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

The Eukaryotic Promoter Region02:40

The Eukaryotic Promoter Region

18.7K
The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences.  The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...
18.7K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.9K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.9K
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
652
Aging01:26

Aging

643
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
643

You might also read

Related Articles

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

Sort by
Same journal

Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation.

Journal of medical systems·2026
Same journal

Correction to: Designing Operating Rooms as an Integrated Socio-Technical Ecosystem: Practical Lessons from a High-Volume Tertiary Center.

Journal of medical systems·2026
Same journal

AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.

Journal of medical systems·2026
Same journal

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Journal of medical systems·2026
Same journal

Predicting the Predictor: Unresolved Validity Threats in LLM-Based ASA Classification.

Journal of medical systems·2026
Same journal

Development and Internal Validation of a Vectorcardiography-Augmented Model for 12-Month Major Adverse Cardiovascular Events in Chronic Heart Failure.

Journal of medical systems·2026
See all related articles

Related Experiment Video

Updated: Jan 24, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Extract Features from Periocular Region to Identify the Age Using Machine Learning Algorithms.

Kishore Kumar Kamarajugadda1, Trinatha Rao Polipalli2

  • 1Department of ECE, Faculty of Science and Technology, IFHE, Hyderabad, India. kkishore@ifheindia.org.

Journal of Medical Systems
|May 24, 2019
PubMed
Summary
This summary is machine-generated.

Facial age estimation needs improvement. This study introduces a new method using the periocular region and a hybrid Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithm for better human age recognition.

Keywords:
AAMAge assessmentBIFsFeatures extractionPeriocular region

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Related Experiment Videos

Last Updated: Jan 24, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Facial age estimation accuracy is currently limited, necessitating improved biometric recognition techniques.
  • Human age is a crucial biometric trait for identification and search, but individual aging variations present challenges.
  • Existing age assessment methods require enhancement for reliable human recognition.

Purpose of the Study:

  • To propose a novel framework for human age estimation using the periocular region.
  • To develop an age-invariant feature extraction method.
  • To enhance age recognition accuracy through a hybrid machine learning algorithm.

Main Methods:

  • Preprocessing and normalization of the periocular region to extract age-invariant features.
  • Application of a hybrid Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithm for age analysis.
  • Utilizing the periocular region as a primary source for age-related feature extraction.

Main Results:

  • The proposed framework successfully extracts age-invariant features from the periocular region.
  • The hybrid SVM-kNN algorithm demonstrates effective analysis of the preprocessed periocular data.
  • The technique achieves superior age recognition performance compared to existing methods.

Conclusions:

  • The periocular region is a viable source for accurate age estimation.
  • Hybrid machine learning approaches combining SVM and kNN enhance human age recognition.
  • The developed framework offers a promising solution for improving biometric age assessment.