About me

Zafer CÖMERT is currently Asst. Prof. in the Department of Software Engineering at Samsun University. He received his BSc degree in Electronics and Computer Education from Firat University in 2008. He also received his MSc degree in Computer and Instructional Technologies from Firat University in 2012. He has finished Ph.D. work on the classification of cardiotocography data with machine learning techniques in the Department of Computer Engineering at Inönü University in 2017.

Expertise: Biomedical Signal Processing, Machine Learning, Clinical Decision Support Systems

Last 10 papers more...

  1. Determination of Tympanic Membrane Region in the Middle Ear Otoscope Images with Convolutional Neural Network Based YOLO Method
  2. Detection of weather images by using spiking neural networks of deep learning models
  3. Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks
  4. Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
  5. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
  6. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
  7. A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
  8. Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
  9. Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
  10. BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

Weekly program

Academic

A. International Publications indexed by SCI, SSCI, SCI-E

  1. Toğaçar M, Ergen B, Cömert Z. Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods. Appl Soft Comput 2020;97:106810. doi:https://doi.org/10.1016/j.asoc.2020.106810.
  2. Alsaggaf W, Cömert Z, Nour M, Polat K, Brdesee H, Toğaçar M. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals. Appl Acoust 2020;167:107429. doi:https://doi.org/10.1016/j.apacoust.2020.107429.
  3. Toğaçar M, Ergen B, Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med 2020;121:103805. doi:10.1016/j.compbiomed.2020.103805.
  4. N. Daldal, A. Sengur, K. Polat, and Z. Cömert, “A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model,” Appl. Acoust., vol. 166, p. 107346, 2020.
  5. M. Toğaçar, B. Ergen, and Z. Cömert, “Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models,” Measurement, vol. 158, p. 107703, (2020).
  6. M. Toğaçar, Z. Cömert, B. Ergen, Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method, Expert Syst. Appl. (2020) 113274. doi:https://doi.org/10.1016/j.eswa.2020.113274.
  7. Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models. Measurement, 107459. https://doi.org/https://doi.org/10.1016/j.measurement.2019.107459
  8. Toğaçar, M., Ergen, B., & Cömert, Z. (2020). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical Hypotheses, 134, 109531. https://doi.org/https://doi.org/10.1016/j.mehy.2019.109531
  9. Zafer C. Fusing fine-tuned deep features for recognizing different tympanic membranes. Biocybern Biomed Eng (2020). doi:10.1016/j.bbe.2019.11.001.
  10. Toğaçar M, Ergen B, Cömert Z. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng (2020). doi:10.1016/j.bbe.2019.11.004.
  11. Toğaçar M, Ergen B, Cömert Z. Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. Med Hypotheses (2020);135:109503. doi:https://doi.org/10.1016/j.mehy.2019.109503.
  12. Toğaçar M, Özkurt KB, Ergen B, Cömert Z. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Its Appl (2020). doi:https://doi.org/10.1016/j.physa.2019.123592.
  13. Toğaçar M, Ergen B, Cömert Z. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models. IRBM 2019. doi:https://doi.org/10.1016/j.irbm.2019.10.006.
  14. E. Başaran, Z. Cömert, Y. Çelik, Convolutional neural network approach for automatic tympanic membrane detection and classification, Biomed. Signal Process. Control. 56 (2020) 101734. doi:https://doi.org/10.1016/j.bspc.2019.101734.
  15. Daldal N, Cömert Z, Polat K. Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time-frequency information. Appl Soft Comput (2020):105834. doi:https://doi.org/10.1016/j.asoc.2019.105834.
  16. Ü. Budak, Z. Cömert, M. Çıbuk, A. Şengür, DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images, Med. Hypotheses. 134 (2020) 109426. doi:https://doi.org/10.1016/j.mehy.2019.109426.
  17. Budak Ü, Cömert Z, Rashid ZN, Şengür A, Çıbuk M. Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput (2019):105765. doi:https://doi.org/10.1016/j.asoc.2019.105765.
  18. Cömert Z, Şengür A, Akbulut Y, Budak Ü, Kocamaz AF, Bajaj V. Efficient approach for digitization of the cardiotocography signals. Phys A Stat Mech Its Appl (2019):122725. doi:https://doi.org/10.1016/j.physa.2019.122725.
  19. Y. Altuntaş, Z. Cömert, and A. F. Kocamaz, “Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach,” Comput. Electron. Agric., vol. 163, p. 104874, (2019).
  20. Z. Cömert, A. Şengür, Y. Akbulut, Ü. Budak, A. F. Kocamaz, and S. Güngör, “A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces,” IRBM, (2019).
  21. Z. Zhao, Y. Zhang, Z. Comert, Y. Deng, Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network, Front. Physiol. 10 (2019) 255. doi:10.3389/fphys.2019.00255.
  22. Cömert, Z., Kocamaz, A. F., & Subha, V. (2018). Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment. Computers in biology and medicine.
  23. Cömert, Z., & Kocamaz, A. F. (2018). Open-access software for analysis of fetal heart rate signals. Biomedical Signal Processing and Control, 45, 98-108.
  24. Cömert, Z., and Kocamaz, A. F.,  (2017), “Comparison of Machine Learning Techniques for Fetal Heart Rate Classification” Acta Physica Polonica A, Vol. 132 (2017), No. 3. 
  25. Varank, İ., Erkoç, F. M., Adıgüzel, T., Cömert, Z., & Zengin, E. (2014). “Effectiveness of an Online Automated Evaluation and Feedback System in an Introductory Computer Literacy Course”. Eurasia Journal of Mathematics, Science & Technology Education, 395-404.

