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Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization

Yıl 2025, Cilt: 14 Sayı: 2, 247 - 258, 27.06.2025

Abdullah Şener , Serdal Poçan , Burhan Ergen

https://doi.org/10.46810/tdfd.1639446

Öz

Matematik eğitimine teknolojinin entegrasyonu, matematiksel kavram ve becerilerin anlaşılmasını artırmanın yanı sıra motivasyonu artırmak için de oldukça önemlidir. Bu durum özellikle geometri dersleri için geçerlidir; teknoloji, geometrik şekillerin algılanmasını kolaylaştırarak hem öğrenme hem de öğretme süreçlerini etkileyebilir. Bu bağlamda, yapay zeka ve derin öğrenme gibi yeni ortaya çıkan kavramlar, bu tür sınırlamaların aşılmasında birer araç olarak kullanılabilir.

Bu çalışma, öğretmenlerin dijital ortamlarda üç boyutlu geometrik şekiller çizerken karşılaştıkları zorlukları ele almaktadır. Dijital ortamlarda elle çizilen şekiller genellikle karmaşık olabilir ve öğretmenlerin doğru ve hassas çizimler oluşturmasını zorlaştırabilir. Derin öğrenme modelleri, öğretmenlere çizim hatalarını düzeltmede yardımcı olarak öğrencilerin geometrik kavramları öğrenmelerini kolaylaştıran daha net ve anlaşılır görseller sunmalarına olanak tanıyabilir.

Çalışma, çeşitli derin öğrenme modelleri kullanılarak elde edilen yüksek doğruluk oranlarına vurgu yaparak, bu modellerin geometrik şekilleri doğru bir şekilde sınıflandırmadaki etkileyici yeteneklerini ön plana çıkarmaktadır.

