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

Bullying02:04

Bullying

8.5K
A modern form of aggression is bullying. As you learn in your study of child development, socializing and playing with other children is beneficial for children’s psychological development. However, as you may have experienced as a child, not all play behavior has positive outcomes. Some children are aggressive and want to play roughly. Other children are selfish and do not want to share toys. One form of negative social interactions among children that has become a national concern is...
8.5K
Classification of Systems-I01:26

Classification of Systems-I

293
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
293
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

184
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
184
Classification of Signals01:30

Classification of Signals

869
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
869
Force Classification01:22

Force Classification

1.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.6K
Observational Learning01:12

Observational Learning

302
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
302

You might also read

Related Articles

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

Sort by
Same author

Green Synthesis and Characterization of Konjac Glucomannan-Capped Cerium Nanoparticles for Photocatalytic Degradation of Naphthol Blue Black and Methyl Orange Dyes in Wastewater.

Nanomaterials (Basel, Switzerland)·2026
Same author

BCD-TransNet: Automatic breast cancer detection and classification using transfer learning approach.

Technology and health care : official journal of the European Society for Engineering and Medicine·2025
Same author

Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM.

Frontiers in medicine·2025
Same author

The Clinical Utility of Automated Immature Granulocyte Measurement in the Early Diagnosis of Bacteremia.

Cureus·2024
Same author

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks.

Scientific reports·2024
Same author

Facile preparation of flame-retardant cellulose composite with biodegradable and water resistant properties for electronic device applications.

Scientific reports·2023

Related Experiment Video

Updated: Sep 7, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.1K

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media.

Neelakandan S1, Sridevi M2, Saravanan Chandrasekaran3

  • 1Department of CSE, R.M.K Engineering College, Kavaraipettai, India.

Computational Intelligence and Neuroscience
|June 21, 2022
PubMed
Summary

This study introduces a novel deep learning approach for detecting and categorizing cyberbullying (CB) on social media. The Feature Subset Selection with Deep Learning-based CB Detection and Categorization (FSSDL-CBDC) method achieves high accuracy in identifying harmful online content.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Sep 7, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.1K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Social Computing

Background:

  • Online social networks (OSN) and social media are increasingly popular, raising significant security and privacy concerns.
  • Cyberbullying (CB), defined as repetitive, aggressive online behavior using information and communication technology (ICT), is a critical issue on these platforms.
  • Effective detection and categorization of CB are essential for mitigating its impact.

Purpose of the Study:

  • To propose a novel approach for detecting and categorizing cyberbullying in online social networks.
  • To enhance the performance of cyberbullying detection models through feature subset selection.
  • To develop and evaluate a deep learning-based system for combating cyberbullying.

Main Methods:

  • A Feature Subset Selection with Deep Learning-based CB Detection and Categorization (FSSDL-CBDC) technique was developed.
  • The Binary Coyote Optimization (BCO)-based Feature Subset Selection (BCO-FSS) was employed for optimal feature selection.
  • The Salp Swarm Algorithm (SSA) was integrated with a Deep Belief Network (DBN) to create the SSA-DBN model for detection and classification.

Main Results:

  • The proposed FSSDL-CBDC technique demonstrated superior classification performance compared to other methods.
  • The SSA-DBN model achieved a high accuracy rate of 99.983% in detecting and categorizing cyberbullying.
  • Extensive simulations confirmed the effectiveness of the FSSDL-CBDC approach in various aspects.

Conclusions:

  • The FSSDL-CBDC technique offers a robust and accurate solution for cyberbullying detection and categorization on social media.
  • The integration of BCO-FSS and SSA-DBN significantly improves the performance of cyberbullying identification systems.
  • This research contributes a valuable tool for enhancing online safety and combating cyberbullying.