Jove
Visualize
Contact Us

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.2K
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...
1.2K
Sympathetic Activation01:16

Sympathetic Activation

6.3K
The sympathetic division can influence tissues and organs by releasing norepinephrine at peripheral synapses and distributing epinephrine and norepinephrine through the bloodstream. In times of crisis or stress, sympathetic activation occurs, which is regulated by sympathetic centers in the hypothalamus. As a result, sympathetic activation prepares the body for physical exertion, rapid ATP production, and heightened alertness, allowing individuals to respond effectively to challenging or...
6.3K
Attitudes01:54

Attitudes

32.2K
Attitude is our evaluation of a person, an idea, or an object. We have attitudes for many things ranging from products that we might pick up in the supermarket to people around the world to political policies. Typically, attitudes are favorable or unfavorable: positive or negative (Eagly & Chaiken, 1993). And, they have three components: an affective component (feelings), a behavioral component (the effect of the attitude on behavior), and a cognitive component (belief and knowledge;...
32.2K
Classification of Systems-II01:31

Classification of Systems-II

387
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
387
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.6K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
4.6K
Somatosensory, Motor, and Association Cortex01:24

Somatosensory, Motor, and Association Cortex

1.7K
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
1.7K

You might also read

Related Articles

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

Sort by
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
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 Experiment Video

Updated: Nov 29, 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

4.9K

Interactive Dual Attention Network for Text Sentiment Classification.

Yinglin Zhu1, Wenbin Zheng1,2, Hong Tang3

  • 1College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Computational Intelligence and Neuroscience
|November 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the Interactive Dual Attention Network (IDAN) for text sentiment classification. IDAN effectively models the interaction between semantics and sentiment, outperforming existing methods on benchmark datasets.

Related Experiment Videos

Last Updated: Nov 29, 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

4.9K

Area of Science:

  • Natural Language Processing
  • Deep Learning
  • Artificial Intelligence

Background:

  • Text sentiment classification is crucial in Natural Language Processing.
  • Deep learning methods outperform traditional machine learning but often neglect semantic-contextual interactions.
  • Existing models fail to capture the interplay between contextual semantics and sentimental tendency.

Purpose of the Study:

  • To propose a novel Interactive Dual Attention Network (IDAN) for text sentiment classification.
  • To interactively learn representations integrating contextual semantics and sentimental tendency.
  • To enhance sentiment classification performance by modeling these interactive relationships.

Main Methods:

  • Utilized linguistic resources to extract sentimental tendency information.
  • Employed BERT (Bidirectional Encoder Representations from Transformers) for word embeddings.
  • Implemented two Bidirectional LSTM (BiLSTM) networks for dependency learning.
  • Applied multi-head and global attention mechanisms for interactive learning and focused representation.

Main Results:

  • The proposed IDAN model demonstrated superior performance compared to competitive methods.
  • Experiments on four benchmark datasets validated the model's effectiveness.
  • Analysis and attention weight visualization confirmed the model's ability to learn semantic-sentiment interactions.

Conclusions:

  • The Interactive Dual Attention Network (IDAN) effectively captures the relationship between contextual semantics and sentimental tendency.
  • IDAN significantly improves text sentiment classification performance.
  • The proposed approach offers a novel and effective method for sentiment analysis.