Araştırma Makalesi
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Dalgacık Aşırı Öğrenme Makinesi Otomatik Kodlayıcılarına Dayalı Öğretmenlerde EEG, EMG ve EKG Tabanlı Psikososyal Risk Düzeylerinin Belirlenmesi

Yıl 2022, Cilt: 25 Sayı: 3, 985 - 989, 01.10.2022
https://doi.org/10.2339/politeknik.886593

Öz

Kutsal bir iş yapan öğretmenler birçok psikososyal riskle karşı karşıyadırlar. Bu riskler genellikle okul yönetimi, öğrenciler ve çevresel faktörlerden kaynaklanabilir. Makine öğrenimi ve veri madenciliği yaklaşımları son zamanlarda sosyal ve eğitim araştırmalarında bir hayli ilgi görmüştür. Bu çalışmada öğretmenlerin psikososyal risk düzeylerini tahmin etmek için veri artırmaya ve veri sınıflandırmaya dayalı yeni bir yaklaşım önerilmiştir. Veri artırma, aşırı öğrenme makinesi tabanlı otomatik kodlayıcıları (AÖM-OK) kullanılarak gerçekleştirilir. Daha spesifik olarak, dalgacık aktivasyon fonksiyonu ileentegre edilen AÖM-OK, DAÖM-OK adı verilen yeni bir yaklaşımın geliştirilmesini sağlamıştır. Veri artırmanın ardından öğretmenlerin psikososyal risk düzeylerinin tahmininde geleneksel bir AÖM sınıflandırıcısı kullanılmıştır. Önerilen yöntemin performans değerlendirilmesi için Elektrokardiyografi (EKG), Elektromiyografi (EMG) ve Elektroensefalografi (EEG) içeren bir veri kümesi kullanılmıştır. Sınıflandırma doğruluğu, değerlendirme ölçütü olarak kullanılmıştır. Tüm kodlamalar MATLAB'de yapılmış ve önerilen yöntemle % 99,9 doğruluk elde edilmiştir. Karar ağaçları (KA), destek vektör makineleri (DVM) ve K-en yakın komşu (KYK) gibi bazı makine öğrenimi teknikleriyle de performans karşılaştırması yapılmıştır. Sonuçlar, önerilen DAÖM-OK ve DAÖM sınıflandırıcısının karşılaştırılan yöntemlerden daha iyi performans gösterdiğini göstermiştir.

