The Academic Perspective Procedia publishes Academic Platform symposiums papers as three volumes in a year. DOI number is given to all of our papers.
Publisher : Academic Perspective
Journal DOI : 10.33793/acperpro
Journal eISSN : 2667-5862
 P. D. Karakurt, “Hipertansiyon ve Evde Bakım,” Atatürk üniversitesi Hemşirelik Yüksekokulu Derg., vol. 10, no. 1, pp. 97–104, 2007, Accessed: Jun. 03, 2021. [Online]. Available: https://dergipark.org.tr/en/pub/ataunihem/issue/2636/33917.
 World Health Organization, “A global brief on hypertension | World Health Day 2013,” Switzerland, 2013. Accessed: Jul. 05, 2021. [Online]. Available: www.who.int.
 World Health Organization, “Hypertension,” World Health Organization, 2021. https://www.who.int/news-room/fact-sheets/detail/hypertension (accessed Jul. 05, 2021).
 E. öncü, “Sağlık Okuryazarlığının Hipertansiyon Kontrolünde önemi,” Dünya İnsan Bilim. Derg., vol. 2018, no. 1, pp. 45–70, May 2018, Accessed: Jun. 03, 2021. [Online]. Available: https://dergipark.org.tr/en/pub/insan/694112.
 J. Esmaelpoor, M. H. Moradi, and A. Kadkhodamohammadi, “A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals,” Comput. Biol. Med., vol. 120, p. 103719, May 2020, doi: 10.1016/j.compbiomed.2020.103719.
 A. Soltan zadi, R. Alex, R. Zhang, D. E. Watenpaugh, and K. Behbehani, “Arterial blood pressure feature estimation using photoplethysmography,” Comput. Biol. Med., vol. 102, pp. 104–111, Nov. 2018, doi: 10.1016/j.compbiomed.2018.09.013.
 A. S. Alghamdi, K. Polat, A. Alghoson, A. A. Alshdadi, and A. A. Abd El-Latif, “A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods,” Appl. Acoust., vol. 164, p. 107279, Jul. 2020, doi: 10.1016/j.apacoust.2020.107279.
 ü. Şentürk, K. Polat, and İ. Yücedağ, “A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks,” Appl. Acoust., vol. 170, p. 107534, Dec. 2020, doi: 10.1016/j.apacoust.2020.107534.
 ü. Şentürk, K. Polat, and İ. Yücedağ, “Towards wearable blood pressure measurement systems from biosignals: a review,” Turkish J. Electr. Eng. Comput. Sci., vol. 27, no. 5, pp. 3259–3281, Oct. 2019, doi: 10.3906/elk-1812-121.
 C. Landry, E. T. Hedge, R. L. Hughson, S. Peterson, and A. Arami, “Accurate Blood Pressure Estimation during Activities of Daily Living: A Wearable Cuffless Solution,” IEEE J. Biomed. Heal. Informatics, 2021, doi: 10.1109/JBHI.2021.3054597.
 C. Landry, E. T. Hedge, R. L. Hughson, S. D. Peterson, and A. Arami, “Wearable Physiological and Blood Pressure Measurements During Activities of Daily Living,” IEEE Dataport, 2021, doi: https://dx.doi.org/10.21227/wysp-gt69.
 R. Alpar, Uygulamalı istatistik ve geçerlilik güvenirlilik: Spor, sağlık ve eğitim bilimlerinden örneklerle, 2nd ed. Ankara: Detay Yayıncılık, 2016.
 K. Polat and K. Onur Koc, “Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All,” J. Artif. Intell. Syst., vol. 2, no. 1, pp. 80–97, Feb. 2020, doi: 10.33969/ais.2020.21006.
 M. K. Ucar, S. Orenc, M. R. Bozkurt, and C. Bilgin, “Evaluation of the relationship between Chronic Obstructive Pulmonary Disease and photoplethysmography signal,” in 2017 Medical Technologies National Congress (TIPTEKNO), Oct. 2017, pp. 1–4, doi: 10.1109/TIPTEKNO.2017.8238032.
 M. K. Uçar, Z. Uçar, K. Uçar, M. Akman, and M. R. Bozkurt, “Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence,” Biomed. Signal Process. Control, vol. 68, p. 102650, Jul. 2021, doi: 10.1016/j.bspc.2021.102650.
 M. K. Uçar, M. Nour, H. Sindi, and K. Polat, “The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets,” Math. Probl. Eng., vol. 2020, pp. 1–17, 2020, doi: 10.1155/2020/2836236.