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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

338
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
338
Modeling in Therapy01:26

Modeling in Therapy

149
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Related Experiment Video

Updated: Sep 13, 2025

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos
05:32

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

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Electromagnetic Interaction Algorithm (EIA)-Based Feature Selection With Adaptive Kernel Attention Network (AKAttNet)

Tathagat Banerjee1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Patna, India.

International Journal of Developmental Neuroscience : the Official Journal of the International Society for Developmental Neuroscience
|August 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for autism spectrum disorder (ASD) detection using the electromagnetic interaction algorithm (EIA) for feature selection and an adaptive kernel attention network (AKAttNet) for classification, significantly improving diagnostic accuracy and efficiency.

Keywords:
adaptive kernel attention network (AKAttNet)autism spectrum disorder (ASD)classificationdeep learningearly diagnosiselectromagnetic interaction algorithm (EIA)feature selectionmachine learningneurodevelopmental disorderspublicly available datasets

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Computational Psychiatry
  • Biomedical Data Analysis

Background:

  • Autism spectrum disorder (ASD) diagnosis relies on identifying cognitive, social, and behavioral patterns.
  • Current diagnostic methods often lack accuracy, efficient feature selection, and computational speed.
  • Early and accurate ASD detection is critical for timely intervention and improved outcomes.

Purpose of the Study:

  • To develop an integrated computational framework for enhanced autism spectrum disorder (ASD) detection.
  • To improve the accuracy and efficiency of ASD classification using advanced machine learning techniques.
  • To address limitations in traditional diagnostic methods for ASD.

Main Methods:

  • Integration of the electromagnetic interaction algorithm (EIA) for optimal feature selection.
  • Application of an adaptive kernel attention network (AKAttNet) for ASD classification.
  • Evaluation on four diverse autism spectrum disorder (ASD) datasets, comparing against traditional and deep learning models.

Main Results:

  • The proposed EIA-AKAttNet model achieved high classification accuracy, ranging from 0.901 to 0.9827 across datasets.
  • Demonstrated superior performance compared to conventional machine learning and existing deep learning methods.
  • Showcased efficient feature dimensionality reduction by EIA, leading to lower computational time and enhanced generalizability.

Conclusions:

  • The hybrid EIA-AKAttNet framework offers a practical and effective solution for early autism spectrum disorder (ASD) diagnosis.
  • This approach enhances diagnostic accuracy while reducing computational overhead, showing promise for clinical application.
  • The study underscores the potential of combining deep learning with optimization algorithms for reliable ASD screening systems.