Kaynakça

  • [1] Villalobos, G. H., Vargas, A. M., Rondón, M. A., & Felknor, S. A., “Validation of new psychosocial factors questionnaires: A Colombian national study”, American journal of industrial medicine, 56(1): 111-123, (2013).
  • [2] Souto, I., Pereira, A., Brito, E., Sancho, L., & Barros, S., “Occupational Health Risk Among Teachers in Higher Education”, In International Conference on Healthcare Ergonomics and Patient Safety, 311-322. Springer, Cham, (2019).
  • [3] Jemeļjanenko, A., & Geske, A., “Management of Psychosocial Risks in The Educational Sector Of Latvia”, In Proceedings of the International Scientific Conference. Volume VI (Vol. 215, p. 223), (2019).
  • [4] Heredia, S. A., Morales, M. F., Infante, R., Sanchez, D., Paez, C., & Gabini, S., “Psychosocial risk factors in university teachers”, Revista Espacios, 39(49), (2018).
  • [5] Mosquera, R., Parra-Osorio, L., & Castrillón, O. D., “Prediction of Psychosocial Risks in Colombian Teachers Public Schools Using Machine Learning Techniques”, Revista de la Universidad Nacional de Colombia, 7(29), 267-281, (2018).
  • [6] Ekici S., Turhan M., “Pychosocial Risk Level Identification for Teachers Using Machine Learning Algorithms”, 3. International Battalgazi Science Conference, 21-23 Sept. pp. 406-410, (2019).
  • [7] Viloria, A., López, J. R., Llinás, N. O., Mercado, C. V., Coronado, L. E. L., Sepulveda, A. M. N., & Lezama, O. B. P. “Prediction of Psychosocial Risks in Teachers Using Data Mining”, In Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies (pp. 501-508). Springer, Singapore, (2020).
  • [8] Huang, G. B., Zhu, Q. Y., & Siew, C. K., “Extreme learning machine: theory and applications”, Neurocomputing, 70(1-3): 489-501, (2006).
  • [9] Alcin, O. F., Sengur, A., Ghofrani, S., & Ince, M. C., “GA-SELM: Greedy algorithms for sparse extreme learning machine”, Measurement, 55: 126-132, (2014).
  • [10] Sun, K., Zhang, J., Zhang, C., & Hu, J., “Generalized extreme learning machine autoencoder and a new deep neural network”, Neurocomputing, 230: 374-381, (2017).
  • [11] Rafiei, M., Niknam, T. and Khooban, M., "Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network," IEEE Transactions on Industrial Informatics, 3(1):71-79, ( 2017).
  • [12] Güner, Ahmet, Ömer Faruk Alçin, and Abdulkadir Şengür. "Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features." Measurement 145: 214-225, (2019).
  • [13] Alcin, Omer Faruk, Abdulkadir Sengur, and Melih Cevdet Ince. "Forward-backward pursuit based sparse extreme learning machine." Journal of The Faculty of Engineering and Architecture of Gazi University 30.1: 111-117, (2015).

EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders

Yıl 2022, Cilt: 25 Sayı: 3, 985 - 989, 01.10.2022
https://doi.org/10.2339/politeknik.886593

Öz

Teachers who perform a sacred work are faced with many psychosocial risks. These risks can often be caused by the school administration, the students, and environmental factors. Machine learning and data mining approaches have recently gained much attention in social and educational researches. In this study, a novel approach, which is based on data augmentation and data classification, is proposed for the prediction of the psychosocial risk levels of the teachers. The data augmentation is carried out by using an extreme learning machine autoencoders (ELM-AE). More specifically, the wavelet activation function is incorporated into the ELM-AE to develop a novel approach called WELM-AE. After data augmentation, a traditional ELM classifier is used in the prediction of the psychosocial risk levels of teachers. A dataset, which contains physiological factors, namely Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG), is used to evaluate the performance of the proposed method. Classification accuracy is used as the evaluation metric. All coding is carried out in MATLAB, and a 99.9% accuracy score is obtained with the proposed method. A performance comparison is also carried out with some machine learning techniques, namely decision trees (DT), support vector machines (SVM), and K-nearest neighbour (KNN). The results show that the proposed WELM-AE and ELM classifier outperform the compared methods.

