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

Neural Circuits01:25

Neural Circuits

1.7K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.7K
Neural Regulation01:37

Neural Regulation

40.4K
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.
40.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

152
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
152
Random Variables01:09

Random Variables

13.5K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.5K
Random Sampling Method01:09

Random Sampling Method

12.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
12.6K

You might also read

Related Articles

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

Sort by
Same author

TriDermCancerNet: A hybrid deep learning framework for skin cancer classification.

The Journal of international medical research·2026
Same author

A lightweight vision transformer with context-aware convolution and uniformity normalization for Alzheimer's Disease diagnosis.

Computer methods and programs in biomedicine·2026
Same author

Explainable Deep Reinforcement Learning for Anomaly Detection in IoT-Enabled Metaverse Healthcare: Toward Trustworthy Cyber Threat Intelligence.

Research (Washington, D.C.)·2026
Same author

Impact of sequential traditional Chinese medicine intervention on pregnancy outcomes of intrauterine insemination: A retrospective cohort study.

Journal of integrative medicine·2026
Same author

Blockchain-driven trust management and AI computing for sensor networks optimization.

Scientific reports·2026
Same author

A novel deep semantic- and vision-based self-attention architecture for skin cancer classification.

Digital health·2026

Related Experiment Video

Updated: Sep 22, 2025

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.2K

VISPNN: VGG-inspired Stochastic Pooling Neural Network.

Shui-Hua Wang1, Muhammad Attique Khan2, Yu-Dong Zhang3

  • 1School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, United Kingdom.

Computers, Materials & Continua
|May 26, 2022
PubMed
Summary

A novel artificial intelligence model, VISPNN, accurately recognizes alcoholism. This VGG-inspired neural network with stochastic pooling and advanced data augmentation outperforms existing methods.

Keywords:
VGGalcoholismconvolutional neural networkdeep learningmultiple-way data augmentationstochastic pooling

More Related Videos

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

652
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

530

Related Experiment Videos

Last Updated: Sep 22, 2025

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.2K
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

652
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

530

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Medical Diagnostics

Background:

  • Alcoholism is a significant health issue characterized by dependence on alcohol.
  • Accurate recognition of alcoholism is crucial for timely intervention and treatment.
  • Current diagnostic methods may benefit from advancements in artificial intelligence.

Purpose of the Study:

  • To design and evaluate a novel artificial intelligence model for improved alcoholism recognition.
  • To introduce the VGG-Inspired stochastic pooling neural network (VISPNN) model.
  • To compare VISPNN performance against baseline models and existing state-of-the-art methods.

Main Methods:

  • Development of the VISPNN model, incorporating a VGG-inspired network and stochastic pooling.
  • Implementation of an enhanced 20-way data augmentation technique, including various noise types and image transformations.
  • Ablation studies using two modified networks (Net-I with max pooling, Net-II without data augmentation) for performance analysis.

Main Results:

  • The VISPNN model achieved high performance metrics, including 97.98% sensitivity, 97.80% specificity, and 97.89% accuracy.
  • Cross-validation demonstrated robust results with minimal deviation (±1.11% to ±2.22% across metrics).
  • The area under the curve (AUC) reached 0.9849, indicating excellent discrimination capability.

Conclusions:

  • The VISPNN model significantly outperforms internal networks (Net-I, Net-II) and ten state-of-the-art alcoholism recognition methods.
  • Stochastic pooling and the proposed data augmentation contribute to the superior performance of VISPNN.
  • The developed AI model shows great promise for accurate and efficient alcoholism detection.