B. International Publication

  1. Cömert Z, Sengür A, Budak Ü, Kocamaz AF. Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models. Heal Inf Sci Syst 2019;7:17. doi:10.1007/s13755-019-0079-z.
  2. Cömert, Zafer and Özge CÖMERT. "A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms." Bitlis Eren Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 7.1 (2017): 286-297.
  3. Z. Cömert, A.F. Kocamaz, “A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals”, Bitlis Eren Univ. J. Sci. Technol. 7 (2017) 93–103.
  4. A. Diker, Z. Cömert, E. Avcı, “A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals”, Bitlis Eren Univ. J. Sci. Technol. 7 (2017) 132–139
  5. Z. Comert and A. F. Kocamaz, (2016), “Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community,” Int. J. Comput. Appl., vol. 156, no. 4, pp. 26–31.
  6. Sevindik, T., & Cömert, Z. (2011). “Öğrenme Nesnelerinin Sınıflandırılması için Semantik Web Tabanlı İnsan Bilgisayar Etkileşimi”. NWSA, 816 - 822.
  7. Sevindik, T., & Cömert, Z. (2010). “Using Algorithms for Evaluation in Web-Based Distance Education”. Elsevier.
  8. Sevindik T., Cömert, Z. (2010). “Virtual Education Environments and Web Mining”. Elsevier, 5120-5124