Kaynakça

  • [1] Villalobos, G. H., Vargas, A. M., Rondón, M. A., & Felknor, S. A., “Validation of new psychosocial factors questionnaires: A Colombian national study”, American journal of industrial medicine, 56(1): 111-123, (2013).
  • [2] Souto, I., Pereira, A., Brito, E., Sancho, L., & Barros, S., “Occupational Health Risk Among Teachers in Higher Education”, In International Conference on Healthcare Ergonomics and Patient Safety, 311-322. Springer, Cham, (2019).
  • [3] Jemeļjanenko, A., & Geske, A., “Management of Psychosocial Risks in The Educational Sector Of Latvia”, In Proceedings of the International Scientific Conference. Volume VI (Vol. 215, p. 223), (2019).
  • [4] Heredia, S. A., Morales, M. F., Infante, R., Sanchez, D., Paez, C., & Gabini, S., “Psychosocial risk factors in university teachers”, Revista Espacios, 39(49), (2018).
  • [5] Mosquera, R., Parra-Osorio, L., & Castrillón, O. D., “Prediction of Psychosocial Risks in Colombian Teachers Public Schools Using Machine Learning Techniques”, Revista de la Universidad Nacional de Colombia, 7(29), 267-281, (2018).
  • [6] Ekici S., Turhan M., “Pychosocial Risk Level Identification for Teachers Using Machine Learning Algorithms”, 3. International Battalgazi Science Conference, 21-23 Sept. pp. 406-410, (2019).
  • [7] Viloria, A., López, J. R., Llinás, N. O., Mercado, C. V., Coronado, L. E. L., Sepulveda, A. M. N., & Lezama, O. B. P. “Prediction of Psychosocial Risks in Teachers Using Data Mining”, In Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies (pp. 501-508). Springer, Singapore, (2020).
  • [8] Huang, G. B., Zhu, Q. Y., & Siew, C. K., “Extreme learning machine: theory and applications”, Neurocomputing, 70(1-3): 489-501, (2006).
  • [9] Alcin, O. F., Sengur, A., Ghofrani, S., & Ince, M. C., “GA-SELM: Greedy algorithms for sparse extreme learning machine”, Measurement, 55: 126-132, (2014).
  • [10] Sun, K., Zhang, J., Zhang, C., & Hu, J., “Generalized extreme learning machine autoencoder and a new deep neural network”, Neurocomputing, 230: 374-381, (2017).
  • [11] Rafiei, M., Niknam, T. and Khooban, M., "Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network," IEEE Transactions on Industrial Informatics, 3(1):71-79, ( 2017).
  • [12] Güner, Ahmet, Ömer Faruk Alçin, and Abdulkadir Şengür. "Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features." Measurement 145: 214-225, (2019).
  • [13] Alcin, Omer Faruk, Abdulkadir Sengur, and Melih Cevdet Ince. "Forward-backward pursuit based sparse extreme learning machine." Journal of The Faculty of Engineering and Architecture of Gazi University 30.1: 111-117, (2015).
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Dönüş Şengür 0000-0002-8786-6557

Yayımlanma Tarihi 1 Ekim 2022
Gönderilme Tarihi 25 Şubat 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 25 Sayı: 3

Kaynak Göster

APA Şengür, D. (2022). EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi, 25(3), 985-989. https://doi.org/10.2339/politeknik.886593
AMA Şengür D. EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi. Ekim 2022;25(3):985-989. doi:10.2339/politeknik.886593
Chicago Şengür, Dönüş. “EEG, EMG and ECG Based Determination of Psychosocial Risk Levels in Teachers Based on Wavelet Extreme Learning Machine Autoencoders”. Politeknik Dergisi 25, sy. 3 (Ekim 2022): 985-89. https://doi.org/10.2339/politeknik.886593.
EndNote Şengür D (01 Ekim 2022) EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi 25 3 985–989.
IEEE D. Şengür, “EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders”, Politeknik Dergisi, c. 25, sy. 3, ss. 985–989, 2022, doi: 10.2339/politeknik.886593.
ISNAD Şengür, Dönüş. “EEG, EMG and ECG Based Determination of Psychosocial Risk Levels in Teachers Based on Wavelet Extreme Learning Machine Autoencoders”. Politeknik Dergisi 25/3 (Ekim 2022), 985-989. https://doi.org/10.2339/politeknik.886593.
JAMA Şengür D. EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi. 2022;25:985–989.
MLA Şengür, Dönüş. “EEG, EMG and ECG Based Determination of Psychosocial Risk Levels in Teachers Based on Wavelet Extreme Learning Machine Autoencoders”. Politeknik Dergisi, c. 25, sy. 3, 2022, ss. 985-9, doi:10.2339/politeknik.886593.
Vancouver Şengür D. EEG, EMG and ECG based Determination of Psychosocial Risk Levels in Teachers based on Wavelet Extreme Learning Machine Autoencoders. Politeknik Dergisi. 2022;25(3):985-9.
 
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