Özgür ve Açık Kaynak Kodlu Yazılım Platformlarının Uygulamalı Yapay Zeka Eğitimlerine Katkısı

Year 2021, , 11 - 14, 30.04.2021

Hıdır Selçuk , T. Çetin Akıncı , Şahin Serhat Şeker

https://doi.org/10.47769/izufbed.859979

Abstract

Açık Kaynak Kodlu (AKK) yazılım ortamları öğrencilere ve araştırmacılara yapay zeka alanında geniş uygulama yapma ve model geliştirme imkanı sunmaktadır. Teknolojinin gelişmesiyle AKK yazılım ortamlarının sayısının artması sonucunda veri bilimi ve veri mühendisliğinde gelişmeler olmuş ve açık kaynak veri tabanları ortaya çıkmıştır. Açık kaynak veri tabanları, bir kapalı kaynak kodlu (KKK) yazılım ortamı olan MATLAB içerisinde kullanılarak yapay zeka alanında yeni gelişmelere yol açmıştır. Bugün tensorflow ve keras gibi açık kaynak yazılım kütüphaneleri sayesinde öğrenciler yapay zeka alanında özgürce tasarım yapma ve geliştirme imkanına sahip olabilmektedir. Bu çalışmada AKK yazılım ortamlarının ve açık kaynak veri tabanlarının yapay zeka eğitimine yaptığı katkılar ele alınmış ve tartışılmıştır. Öğrencilerin üniversite eğitimi süresince AKK yazılım platformlarına erişebilmelerini sağlayacak ve onlara AKK platformunda uygulamalar ve yeni tasarımlar yapma becerisi kazandırabilecek imkanlar sunmak için ilgili bölümlerin müfredat programlarında değişiklikler yapılması gerekliliği ortaya çıkmıştır.

Keywords

Yapay zeka,, Eğitim,, Açık kaynak kodlu (AKK),, yazılım,, , Kapalı kaynak kodlu (KKK),, yazılım mühendisliği

