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Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

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
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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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.
Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Updated: Jun 30, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Sales-Training-Inspired Optimization for Deep High-Order Principal Network in Autism Spectrum Disorder

T Venkatakrishnamoorthy1, Anuradha Chinta2, P Sujatha3

  • 1Department of Electronics and Communication Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, India.

International Journal of Developmental Neuroscience : the Official Journal of the International Society for Developmental Neuroscience
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces STBO_DHPCNet, an advanced AI model for diagnosing Autism Spectrum Disorder (ASD) using brain imaging. The novel approach significantly improves early ASD detection accuracy, aiding timely intervention.

Keywords:
autism spectrum disorder classificationdeep high‐order attention neural networkdeep learningprincipal component analysis networksales training based optimization

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Last Updated: Jun 30, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Autism Spectrum Disorder (ASD) presents diverse challenges for early diagnosis.
  • Traditional diagnostic methods are time-consuming and require specialized expertise.
  • Early detection of ASD is crucial for effective intervention and improved outcomes.

Purpose of the Study:

  • To develop an automated and efficient method for Autism Spectrum Disorder classification.
  • To enhance the accuracy and accessibility of early ASD detection using neuroimaging data.
  • To introduce the Sales Training-Based Optimization enabled Deep High-Order Principal Component Network (STBO_DHPCNet) for ASD diagnosis.

Main Methods:

  • Utilized resting-state fMRI (rs-fMRI) data from 1114 subjects in the ABIDE dataset.
  • Applied gamma correction for image enhancement and Region of Interest (ROI) extraction.
  • Implemented Sales Training Based Optimization (STBO) for nub region extraction and DHPCNet training.
  • Developed DHPCNet by integrating Deep High-Order Attention Neural Network (DHA-Net) and Principal Component Analysis Network (PCA-Net).

Main Results:

  • The STBO_DHPCNet model achieved high classification performance.
  • Achieved accuracy of 95.62%, sensitivity of 94.79%, and specificity of 95.86% for ASD detection.
  • Demonstrated the effectiveness of the proposed model in classifying ASD from rs-fMRI images.

Conclusions:

  • The STBO_DHPCNet offers a promising, accurate, and efficient approach for early Autism Spectrum Disorder detection.
  • This AI-driven method can aid healthcare professionals in diagnosing ASD more effectively.
  • The study highlights the potential of advanced deep learning techniques in neurodevelopmental disorder research.