Jove
Visualize
Contact Us

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.4K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.4K
Convolution Properties II01:17

Convolution Properties II

215
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
215
Convolution Properties I01:20

Convolution Properties I

160
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
160
Survival Tree01:19

Survival Tree

93
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...
93
Layers of Connective Tissue Proper01:21

Layers of Connective Tissue Proper

1.9K
Fascia, a thin layer of fibrous connective tissue, is distributed throughout the body. It demarcates and forms a supportive covering over skeletal muscles, bones, blood vessels, and organs. There are three main types of facia— superficial fascia, deep fascia, and subserous fascia. These are all present at different depths in the body. Fascia reduces the friction and permits muscles, joints, and organs to easily slide against each other, facilitating movement of the body and preventing...
1.9K
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

222
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
222

You might also read

Related Articles

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

Sort by
Same author

Spatial temporal fusion based features for enhanced remote sensing change detection.

Scientific reports·2025
Same author

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.

Frontiers in genetics·2023
Same author

Certificateless pairing-free authentication scheme for wireless body area network in healthcare management system.

Journal of medical engineering & technology·2020
Same author

A Review of Current Patient Matching Techniques.

Studies in health technology and informatics·2017
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 Experiment Video

Updated: Jul 14, 2025

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

568

Improved transfer learning using textural features conflation and dynamically fine-tuned layers.

Raphael Ngigi Wanjiku1, Lawrence Nderu2, Michael Kimwele2

  • 1Nexford University, Washington DC, United States.

Peerj. Computer Science
|October 9, 2023
PubMed
Summary

This study improves transfer learning by selecting relevant data points and model layers based on textural features and weights. This approach enhances model accuracy and reduces trial-and-error in fine-tuning pre-trained models.

Keywords:
Deep learningDomain adaptationFeature extractionFine-tuning layersLayer selectionPre-trained modelsSource taskTarget taskTransfer learning

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

605

Related Experiment Videos

Last Updated: Jul 14, 2025

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

568
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

605

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transfer learning leverages existing knowledge for new tasks, but effectiveness diminishes with task dissimilarity.
  • Selecting relevant data and model layers is crucial for successful knowledge transfer.
  • Previous methods focused on data or layer selection independently.

Purpose of the Study:

  • To develop a unified model pipeline for transfer learning by combining data and layer selection strategies.
  • To minimize knowledge loss during transfer learning by utilizing least divergent textural features and model layers.
  • To enhance the accuracy and efficiency of fine-tuning pre-trained models.

Main Methods:

  • Utilized five pre-trained models (ResNet50, DenseNet169, InceptionV3, VGG16, MobileNetV2) across nine diverse datasets.
  • Implemented a novel approach combining data point selection based on minimal textural feature divergence and dynamic layer selection based on positive weight analysis.
  • Evaluated the impact of individual and combined selection methods on model performance.

Main Results:

  • Data points with lower textural feature divergence improved accuracy by 3.54% to 6.75% on CIFAR-100.
  • Selecting layers with more positive weights boosted accuracy by 2.42% to 13.04% on CIFAR-100.
  • The combined approach yielded an additional 1.56% accuracy improvement, demonstrating synergistic benefits.

Conclusions:

  • Selecting data points with low divergence from source samples significantly improves target task adaptation in transfer learning.
  • Choosing pre-trained model layers with predominantly positive weights streamlines the fine-tuning process, reducing experimental effort.
  • The integrated pipeline offers a more robust and efficient strategy for optimizing transfer learning performance.