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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Aufa Rafiki
Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Aceh, Indonesia.
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda Melinda; Syahrul Gazali; Yuwaldi Away; Aufa Rafiki; W.K Wong; Muliyadi Muliyadi; Siti Rusdiana
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.
Non-Contact Multispectral Image Binary Classification of Water and Sodium Hydroxide Solutions Using Convolutional Neural Networks Siti Rusdiana; Asep Rusyana; Juwita; Mauliza Putri; Aufa Rafiki; Souvik Das
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1727

Abstract

Identification of visually transparent liquids remains a challenging problem in non-contact sensing because chemically different solutions can appear nearly identical under normal observation. In laboratory and industrial environments, direct-contact chemical measurements are reliable but may require sample handling, probe calibration, cleaning, and additional processing time, which can limit their use in rapid or automated monitoring systems. This study aims to develop and evaluate a non-contact image-based classification framework for distinguishing pure water (H₂O) from sodium hydroxide solution (H₂O with NaOH) using multispectral fluctuation-pattern images. The proposed approach integrates image preprocessing, K-means segmentation, and a convolutional neural network (CNN)-based classification. A balanced dataset of 1,050 multispectral images, consisting of 525 images for each class, was used in the experiment. Each image was resized, converted to grayscale, normalized, and segmented using K-means clustering to emphasize the dominant liquid-region fluctuation pattern before classification. Three CNN architectures, namely InceptionV3, VGG19, and DenseNet201, were trained and compared under identical data-splitting and evaluation conditions. The experimental results showed that VGG19 achieved the best testing performance, with an accuracy of 97.47%, precision of 95.18%, recall of 100.00%, and F1-score of 97.53%. DenseNet201 obtained 94.30% accuracy, while InceptionV3 achieved 89.24% accuracy. These results indicate that multispectral fluctuation-pattern images contain discriminative optical information that can be learned effectively by CNN models, even when the liquid samples are visually indistinguishable to the human eye. The proposed framework demonstrates the feasibility of non-contact transparent liquid identification and may support the development of automated monitoring systems for laboratory, chemical, and industrial applications where direct sample contact is undesirable or impractical.
Co-Authors Asep Rusyana Juwita Mauliza Putri Melinda Melinda Muliyadi Muliyadi Siti Rusdiana Siti Rusdiana Souvik Das Syahrul Gazali W.K Wong Yuwaldi Away