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Improving Translational Accuracy

Improving Translational Accuracy

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...

Improving Translational Accuracy

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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...

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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Enhancing age and gender verification in OTT accounts using deep learning techniques.

M Sanjay¹, Pillaram Manoj¹, S Graceline Jasmine¹

¹School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India.

Frontiers in Artificial Intelligence

|April 23, 2026

Summary

This study introduces a convolutional neural network (CNN) model to verify user age on Over-The-Top (OTT) accounts, enhancing child safety by restricting access to inappropriate content. The system achieved 91% accuracy in age and gender identification.

Keywords:

OTT (Over-The-Top)age and gender identification deep learning techniques digital child safety measures parental concerns responsible content consumption video recognition

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Computer Science
Artificial Intelligence
Digital Media Security

Background:

Children's exposure to age-inappropriate content on Over-The-Top (OTT) platforms is a significant concern.
Minors frequently access content using parental accounts, bypassing existing safeguards.
OTT platforms need robust mechanisms for age verification to ensure child safety.

Purpose of the Study:

To develop an innovative method for accurate user age ascertainment in OTT accounts.
To limit children's access to unsuitable audio-visual content.
To enhance the responsibility of OTT platforms in content delivery through age identification.

Main Methods:

A tailored convolutional neural network (CNN) model was developed for age and gender identification.
The UTK Face dataset was utilized for training and evaluating the CNN model.
A user interface (UI) was created using OpenCV and the Flask Framework for age verification.

Main Results:

The proposed CNN model achieved a high accuracy rate of 91% for age and gender identification on the UTK Face dataset.
The developed UI effectively integrates the age verification model into a functional system.
The solution demonstrates a significant improvement in identifying user age for content restriction.

Conclusions:

The developed age verification system effectively addresses child safety concerns on OTT platforms.
Implementing this solution promotes responsible content consumption among minors and reduces parental anxiety.
This research sets a new benchmark for age verification technologies in the digital media landscape.