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

Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

You might also read

Related Articles

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

Sort by

Same author

A framework based on hybrid Suzuki-Abe and convex Hull approach for improved classification of skin lesions.

Scientific reports·2026

Same author

LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination.

Scientific reports·2026

Same author

XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization.

Bioengineering (Basel, Switzerland)·2026

Same author

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

The American journal of pathology·2026

Same author

Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.

Bioengineering (Basel, Switzerland)·2026

Same author

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

BMC plant biology·2026

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

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

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

Published on: December 6, 2024

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

Waqar Ashiq¹, Samra Kanwal², Adnan Rafique³

¹Department of Software Engineering, University of Management and Technology, Lahore, 54590, Pakistan.

Scientific Reports

|November 20, 2024

Summary

This summary is machine-generated.

This study introduces a novel hybrid model for detecting hate speech in Roman Urdu text on social media. The model combines deep learning and transformer features with machine learning classifiers, achieving state-of-the-art results on benchmark datasets.

Keywords:

Deep learning Hate speech detection Model optimization Urdu text classification

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jun 7, 2025

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

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

Published on: December 6, 2024

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Natural Language Processing (NLP)
Artificial Intelligence (AI)
Social Media Analysis

Background:

Social media growth has led to increased cyberbullying and hate speech.
Automatic hate speech detection (HSD) is a critical NLP research area.
Research on Urdu language HSD is limited, particularly for Roman Urdu text.

Purpose of the Study:

To develop and evaluate a novel hybrid model for Roman Urdu hate speech detection on Twitter.
To address the scarcity of research in Urdu language HSD.
To explore the integration of deep learning, transformer models, and machine learning algorithms for this task.

Main Methods:

Developed a hybrid model integrating deep learning (DL) and transformer models for feature extraction.
Employed machine learning algorithms (MLAs) for classification.
Utilized hyperparameter optimization (HPO) techniques like Grid Search, Randomized Search, and Bayesian Optimization.
Evaluated the model on two public Roman Urdu corpora: HS-RU-20 and RUHSOLD.

Main Results:

The hybrid model using Multilingual BERT (MBERT) with a Support Vector Machine (SVM) classifier, optimized via Randomized Search (RS), achieved state-of-the-art performance.
On the HS-RU-20 corpus, achieved 0.93 accuracy and 0.95 F1 score (Neutral-Hostile) and 0.89 accuracy and 0.88 F1 score (Hate Speech-Offensive).
On the RUHSOLD corpus, achieved 0.95 accuracy and 0.94 F1 score (Coarse-grained) and 0.87 accuracy and 0.84 F1 score (Fine-grained).

Conclusions:

The proposed hybrid approach demonstrates significant effectiveness for Roman Urdu hate speech detection.
The integration of advanced NLP techniques and optimized machine learning classifiers is crucial for high performance.
This research contributes a valuable tool for combating online hate speech in the Urdu language context.