References

• Akinci T. C., Nogay, H. S. , Gokmen, G. (2011). Determination of optimum operation cases in electric arc welding machine using neural network”, Journal of Mechanical Science and Technology, 25: 1003-1010.
• Akinci, T.C., Nogay, H. S., Guseinoviene, E., Dikun, J., Seker, S., “Application of ANN for Short Term Forecasting of Wind Power Density. (2016). Renewable Energy and Innovative Technologies, Smolyan, Bulgaristan, 10 -11 June, c1 : 157-163.
• Akinci, T. C. , Nogay, H.S. (2012). Wind Speed Correlation Between Neighboring Measuring Stations”, Arabian Journal for Science and Engineering, 37:1007-1019.
• Bhattacharya, S., Czejdo, B., Agrawal, R., Erdemir, E., Gokaraju, B. (2018). Open Source Platforms and Frameworks for Artificial Intelligence and Machine Learning. SoutheastCon, 19-22 April, IEEE St. Petersburg, FL, USA.
• Birbir Y. Nogay, H. S. (2007). Application of Artificial Neural Network for Harmonic Estimation in Different Produced Induction Motors. International Journal of Circuits, Systems and Signal Processing, 4: 334-340, 2007.
• Birbir Y., Nogay H. S., Ozel Y. (2007). Neural Network Solution to Low Order Odd Current Harmonics in Short Chorded Induction Motors. International Journal of Systems Applications, Engineering & Development, 1:21-28, 2007.
• DeKoenigsberg, G. (2008). How Successful Open Source Projects Work, and How and Why to Introduce Students to the Open Source World. 21st Conference on Software Engineering Education and Training, 14-17 April, Charleston, SC, USA.
• Dorodchi, M., Dehbozorgi, N. (2016). Utilizing Open Source Software in Teaching Practicebased Software Engineering Courses. 2016 IEEE Frontiers in Education Conference (FIE), 12-15 Oct. Erie, PA, USA.
• Drummond, D. E., Alto, P. (2016). Open sourcing education for Data Engineering and Data Science. IEEE Frontiers in Education Conference (FIE), 12-15 Oct. Erie, PA, USA.
• Ersoz S., Akinci T. C., Nogay H. S., Dogan G. (2013). Determination of Wind Energy Potential in Kirklareli-Turkey”, International Journal of Green Energy, 10:103-116.
• Hawthorne M., J., Perry, D. E. (2005). Software Engineering Education in the Era of Outsourcing, Distributed Development, and Open Source Software: Challenges and Opportunities. Proceedings. 27th International Conference on Software Engineering ICSE,15-21 May, 2 Saint Louis, MO, USA.
• Hislop, G. W., Ellis, H. J. C. (2017). Humanitarian Open Source Software in Computing Education. Computer IEEE, 50(10):98-101.
• Hu, Z., Song, Y., Gehringe, E.F. (2018). Open-Source Software in Class: Students’ Common Mistakes. IEEE/ACM 40th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 25 May-3 June, Gothenburg, Sweden.
• Hung, C. K. (2018). Making Machine-Learning Tools Accessible to Language Teachers and Other Non-Techies: t-sne-lab and Rocanr as First Examples. IEEE 8th International Conference on Awareness Science and Technology (iCAST), 8-10 Nov. Taichung, Taiwan.
• Kusbeyzi, I., Hacinliyan, A., Aybar, OO. (2011). Open source software in teaching mathematics”, Procedia Social and Behavioral Sciences, (15): 769–771, 2011.
• Lynch, C., O’Leary, C., Smith, G., Bain, R., Kehoe, J., Vakaloudis, A., Linger, R. (2020). A review of open-source machine learning algorithms for twitter text sentiment analysis and image classification. International Joint Conference on Neural Networks (IJCNN), IEEE, 19-24 July, Glasgow, United Kingdom.
• Nogay, H. S. (2017). Deep Convolutional Neural Networks To Detect Lung Cancer Stage, The Journal of Cognitive Systems, 2: 33-36.
• Nogay, H. S., Akıncı, T. Ç., Erdemir, G. (2018a). A Convolutional Neural Network Application For The Classification Of Lung Cancer Types. Academic Journal Industrial Technologies, 5:7-12.
• Nogay, H. S., Akıncı, T. Ç., Erdemir, G. (2018b). Estimation Of Head & Neck Cancer Stage By Using Deep Convolutional Neural Networks. Academic Journal Industrial Technologies, 5: 13-19.
• Nogay, H. S. (2020). Prediction of Post-Treatment Survival Expectancy in Head & Neck Cancers by Machine Learning Methods”, The Journal of Cognitive Systems, 5(1): 23-32.
• Nogay, H. S. (2018). Classification Of Different Cancer Types By Deep Convolutional Neural Networks. Balcan Journal of Electrical&Computer Engineering, 5: 56-59.
