering.

PloS one·2023
Same author

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image.

Biomolecules·2022
Same author

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem.

Computational intelligence and neuroscience·2022
Same author

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace.

Sensors (Basel, Switzerland)·2022
Same author

Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer.

Wireless personal communications·2021
Same author

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

Computational intelligence and neuroscience·2020
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.4K

A multi hidden recurrent neural network with a modified grey wolf optimizer.

Tarik A Rashid1,2, Dosti K Abbas3, Yalin K Turel4

  • 1Computer Science and Engineering Department, University of Kurdistan Hewler, Kurdistan, Iraq.

Plos One
|March 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid system using a modified Recurrent Neural Network and Grey Wolf Optimizer to predict university students' academic weaknesses. The novel approach offers improved accuracy for early intervention and enhanced learning outcomes.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
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

Related Experiment Videos

Last Updated: Jan 27, 2026

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.4K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
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

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Machine Learning for Student Success

Background:

  • Identifying student weaknesses is crucial for academic improvement and early intervention.
  • Current systems for identifying student weaknesses lack satisfactory performance.
  • There is a need for dynamic, hybrid systems to effectively predict student outcomes.

Purpose of the Study:

  • To develop and evaluate a novel hybrid system for forecasting university students' academic performance.
  • To enhance the effectiveness of early warning systems for student academic support.
  • To improve faculty instruction and student learning experiences through accurate outcome prediction.

Main Methods:

  • A hybrid system combining a modified Recurrent Neural Network (RNN) with an adapted Grey Wolf Optimizer (GWO) was developed.
  • The system was designed to forecast student outcomes by analyzing academic data.
  • Performance was evaluated against other predictive models.

Main Results:

  • The proposed hybrid system demonstrated superior accuracy in forecasting student outcomes.
  • The modified RNN with adapted GWO outperformed existing models in predictive performance.
  • The findings indicate significant potential for improving educational strategies.

Conclusions:

  • The hybrid RNN-GWO model offers a promising solution for identifying student weaknesses.
  • Accurate prediction of student outcomes can lead to better educational interventions.
  • This approach can significantly enhance the learning experience and academic success of university students.