C. International Publications of International Congresses and Symposium 

  1. Basaran E, Cömert Z, Şengür A, Budak Ü, Çelik Y, Togacar M. Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network. 2019 4th Int. Conf. Comput. Sci. Eng., 2019, p. 1–4. doi:10.1109/UBMK.2019.8907070.
  2. Şengür A, Akbulut Y, Budak Ü, Cömert Z. White Blood Cell Classification Based on Shape and Deep Features. 2019 Int. Artif. Intell. Data Process. Symp., 2019, p. 1–4. doi:10.1109/IDAP.2019.8875945.
  3. Basaran E, Sengur A, Comert Z, Budak U, Celik Y, Velappan S. Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks. 2019 Int. Artif. Intell. Data Process. Symp., 2019, p. 1–6. doi:10.1109/IDAP.2019.8875973.
  4. Z. Cömert, A.F. Kocamaz, Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach, in: R. Silhavy (Ed.), Softw. Eng. Algorithms Intell. Syst., Springer International Publishing, Cham, 2019: pp. 239–248. 
  5. Diker, A., et. al., “Classification of ECG Signal by using Machine Learning Methods”, 26th Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2018.
  6. Aydın, M. C., et. al., “Estimation Of Flow Series Using Discrete Wavelet Analysis And Artificial Neural Networks”, 4th International Conference on Engineering and Natural Sciences, Kiev, Ukraine, 2018.  
  7. Cömert, Z. et. al., “Performance Evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for Computerized Hypoxia Detection and Prediction”, 26th Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2018.
  8. Cömert, Z. et. al., “The Influences of Different Window Functions and Lengths on Image-based Time-Frequency Features of Fetal Heart Rate Signals”, 26th Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2018.
  9. Diker, A., et. al., “Intelligent System based on Genetic Algorithm and Support Vector Machine for Detection of Myocardial Infarction from ECG signals”,  26th Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2018.
  10. Cömert, Ö., Cömert, Z., and Genç, Z., "Distance Education Technologies and New Trends in Distance Education", International Conference on Multidisciplinary, Engineering, Science, Education and Technology (IMESET'17)", Bitlis, Turkey, 2017.
  11. Cömert, Z., and Kocamaz, A. F., (2017), "Using Wavelet Transform for Cardiotocography Signals Classification", 25th Signal Processing and Communications Applications Conference, Antalya, Turkey.
  12. Cömert, Z., and Kocamaz, A. F., (2017), "Cardiotocography Analysis based on Segmentation-based Fractal Texture Decomposition and Extreme Learning Machine", 25th Signal Processing and Communications Applications Conference, Antalya, Turkey.
  13. Cömert, Z., and Kocamaz, A. F., (2017), "A Novel Software for Comprehensive Analysis of Cardiotocography Signals, CTG-OAS", International Artificial Intelligence and Data Processing Symposium (IDAP'17), Malatya, Turkey.
  14. Z. Cömert and A. F. Kocamaz, (2017), "CTG-OAS: Open Access Software for Analysis of Fetal Heart Rate Signals," in 4th International Conference on Computational and Experimental Science and Engineering, Antalya, Turkey.
  15. Z. Cömert and A. F. Kocamaz, (2017), "Evaluation of Feature Selection Algorithms on Cardiotocography Data," in 4th International Conference on Computational and Experimental Science and Engineering, Antalya, Turkey.
  16. Z. Cömert and A. F. Kocamaz, (2016), "Comparison of Machine Learning Techniques for Fetal Heart Rate Classification," in International Conference on Computational and Experimental Science and Engineering, Antalya, Turkey.
  17. Z. Cömert and A. F. Kocamaz, (2016), "Performance Comparison of Neural Network Training Algorithms for Fetal Heart Rate Patterns," in International Conference on Computational and Experimental Science and Engineering, Antalya, Turkey.
  18. Z. Cömert and A. F. Kocamaz, (2016), "A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses," in International Artificial Intelligence and Data Processing Symposium'16, Malatya, Turkey.
  19. Z. Cömert, A. F. Kocamaz and S. Güngör, (2016), "Classification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine", 24th Signal Processing and Communications Applications Conference, Zonguldak.
  20. Çıbuk, M., & Cömert, Z. (2015). “Elektronik Talep Yönetim Sistemi”. International Science and Technology Conference. Petersburg, 753-759 pp, Russia: ISTE-C
  21. Cömert, Z., & Sevindik, T. (2011). “The use of Google Chart for Visual Presentation of Data in Semantic Web Based Learning Management System”. 5th International Computer & Instructional Technologies Symposium (s. 902-908). Elazığ - Turkey: Fırat University.
  22. Sevindik, T., Genç, Z., Kayışlı, K., & Cömert, Z. (2011). “Education Platform in E-Government Applications”. 5th International Computer & Instructional Technologies Symposium (s. 317-323). Elazığ-Turkey: Fırat University.

D. Publications of National Congresses and Symposium 

  1. Başaran E, Cömert Z, Şengür A, Budak Ü, Çelik Y, and Toğaçar M 2020 Normal ve Kronik Hastalıklı Orta Kulak İmgelerinin Evrişimsel Sinir Ağları Yöntemiyle Tespit Edilmesi, Türkiye Bilişim Vakfı Bilgi. Bilim. ve Mühendisliği Derg., 13, (1), pp. 1–10, [Online]. Available: https://dergipark.org.tr/tr/pub/tbbmd/issue/53711/657649.
  2. Cömert, Z., Kocamaz, A. F., & Çıbuk, M. (2015). “Web Tabanlı Hibrit Bir Uygulama Modeliyle Personel Bilgi Sistemi Tasarımı”. Akademik Bilişim. Eskişehir.
  3. Z. Cömert ve A. F. Kocamaz, (2015), "Determination of QT Interval on Synthetic Electrocardiogram", Sinyal İşleme ve İletişim Kurultayı, Malatya.

E. Projects 

  1. TUBİTAK 1512, Teknogirişim Sermayesi Desteği Programı (BiGG), Kardiyotokografi Cihazına Dayalı Bilgisayarlı Fetal Kalp Hızı Analiz Sisteminin Geliştirilmesi, Yürütücü
  2. Kamu Kurumları ile Ortak Geliştirilen Proje, (2014-2016),Egzoz Gazı Emisyon Ölçümü Takip Sistemi Projesi”, Bitlis Eren Üniversitesi, Çevre ve Şehircilik Bakanlığı, Araştırmacı
  3. Kamu Kurumları ile Ortak Geliştirilen Proje, (2014-2016), Afet Riski Haritası Hazırlanması, Coğrafi Bilgi Sistem Tabanlı Otomasyon Sistemlerinin Araştırılması ve Geliştirilmesi İşi”, Bitlis Eren Üniversitesi, Çevre ve Şehircilik Bakanlığı, Araştırmacı
  4. TUBITAK 1001 Projesi, (2012-2014), “Temel Bilgi Teknolojisi Becerilerinin Kazandırılmasına Yönelik Geliştirilen Otomatik Değerlendirme ve Geri Bildirim Sistemi İle Verilecek Sosyal Paylaşım Temelli ve Bilgisayar Temelli Geri Bildirimin Öğrenme Performansına ve Öz-Yeterlilik Algısına Etkisi”, Burslu doktora öğrencisi.
  5. Bitlis Eren Üniversitesi ve Saban Üniversitesi İşbirliği ile Geliştirilen Meslek Edindirme Projesi, (2012-2013) “Bilişim Teknolojileri Meslek Edindirme Programı (BİTMEP)”, Eğitmen

