Hybrid feature-based anxiety detection in autism using hybrid optimization tuned artificial neural network

No abstract is available for this paper.

• Publication year

  2022

• Venue

  Biomedical Signal Processing and Control

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Computer Science, Psychology

• Identifiers
  DOI 10.1016/j.bspc.2022.103699
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

• Empowering Clinical Outcomes with AI Powered Innovations in Medical Imaging Diagnostics

  2025cites this paper
• Identification of Motion States Based on EEG and Deep Learning

  2025cites this paper
• Exploring Diverse Features: A Thorough Survey for Anxiety Disorders

  2025cites this paper
• Hybrid Quantum-Classical Machine Learning for Enhanced Predictive Analytics

  2025cites this paper
• Systematic Approach to Emotion Recognition in Autistic Children Using MTAE-CTrans

  2025cites this paper
• Hybrid weight optimized deep learning for autism and visual sensory processing disorder detection: A heuristic strategy on medical domain

  2024influential citation
• Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

  2024cites this paper
• Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023

  2024cites this paper
• Computational Approaches for Anxiety and Depression: A Meta- Analytical Perspective

  2024cites this paper
• A Multimodal Data-driven Framework for Anxiety Screening

  2023influential citation
• SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

  2022cites this paper