• Nogay, H. S., Akıncı, T. C. (2018). A Convolutional Neural Network Application For Predicting The Locating Of Squamous Cell Carcinoma In The Lung. Balkan Journal of Electrical & Computer Engineering, 6: 207-210.
• Nogay, H. S., Adeli, H. (2020). Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging, Reviews In The Neurosciences, DOI: 10.1515/revneuro-2020-0043, 1-17.
• Nogay, H. S., Akıncı T. Ç. (2020). Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks, Neural Computing & Applications, https://doi.org/10.1007/s00521-020-05436-y, 1-14.
• Nogay, H. S., Akinci, T. C. (2012). Long term wind speed estimation for a randomly selected time interval by using artificial neural networks, Amasra, Turkey”, Energy Education Science and Technology Part A-Energy Science and Research, 28:759-772.
• Nogay H. S., Akinci T. C., Eidukeviciute M. (2012a) Application of artificial neural networks for short term wind speed forecasting in Mardin, Turkey. Journal of Energy in Southern Africa, 23:2-7.
• Noğay, H. S., Akıncı, T. Ç. (2019). Application of decision tree methods for wind speed estimation”, European Journal of Technique, cilt.9, ss.74-83.
• Nogay, H., S. (2016a). Determination Leakage Reactange in Monophase Transformers Using by Cascaded Neural Network”, Balcan Journal of Electrical and Electronical Engineering, 4: 89-96.
• Nogay, H. S., Akinci T. C., Guseinoviene E. (2012b).Determination of effect of slot form on slot leakage flux at rotating electrical machines by the method of artificial neural networks”, Energy Education Science and Technology Part A-Energy Science and Research, 29:451-462, 2012.
• Nogay, H. S. (2008). A Neural Network Solution to Design Dual Stator Winding Insulation Level Detector for Three Phase Induction Motors. WSEAS Transactions on Advances in Engineering Education, 10s.234-240.
• Nogay, H. S. (2011). Prediction of internal temperature in stator winding of three-phase induction motors with ann”, European Transactions on Electrical Power, 21:120-128.
• Nogay, H. S. (2016b). Asenkron Motorda Yapay Sinir Ağları ile Durum Kestirimi. Electronic Journal of Vocational Colleges, 6 : 41-48.
• Pinto, G., Ferreira, C., Souza, C., Steinmacher, I., Meirelles, P. (2019). Training Software Engineers sing Open-Source Software: The Students’ Perspective. IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 25-31 May, Montreal, QC, Canada.
• Pinto, G., Ferreira, C., Souza, C., Steinmacher, I., Meirelles, P. (2017). Training Software Engineers sing Open-Source Software: The Professors’ Perspective. The 30th IEEE Conference on Software Engineering Education and Training, 7-9 Nov., Savannah, GA, USA.
• Seker, S. S., Akinci, T. C., Nogay, H. S. (2013). Forecasting of wind speed and directions in Kirklareli, Turkey by simple multilayer perceptron, International Symposium on Sustainable Development (ISSD2013), Saraybosna, Bosna-Hersek, 13 Kasım.
• Serteller, N.F.O., Bektas Y., Nogay, S., Akinci, T.C. (2012). Speed Estimation of Brushless Direct Current (BLDC) Motor with Multilayer Perceptron. Przegland Elektrotechniczny, 88:255-260.
• Suen, H.Y., Hung, K.E., LIN, C.L. (2019). TensorFlow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews. IEEE Access, DOI: 10.1109/ACCESS.2019.2902863.
• Tang, L., Mao, X., Zhang, Z. (2019). Language to Code with Open Source Software. IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), 18-20 Oct. Beijing, China.
• Tao, Y., Nandigam, J. (2006). Work in Progress: Open Source Software as the Basis of Developing Software Design Case Studies. Proceedings. Frontiers in Education. 36th Annual Conference, 27-31 Oct. San Diego, CA, USA.
• Thar, K., Tran, N.H., Oo. T. Z., Hong, C. S. (2018). DeepMEC: Mobile Edge Caching Using Deep Learning, IEEE Access, (6): 78260-78275. Xie, Y., Qian, K., He, J. (2016). Multi-dimensional and Customizable Open-Source Labware for Promoting Big Data Analytical Skills in STEM Education. 2016 IEEE Frontiers in Education Conference (FIE), 12-15 Oct. Erie, PA, USA.
• Volkovas, V., Eidukeviciute, M., Nogay, H. S. , Akinci, T. C. (2012). Application of wavelet transform to defect detection of building's structure, Mechanika, ss.683-690.
• Yun, J., Woo, J. (2020). A Comparative Analysis of Deep Learning and Machine Learning on Detecting Movement Directions Using PIR Sensors. IEEE Internet of Things Journal, 7(4):2855-2868.