• varshitha NC
• Dr.Roopa N K

Autism spectrum disorders (ASDs) are a complex neurological development disorder that makes communication difficult, interacting with others and doing similar things many times. It is very important that TSA diagnosis is early and accurate for treatment to start immediately; this can have a significant impact on development. Traditional diagnostic methods often take time and rely on subjective interpretations, which can lead to delays and inconsistencies. Automatic learning (ML) is a new technology that can help diagnose and predict TSA more accurately and effectively. The magazine document considers how different automatic learning methods can be used to research and predict ASD. It focuses on the EEG signals and the deep-chest-Ron neural network. To create and evaluate predictable models, we use many different data sets, such as models related to children and children of school age. Using knowledge of current studies in this field, research includes data analysis, functional techniques, model selection, and performance evaluation.

Autism Spectrum Disorder, Machine Learning, EEG Signals, Deep Learning, Convolutional Neural Networks, Transfer Learning, Early Diagnosis, ASD Prediction.

"Models for Classifying Autism Spectrum Disorder From EEG Signals Using Hybrid Deep Convolutional Neural Networks and Transfer Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.i413-i420, June-2025, Available :http://www.jetir.org/papers/JETIR2506853.pdf

"Models for Classifying Autism Spectrum Disorder From EEG Signals Using Hybrid Deep Convolutional Neural Networks and Transfer Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppi413-i420, June-2025, Available at : http://www.jetir.org/papers/JETIR2506853.pdf