[1] B. Unutmaz, A. C. Karaca, and M. K. Gullu, “Turkish Sign Language Recognition Using Kinect Skeleton and Convolutional Neural Network,” in 2019 27th Signal Processing and Comm. Appl. Conference (SIU), Apr. 2019, pp. 1–4. https://doi.org/10.1109/siu.2019.880638010.1109/SIU.2019.8806380
[2] P. Kumar, P. P. Roy, and D. P. Dogra, “Independent Bayesian Classifier Combination Based Sign Language Recognition Using Facial Expression,” Information Sciences, vol. 428, pp. 30–48, Feb. 2018. https://doi.org/10.1016/j.ins.2017.10.04610.1016/j.ins.2017.10.046
[3] H. Haberdar and S. Albayrak, “Real Time Isolated Turkish Sign Language Recognition from Video Using Hidden Markov Models with Global Features,” Lecture Notes in Computer Science, pp. 677–687, 2005. https://doi.org/10.1007/11569596_7010.1007/11569596_70
[6] B. Oktekin, “İşitme ve Konuşma Engelli Bireyler için İşaret Tanıma Sistemi Geliştirme,” Uluslararasi Kibris Universitesi Fen-Edebiyat Fakultesi, vol. 25, no. 97-1, pp. 593–609, Jan. 2019. https://doi.org/10.22559/folklor.96910.22559/folklor.969
[7] Y. Mori and M. Toyonaga, “Data-Glove for Japanese Sign Language Training System with Gyro-Sensor,” in 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), Dec. 2018, pp. 1354–1357. https://doi.org/10.1109/scis-isis.2018.0021110.1109/SCIS-ISIS.2018.00211
[8] B. Demircioǧlu, G. Bülbül, and H. Köse, “Turkish Sign Language Recognition with Leap Motion [Leap Motion ile Türk Işaret Dili Tanima],” in 2016 24th Signal Processing and Communication Application Conference (SIU), May 2016. https://doi.org/10.1109/siu.2016.749580910.1109/SIU.2016.7495809
[10] A. Memiş and S. Albayrak, “A Kinect Based Sign Language Recognition System Using Spatio-Temporal Features,” in Sixth International Conference on Machine Vision (ICMV 2013), Dec. 2013. https://doi.org/10.1117/12.205101810.1117/12.2051018
[11] O. Yalcinkaya, A. Atvar, and P. Duygulu, “Turkish Sign Language Recognition Application Using Motion History Image,” in 2016 24th Signal Processing and Communication Application Conference (SIU), May 2016, pp. 801–804. https://doi.org/10.1109/siu.2016.749586110.1109/SIU.2016.7495861
[12] E. Ezel, O. Baykan, “Vision-Based Turkish Sign Language Recognition Using Convolutional Neural Networks,” in International Conference on Theoretical and Applied Computer Science and Engineering (ICTACSE, 2017), Nov. 2017, p. 14.
[13] M. Aktas, B. Gokberk, and L. Akarun, “Recognizing Non-Manual Signs in Turkish Sign Language,” in 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov. 2019, pp. 1–6. https://doi.org/10.1109/ipta.2019.893608110.1109/IPTA.2019.8936081
[14] S. G. Moreira Almeida, F. G. Guimarães, and J. Arturo Ramírez, “Feature Extraction in Brazilian Sign Language Recognition Based on Phonological Structure and Using RGB-D Sensors,” Expert Systems with Applications, vol. 41, no. 16, pp. 7259–7271, Nov. 2014. https://doi.org/10.1016/j.eswa.2014.05.02410.1016/j.eswa.2014.05.024
[15] W. Tao, M. C. Leu, and Z. Yin, “American Sign Language Alphabet Recognition Using Convolutional Neural Networks with Multiview Augmentation and Inference Fusion,” Engineering Applications of Artificial Intelligence, vol. 76, pp. 202–213, Nov. 2018. https://doi.org/10.1016/j.engappai.2018.09.00610.1016/j.engappai.2018.09.006
[18] M. Mohandes, S. Aliyu, and M. Deriche, “Arabic Sign Language Recognition Using the Leap Motion Controller,” in 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), Jun. 2014, pp. 960–965. https://doi.org/10.1109/isie.2014.686474210.1109/ISIE.2014.6864742
[19] A. Karacı, K. Akyol, and Y. Gültepe, “Turkish Sign Language Alphabet Recognition with Leap Motion,” in Proceedings of the International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES’18)’, May 2018, pp. 189–192.
[20] A. Vaitkevičius, M. Taroza, T. Blažauskas, R. Damaševičius, R. Maskeliūnas, and M. Woźniak, “Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion,” Applied Sciences, vol. 9, no. 3, pp. 445, Jan. 2019. https://doi.org/10.3390/app903044510.3390/app9030445
[23] A. Karacı and A. Kemal, “Classification of Alcohols Obtained From QCM Sensors Using Hybrid Ensemble Classifier’, in 2nd International Turkish World Engineering and Science Congress, Nov. 2019, pp. 159–163.
[24] A. Karaci, A. Caglar, B. Aydinli, and S. Pekol, “The Pyrolysis Process Verification of Hydrogen Rich Gas (H–rG) Production by Artificial Neural Network (ANN),” International Journal of Hydrogen Energy, vol. 41, no. 8, pp. 4570–4578, Mar. 2016. https://doi.org/10.1016/j.ijhydene.2016.01.09410.1016/j.ijhydene.2016.01.094
[25] A. Karacı, “Self-Care Problems Classification of Children with Physical and Motor Disability by Deep Neural Networks,” J. Polytech., vol. 23, no. 2, pp. 333–341, 2020.10.2339/politeknik.522641
[26] A. Karaci, “Predicting Breast Cancer with Deep Neural Networks” in Lecture Notes on Data Engineering and Communications Technologies, D. Hemanth, U. Kose, Eds. 2020, pp. 996–1003.10.1007/978-3-030-36178-5_88
[29] P. Kumar, R. Saini, S. K. Behera, D. P. Dogra, and P. P. Roy, “Real-Time Recognition of Sign Language Gestures and Air-Writing Using Leap Motion,” in 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), May 2017, pp. 157–160. https://doi.org/10.23919/mva.2017.798682510.23919/MVA.2017.7986825