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Sengul Dogan

Sengul Dogan

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Computer Science
Turkey
2026

D-Index & Metrics

Computer Science

D-Index
45
Citations
6534
World Ranking
7310
National Ranking
10

Research.com Recognitions

  • 2026 - Research.com Computer Science in Turkey Leader Award

Overview

Sengul Dogan is a researcher affiliated with Fırat University in Turkey, contributing extensively to the fields of Computer Science and Medicine. Their work spans a broad range of interdisciplinary topics, integrating advanced computational techniques with medical and cognitive neuroscience applications.

The main fields of study for Sengul Dogan include:

  • Computer Science
  • Medicine

The researcher's subfields cover:

  • Cognitive Neuroscience
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging

Key research topics encompass:

  • EEG and Brain-Computer Interfaces
  • ECG Monitoring and Analysis
  • Music and Audio Processing
  • COVID-19 diagnosis using AI
  • Emotion and Mood Recognition
  • Speech and Audio Processing
  • Phonocardiography and Auscultation Techniques

Several recent publications showcase Sengul Dogan's contribution to both methodological advances and applied biomedical signal analysis:

  • "An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image" (2020), published in Chemometrics and Intelligent Laboratory Systems
  • "Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition" (2020), published in Biomedical Signal Processing and Control
  • "EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier" (2021), published in Biomedical Signal Processing and Control
  • "Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques" (2020), published in Knowledge-Based Systems
  • "Automated ASD detection using hybrid deep lightweight features extracted from EEG signals" (2021), published in Computers in Biology and Medicine

Frequent co-authors in Sengul Dogan's body of work include:

  • Türker Tuncer
  • U. Rajendra Acharya
  • Prabal Datta Barua
  • Mehmet Bayğın

Publishing activity is notably concentrated in the following venues:

  • Multimedia Tools and Applications
  • Biomedical Signal Processing and Control
  • Diagnostics
  • Expert Systems with Applications
  • Cognitive Neurodynamics

This profile illustrates Sengul Dogan's multidisciplinary approach at the intersection of computing and biomedical sciences, with particular emphasis on signal processing and artificial intelligence applications in healthcare diagnostics and cognitive neuroscience.

Best Publications

  • Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals

    Turker Tuncer;Sengul Dogan;Paweł Pławiak;U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya

  • An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image.

    Turker Tuncer;Sengul Dogan;Fatih Ozyurt

  • Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition

    Turker Tuncer;Sengul Dogan;Abdulhamit Subasi

  • EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier

    Abdulhamit Subasi;Abdulhamit Subasi;Turker Tuncer;Sengul Dogan;Dahiru Tanko

  • Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques

    Turker Tuncer;Sengul Dogan;U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya

  • Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

    Mehmet Baygin;Sengul Dogan;Turker Tuncer;Prabal Datta Barua

  • GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

    Hui Wen Loh;Chui Ping Ooi;Elizabeth Palmer;Prabal Datta Barua

  • Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

    Turker Tuncer;Sengul Dogan;Fatih Özyurt;Samir Brahim Belhaouari

  • Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels

    Turker Tuncer;Sengul Dogan;Udyavara Rajendra Acharya;Udyavara Rajendra Acharya;Udyavara Rajendra Acharya

  • Automated accurate fire detection system using ensemble pretrained residual network

    Unknown

  • Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals

    Unknown

  • A new fractal pattern feature generation function based emotion recognition method using EEG

    Turker Tuncer;Sengul Dogan;Abdulhamit Subasi;Abdulhamit Subasi

  • EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

    Turker Tuncer;Sengul Dogan;Abdulhamit Subasi;Abdulhamit Subasi

  • Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals

    Hui Wen Loh;Chui Ping Ooi;Emrah Aydemir;Turker Tuncer

  • PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition.

    Abdullah Dogan;Merve Akay;Prabal Datta Barua;Prabal Datta Barua;Mehmet Baygin

  • PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images

    Unknown

  • A novel Covid-19 and Pneumonia Classification Method based on F-transform.

    Turker Tuncer;Fatih Ozyurt;Sengul Dogan;Abdulhamit Subasi;Abdulhamit Subasi

  • LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

    Turker Tuncer;Sengul Dogan;Abdulhamit Subasi;Abdulhamit Subasi

  • Automated brain disease classification using exemplar deep features

    Unknown

  • A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

    Turker Tuncer;Sengul Dogan;Fatih Ertam;Abdulhamit Subasi

  • Tetromino pattern based accurate EEG emotion classification model

    Turker Tuncer;Sengul Dogan;Mehmet Baygin;U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya

  • A novel octopus based Parkinson’s disease and gender recognition method using vowels

    Turker Tuncer;Sengul Dogan

  • Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records

    Mehmet Baygin;Türker Tuncer;Sengül Dogan;Ru San Tan

  • A new data hiding method based on chaos embedded genetic algorithm for color image

    Şengül Doğan

Frequent Co-Authors

Turker Tuncer
Turker Tuncer Fırat University
U. Rajendra Acharya
U. Rajendra Acharya University of Southern Queensland
Abdulhamit Subasi
Abdulhamit Subasi Effat University
Moloud Abdar
Moloud Abdar Deakin University
Ganesh R. Naik
Ganesh R. Naik Flinders University
N. Arunkumar
N. Arunkumar SASTRA University
Oliver Faust
Oliver Faust Sheffield Hallam University

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