Author

Melinda

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Open access Sep 2026

A CatBoost-Based Framework for Autism Spectrum Disorder Classification from EEG Signals with Blackman–Harris FIR Filtering

These findings support preliminary within-cohort feasibility but not clinical or screening readiness, and support an EEG-based ASD classification framework combining Blackman–Harris FIR filtering, feature extraction and selection, and machine learning.

Buleun Waladah, Melinda, Yunidar et al. · International Islamic Univer... · 0 citations

Open access Sep 2026

Improved Autism Spectrum Disorder Detection Using Euclidean-Distance Facial Landmark Features to Enhance Classification Accuracy and Stability with Cross-Validation

Findings indicate that geometric feature extraction based on facial landmark distances is effective for ASD detection and has strong potential to be developed as an objective, interpretable, and efficient early screening tool using children’s facial images.

Syifa Anzella, Y. Nurdin, Melinda et al. · Jurnal Teknokes · 0 citations

Open access Jul 2026

Temporal Deep Learning with Multiscale Principal Component Features for Autism Classification from Electroencephalographic Signals

A comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal...

M. Muliyadi, Melinda, Yuwaldi Away et al. · Journal of Electronics Elect... · 0 citations