E. Chapters

  1. Subha Velappan, Manivanna Boopathi Arumugam, and Zafer Cömert, Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction TechniquesHandbook of Artificial Intelligence in Biomedical Engineering (2020), Hard ISBN: 9781771889209. 
  2. Cömert Z, Akbulut Y, Akpinar MH, Alçin ÖF, Budak Ü, Aslan M, et al. (2020) Electrocardiogram beat classification using deep convolutional neural network techniques. Model. Anal. Act. Biopotential Signals Heal. Vol. 1, IOP Publishing; 2020, p. 12–26. doi:10.1088/978-0-7503-3279-8ch12.
  3. Alçin ÖF, Budak Ü, Aslan M, Akbulut Y, Cömert Z, Akpınar MH, et al. (2020) Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns. Model. Anal. Act. Biopotential Signals Heal. Vol. 1, IOP Publishing; 2020, p. 8–23. doi:10.1088/978-0-7503-3279-8ch8.
  4. Z. Cömert, A.F. Kocamaz, Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach, in: R. Silhavy (Ed.), Softw. Eng. Algorithms Intell. Syst., Springer International Publishing, Cham, 2019: pp. 239–248.

 

 

Research Group

Abdülkadir ŞENGÜR

Prof. Dr.

Fırat University
Turkey

Agnese SBROLLİNİ

Ph.D. Candidate

Università Politecnica delle Marche
Italy

Al-yousif SHAHAD

Dr.

Management and Science University
Malaysia

Burhan ERGEN

Assoc. Prof.

Firat University
Turkey

Erdal BAŞARAN

Asst. Prof.

Ağrı İbrahim Çeçen University
Turkey

Fatih KOCAMAZ

Assoc. Prof.

İnönü University, Department of Computer Engineering
Turkey

İbrahim AYAZ

Master Student

Muş Alparslan University
Turkey

İlter DEDE

Opt. Dr.

Zeynep Kamil Education and Research Hospital
Turkey

Kemal POLAT

Prof. Dr.

Abant İzzetbaysal University
Turkey

Laura BURATTİNİ

Assoc. Prof.

Polytechnic University, Ancoda
Italy

Manivanna Boopathi ARUMUGAM

Associate Professor

Bahrain Training Institute
Kingdom of Bahrain

Mesut TOĞAÇAR

Firat University
Turkey

Musa ÇIBUK

Dr.

Bitlis Eren University
Turkey

Sami GÜNGÖR

Opt. Dr.

Medical Park Hospital, Department of Obstetrics and Gynecology
Turkey

Subha VELAPPAN

Dr.

Manonmaniam Sundaranar University, Department of Computer Science and Engineering
India

Ümit BUDAK

Dr.

Bitlis Eren University
Turkey

Yüksel ÇELİK

Asst. Prof.

Karabük University
Turkey

Zafer CÖMERT

Dr.

Samsun University
Turkey

Zhang YANG

Ph.D. Candidate

Hangzhou Dianzi University
China

Papers From CtgAnalysis Research Group

  1. Determination of Tympanic Membrane Region in the Middle Ear Otoscope Images with Convolutional Neural Network Based YOLO Method
  2. Detection of weather images by using spiking neural networks of deep learning models
  3. Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks
  4. Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
  5. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
  6. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
  7. A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
  8. Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
  9. Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
  10. BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
  11. Fusing fine-tuned deep features for recognizing different tympanic membranes
  12. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
  13. Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders
  14. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
  15. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
  16. Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks
  17. Convolutional neural network approach for automatic tympanic membrane detection and classification
  18. Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
  19. DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
  20. Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
  21. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
  22. Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
  23. Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
  24. Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  25. Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
  26. The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
  27. Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
  28. A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
  29. A study of artificial neural network training algorithms for classification of cardiotocography signals
  30. Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
  31. Using wavelet transform for cardiotocography signals classification
  32. Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
  33. A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
  34. Cardiotocography signals with artificial neural network and extreme learning machine
  35. Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
  36. Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
  37. Open-access software for analysis of fetal heart rate signals
  38. Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  39. Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
  40. A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces