Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by

Same author

Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism.

Computational intelligence and neuroscience·2020

Same author

Prediction of Breast Cancer from Imbalance Respect Using Cluster-Based Undersampling Method.

Journal of healthcare engineering·2019

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Mixed Script Identification Using Automated DNN Hyperparameter Optimization.

Muhammad Yasir¹, Li Chen¹, Amna Khatoon²

¹School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China.

Computational Intelligence and Neuroscience

|December 20, 2021

Summary

This summary is machine-generated.

This study enhances natural language processing (NLP) by accurately identifying mixed scripts in multilingual text. Bidirectional Gated Recurrent Unit (Bi-GRU) achieved 90.17% accuracy, improving NLP tasks in diverse language environments.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Oct 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Computational Linguistics
Natural Language Processing (NLP)
Machine Learning

Background:

Automated natural language processing (NLP) systems face significant challenges with mixed-script text, hindering tasks like Part-of-Speech (POS) tagging and word sense disambiguation due to noisy data.
The prevalence of multilingual environments, particularly those involving Roman Urdu, Hindi, Saraiki, Bengali, and English, necessitates robust mixed script identification methods.

Purpose of the Study:

To develop and optimize a language identification model for accurately detecting mixed scripts in a dataset comprising Roman Urdu, Hindi, Saraiki, Bengali, and English.
To evaluate the performance of various Recurrent Neural Network (RNN) variants, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, for this task.

Main Methods:

Utilized word vectorization and RNN variants for training the language identification model.
Experimentally investigated and optimized different architectures: LSTM, Bidirectional LSTM (Bi-LSTM), GRU, and Bidirectional GRU (Bi-GRU).
Incorporated learned word class features with GloVe embeddings for enhanced performance.

Main Results:

Achieved a highest accuracy of 90.17% using the Bidirectional Gated Recurrent Unit (Bi-GRU) architecture.
Demonstrated the effectiveness of combining word class features with GloVe embeddings for improved script identification.
Successfully addressed issues common in multilingual text, such as Romanized words merged with English characters, generative spellings, and phonetic typing.

Conclusions:

The Bidirectional Gated Recurrent Unit (Bi-GRU) model, enhanced with GloVe embeddings and word class features, provides a highly accurate solution for mixed script identification.
This research significantly advances NLP capabilities in handling complex, multilingual text, paving the way for more effective automated language processing in diverse linguistic contexts.