Ilknur Sercek

Deep learning-based classification of cerebrovascular lesions on computed tomography images

Gülay MAÇİN, İrem Taşçı, Prabal Datta Barua, Ilknur Sercek et autres

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TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection

İrem Taşçı, Ilknur Sercek, Yunus Talu, Prabal Datta Barua et autres

Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains …

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MountPat: investigations on the EEG signals

Uğur İnce, Ömer Faruk Göktaş, Ilknur Sercek, Serkan Kirik et autres

To extract information from the brain, the most cost-effective method is electroencephalography (EEG) signal acquisition. Therefore, many researchers have used EEG signals to capture brain activity. EEG signals are complex; hence, computer-aided models-especially machine learning (ML)-are generally employed to interpret them. The …

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KANPoolConvNeXt: an innovative malware detection method

Ilknur Sercek, Mehmet Veysel Gün, Sengul Dogan, Turker Tuncer

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FlexiLPQ: automated osteoid osteoma detection using computed tomography

Sefa Key, Anil Agar, Ilknur Sercek, Ahmet Kursad Poyraz et autres

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DCPat-XFE: an explainable EEG model for psychogenic nonepileptic seizure detection

Derya Ünal, Dahiru Tanko, Ilknur Sercek, İrem Taşçı et autres

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StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images

Evren Ekingen, Ferhat Yıldırım, Özgür Bayar, Erhan Akbal et autres

BACKGROUND AND OBJECTIVE: Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightweight convolutional neural network (CNN) for computed tomography (CT) …

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A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection

Ilknur Sercek, Niranjana Sampathila, İrem Taşçı, Tuba Ekmekyapar et autres

Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and …

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Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures

Can Berk Biret, Şükrü Gürbüz, Erhan Akbal, Mehmet Bayğın et autres

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Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals

Gülay Taşçı, Prabal Datta Barua, Dahiru Tanko, Tuğçe Keleş et autres

Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic …

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Bipartite Dynamic Pattern and Penta Pooling‐Based Valvular Heart Disorder Detection Model

Mehmet Nail Bilen, Irfan Yaman, Mehmet Ali Kobat, Ilknur Sercek et autres

ABSTRACT Valvular heart disorders (VHD) have high mortality rates, making early detection essential. Machine learning offers a strong solution for improving diagnosis. This study presents a self‐organised feature engineering model designed for high classification accuracy. A large dataset of respiratory sounds, with …

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