Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Access Right: Open AccessAuthor: search.filters.author.Gumus, Abdurrahman

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  • Article
    Vision-Language Model Approach for Few-Shot Learning of Attention Deficit Hyperactivity Disorder Using EEG Connectivity-Based Featured Images
    (IOP Publishing Ltd, 2025) Catal, Mehmet Sergen; Gumus, Abdurrahman; Karabiber Cura, Ozlem; Aydin, Ocan; Zubeyir Unlu, Mehmet
    Traditional medical diagnosis approaches have predominantly relied on single-modality analysis, limiting clinicians to interpreting isolated data streams such as images or time series. The integration of vision language models (VLMs) into neurophysiological analysis represents a paradigm shift toward multimodal diagnostic frameworks, enabling clinicians to interact with diagnosis models through diverse modalities including text, audio, visual inputs, etc. This multimodal interaction capability extends beyond conventional label-based classification, offering clinicians flexibility in diagnostic reasoning and decision-making processes. Building on this foundation, this study explores the application of VLMs to electroencephalography (EEG)-based attention deficit hyperactivity disorder (ADHD) classification, addressing a gap in neurophysiological diagnostics. The proposed framework applies VLM-based few-shot ADHD classification by converting raw EEG data into EEG connectivity-based featured images compatible with contrastive language-image pre-training's (CLIP) image encoder. The adaptor-based CLIP approach (Tip-Adapter and Tip-Adapter-F) for few-shot learning improves CLIP's zero-shot classification performance, achieving 78.73% accuracy with 1-shot and 98.30% accuracy with 128-shot using the RN50x16 backbone. Experiments investigate prompt engineering effects, backbone architectures of CLIP, patient-based classification, and combinations of EEG connectivity features. Comparative analysis is performed with two datasets to evaluate the approach between different data sources. Through the adaptation of pre-trained VLMs to neurophysiological data, this technique demonstrates the potential for multimodal diagnostic frameworks that enable flexible clinician-model interactions beyond conventional label-based classification systems. The approach achieves effective ADHD classification with minimal training data while establishing foundations for applying VLMs in clinical neuroscience, where diverse modality interactions through text, visual, and audio inputs can enhance diagnostic workflows. The code is publicly available on GitHub to facilitate further research in the field: https://github.com/miralab-ai/vlm-few-shot-eeg.
  • Article
    Understanding the Synthesis Mechanism of Arginine Functionalized Silver/Silver Chloride Nanoparticles Using Sugar Ligands
    (Elsevier, 2025) Bolat, Suheda; Degirmenci, Suna; Gumus, Abdurrahman; Sancak, Zafer; Yazgan, Dris
    In this study, we performed a mechanistic study to understand how the sugar ligand chemistry affected the morphology, size and surface chemistry of Ag/AgCl_NPs synthesized in the presence of L-Arginine hydrochloride and L-Arginine/KCl mixture. The sugar ligands Lactose p-methoxyaniline (LMA) and Galactose 5-aminosalicylic acid (G5AS) resulted in formation of sheet-like Ag/AgCl_NPs while Lactose sulfanilic acid (LSA) and Lactose psulfonyldianiline (LPSA) caused the formation of anisotropic and film-like Ag/AgCl_NPs. The UV-Vis based mechanistic studies showed that the presence of Arginine posed a strong effect on how G5AS and LMA ligands interact with silver ions while the effect was more complicated for the LSA and LPSA ligands due to the fact that they form complexation with Ag+ ions. The mechanism was further investigated using infrared (IR) studies that showed the increases in Argine and chloride ion concentrations resulted in differentiation of the surface chemistry of the Ag/AgCl_NPs, and appearance of Arginine related IR bands became clearer in the case of cointroduction of Arginine and the sugar ligands. The characterized nanoparticles were then used as antibacterial agent for multidrug resistant Escherichia coli species for which less than 10 mu M minimum inhibitory concentrations were obtained. The promising antibacterial activity, which could be assigned to the presence of Arginine, was independent from the sugar ligand chemistry and nanoparticles' morphology and size. Particularly, large Ag/AgCl_NP film forming capacity can call further research to be exploited as coating materials for antibacterial application.