Anahtar Kelimeler

Yapay zeka derin öğrenme matematik eğitimi üç boyutlu şekiller

Kaynakça

  • Bray A., Tangney B. Technology usage in mathematics education research–A systematic review of recent trends. Computers & Education. 2017;114;255-273.
  • Chao W. H., Yang C. Y., Chang R. C. A study of the interactive mathematics mobile application development. 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII). 2018. p.248-249
  • Daly I., Bourgaize J., Vernitski A. Mathematical mindsets increase student motivation: Evidence from the EEG. Trends in Neuroscience and Education. 2019;15; 18–28.
  • Baykul Y. İlkokulda matematik öğretimi [Teaching mathematics in primary school].12th ed. Pegem Akademi; 2014.
  • Altun M. Eğitim fakülteleri ve sınıf öğretmenleri için matematik öğretimi [Teaching mathematics for education faculties and primary teachers]. 19th ed. Aktüel Alfa Akademi; 2015.
  • Sevimli E., Kul Ü. Evaluation of the contents of mathematics textbooks in terms of compliance to technology: Case of secondary school. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education. 2015. 9(1), 308-331.
  • Engelbrecht J., Llinares S., Borba M. C. Transformation of the mathematics classroom with the internet. ZDM Mathematics Education. 2020. 52(5), 825-841.
  • Azizul S. M. J., Din R. Teaching and learning geometry using GeoGebra software via MOOC. Journal of Personalized Learning. 2016. 2(1), 39-50.
  • Chen B., Bao L., Zhang R., Zhang J., Liu F., Wang S., Li M. A Multi-Strategy Computer-Assisted EFL Writing Learning System With Deep Learning Incorporated and Its Effects on Learning: A Writing Feedback Perspective. Journal of Educational Computing Research. 2023. 61(8), 60-102.
  • Park W., Kwon H. Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education. 2023. p.1-27.
  • Kearney M., Maher D. Mobile learning in pre-service teacher education: Examining the use of professional learning networks. Australasian Journal of Educational Technology. 2019. 35(1), 135-148.
  • Aksu H. H. Mathematics teachers' opinions on distance education using the Educational Informatics Network (EBA). Turkish Online Journal of Educational Technology. 2021. 20(2), 88-97.
  • Chassignol M., Khoroshavin A., Klimova A., Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science. 2018. 136, 16-24.
  • Shumway J. F., Welch L. E., Kozlowski J. S., Clarke-Midura J., Lee V. R. Kindergarten students’ mathematics knowledge at work: the mathematics forprogramming robot toys. Mathematical Thinking and Learning. 2021. p.1-29.
  • Iskrenovic-Momcilovic O. Improving geometry teaching with scratch. International Electronic Journal of Mathematics Education. 2020. 15(2), 5-8.
  • Roll I., Wylie R. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education. 2016. 26(2), 582-599.
  • Bussi M. B., Mariotti M. A. Semiotic mediation in the mathematics classroom: Artifacts and signs after a Vygotskian perspective. Handbook of international research in mathematics education. 2nd ed., 2008. pp. 746–805.
  • Bolliger D. U., Wasilik O. ( Factors influencing faculty satisfaction with online teaching and learning in higher education. Distance Education. 2009. 30(1), 103-116.
  • Jumadi A., Nasrudin F. S. M., Arunah N. S. K., Mohammad S. A., Abd Ghafar N., Zainuddin N. A. Students’ and lecturers’ perceptions of students’ difficulties in geometry courses. International Journal of Advanced Research in Education and Society. 2022. 4(2), 50-64.
  • Neubauer A. C. The future of intelligence research in the coming age of artificial intelligence–With a special consideration of the philosophical movements of trans-and posthumanism. Intelligence. 2021. 87, 101563.
  • Goralski M. A., Tan T. K. Artificial intelligence and sustainable development. The International Journal of Management Education. 2020. 18(1), 100330.
  • Zhang C., Lu Y. Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration. 2021.23, 100224.
  • Wang P., Liu K., Dougherty Q. Conceptions of artificial intelligence and singularity. Information. 2018. 9.4: 79.
  • Rapaport W. J. What is artificial intelligence? Journal of Artificial General Intelligence. 2020. 11(2), 52–56.
  • Bundy A. Preparing for the future of Artificial Intelligence. AI & Soc. 2017. 32, 285–287.
  • Mohamad Nasri N., Husnin H., Mahmud S. N. D., Halim L. Mitigating the COVID-19 pandemic: a snapshot from Malaysia into the coping strategies for pre-service teachers’ education. Journal of Education for Teaching. 2020. 46(4), 546-553.
  • Van Vaerenbergh, Steven; Pérez-Suay, Adrián. A classification of artificial intelligence systems for mathematics education. In: Mathematics education in the age of artificial intelligence: How artificial intelligence can serve mathematical human learning. Cham: Springer International Publishing. 2022. p. 89-106.
  • Sulistiowati D. L., Herman T., Jupri A. Student difficulties in solving geometry problem based on Van Hiele thinking level. In: Journal of Physics: Conference Series. IOP Publishing. 2019. p. 042118.
  • Wong W. K., Yin S. K., Yang H. H., Cheng Y. H. Using computer-assisted multiple representations in learning geometry proofs. Journal of Educational Technology & Society. 2011. 14(3), 43-54.
  • Fujita T., Kondo Y., Kumakura H., Kunimune S., Jones K. Spatial reasoning skills about 2D representations of 3D geometrical shapes in grades 4 to 9. Mathematics Education Research Journal. 2020. 32(2), 235-255.
  • Cohen C. A., Hegarty M. Sources of difficulty in imagining cross sections of 3D objects. In Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 29. No. 29. 2007.
  • Parmanov A. A., Mamatov J. A., Kahhorov M. J., Fayzullaev S. U. Main tasks of teaching geometry at the secondary school. Eastern European Scientific Journal. 2020. 1(2).57-59.
  • Sarama, J., Clements, D. H.. Clements. Early childhood mathematics education research: Learning trajectories for young children. Routledge, 2009.
  • Wojtowicz A., Wojtowicz B., Kopec K. Descriptive geometry in the time of COVID-19: Preliminary assessment of distance education during pandemic social isolation. Advances in Engineering Education. 2020. 8(4), 1-10.
  • Yurniwati Y., Utomo E. Problem-based learning flipped classroom design for developing higher-order thinking skills during the COVID-19 pandemic in geometry domain. In: Journal of Physics: Conference Series. IOP Publishing. 2020. p. 012057.
  • Carter Jr R. A., Rice M., Yang S., Jackson H. A. Self-regulated learning in online learning environments: strategies for remote learning. Information and Learning Sciences.2020. 121 (5/6), 321-329.
  • Lowrie T., Jorgensen R. Teaching mathematics remotely: Changed practices in distance education. Mathematics Education Research Journal, 24(3).2012. 371-383.
  • Haj-Yahya A. Do prototypical constructions and self-attributes of presented drawings affect the construction and validation of proofs? Mathematics Education Research Journal. 2020. 32(4), 685-718.
  • Murugan A., Tadesse F. W., Wondirad E. N., Bizuneh M. T., Firew A. Machine learning in identification of polygon shapes for recognition of mechanical engineering drawings. International Journal of Mechanical Engineering. 2022. 7(2), 2282-2288.
  • Patkin D., Plaksin O. Procedural and relational understanding of pre-service mathematics teachers regarding spatial perception of angles in pyramids. International Journal of Mathematical Education in Science and Technology. 2019. 50(1), 121-140.
  • Kepceoglu, I. Effect of dynamic geometry software on 3-dimensional geometric shape drawing skills. Journal of Education and Training Studies. 2018. 6(10), 98-106.
  • Šafhalter A., Glodež S., Šorgo A., Ploj Virtič M. Development of spatial thinking abilities in engineering 3D modeling course aimed at lower secondary students. International Journal of Technology and Design Education. 2022. 32, 167-184.
  • Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Shuai B., Liu T., Wang X., Wang G., Cai J., Chen T. Recent advances in convolutional neural networks. Pattern Recognition. 2018. 77, 354-377.
  • Min S., Lee B., Yoon S. Deep learning in bioinformatics. Briefings in Bioinformatics. 2017. 18(5), 851-869.
  • Hanbay K. Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform. Journal of the Faculty of Engineering and Architecture of Gazi University. 2020. 35(1), 443-456.
  • Kurt F. Analysis of the effects of hyperparameters in convolutıonal neural networks. (Publication No. 519157) Master’s thesis, Hacettepe University; 2018.
  • Niepert M., Ahmed M., Kutzkov K. Learning convolutional neural networks for graphs. In Proceedings of the 33rd international conference on machine learning. 2016. p. 2014-2023
  • Mateen M., Wen J., Nasrullah Song S., Huang Z. Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry. 2018. 11(1), 1-12.
  • Lin Min, Chen Qiang, Yan Shuicheng. Network in network. arXiv preprint arXiv:1312.4400.2013.
  • Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. p.2818–2826.
  • Chollet F. Xception: Deep learning with depthwise separable convolutions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 1251-1258.
  • Patil S., Golellu A. Classification of COVID-19 CT Images using transfer learning models. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). 2021. p. 116-119.
  • Özdemir D., Arslan N. N. Analysis of deep transfer learning methods for early diagnosis of the Covid-19 disease with chest X-ray images. Düzce University Journal of Science & Technology. 2022. 10(2), 628-640.
  • Humayun M., Sujatha R., Almuayqil S. N., Jhanjhi N. Z. A transfer learning approach with a convolutional neural network for the classification of lung carcinoma. In: Healthcare. MDPI, 2022. p. 1058.
  • Garin A., Tauzin G. A topological" reading" lesson: Classification of MNIST using TDA. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). 2019. p. 1551-1556.
  • Grover D., Toghi B. MNIST dataset classification utilizing k-NN classifier with modified sliding-window metric. In Science and Information Conference. 2019. p. 583-591
  • Iyer L. R., Chua Y., Li, H. Is neuromorphic mnist neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain. Frontiers in Neuroscience. 2021. 15, 1-21.
  • Kadam S. S., Adamuthe A. C., Patil A. B. CNN model for image classification on MNIST and fashion-MNIST dataset. Journal of Scientific Research. 2020. 64(2), 374-384.
  • Prabhu V. U. Kannada-MNIST: A new handwritten digits dataset for the Kannada language. arXiv preprintarXiv:1908.01242, 2019.
  • Audibert R. B., Maschio V. M. FINNger--Applying artificial intelligence to ease math learning for children. arXiv preprint arXiv:2105.12281, 2021.
  • Zhang L. Hand-drawn sketch recognition with a double-channel convolutional neural network. EURASIP Journal on Advances in Signal Processing. 2021.1: 73.
  • Hayat S., She K., Yu Y., Mateen M. Deep cnn-based features for hand-drawn sketch recognition via transfer learning approach. Editorial Preface From the Desk of Managing Editor, 2019, 10.9.
  • Ali S., Aslam N., Kim D., Abbas A., Tufail S., Azhar B.Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT. PeerJ Computer Science, 2023, 9: e1186.

Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization

Yıl 2025, Cilt: 14 Sayı: 2, 247 - 258, 27.06.2025

Abdullah Şener , Serdal Poçan , Burhan Ergen

https://doi.org/10.46810/tdfd.1639446

Öz

Integrating technology into mathematics education is crucial for enhancing the understanding of mathematical concepts and skills, as well as increasing motivation. This is particularly applicable in geometry classes, where technology can facilitate the detection of geometric shapes, impacting both the learning and teaching processes. In this context, emerging concepts of artificial intelligence and deep learning can be utilized as tools to overcome such limitations. This study addresses the challenges that teachers face when drawing three-dimensional geometric shapes in digital environments. Shapes drawn manually in digital environments can often be complex, making it difficult for teachers to create accurate and precise drawings. Deep learning models can assist teachers in correcting drawing errors, thereby providing students with clearer and more comprehensible visuals to facilitate the learning of geometric concepts. The study emphasizes the high accuracy rates achieved using various deep learning models, highlighting their impressive capabilities in accurately classifying geometric shapes.

Anahtar Kelimeler

Artificial intelligence deep learning mathematics education three-dimensional shapes

Kaynakça

  • Bray A., Tangney B. Technology usage in mathematics education research–A systematic review of recent trends. Computers & Education. 2017;114;255-273.
  • Chao W. H., Yang C. Y., Chang R. C. A study of the interactive mathematics mobile application development. 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII). 2018. p.248-249
  • Daly I., Bourgaize J., Vernitski A. Mathematical mindsets increase student motivation: Evidence from the EEG. Trends in Neuroscience and Education. 2019;15; 18–28.
  • Baykul Y. İlkokulda matematik öğretimi [Teaching mathematics in primary school].12th ed. Pegem Akademi; 2014.
  • Altun M. Eğitim fakülteleri ve sınıf öğretmenleri için matematik öğretimi [Teaching mathematics for education faculties and primary teachers]. 19th ed. Aktüel Alfa Akademi; 2015.
  • Sevimli E., Kul Ü. Evaluation of the contents of mathematics textbooks in terms of compliance to technology: Case of secondary school. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education. 2015. 9(1), 308-331.
  • Engelbrecht J., Llinares S., Borba M. C. Transformation of the mathematics classroom with the internet. ZDM Mathematics Education. 2020. 52(5), 825-841.
  • Azizul S. M. J., Din R. Teaching and learning geometry using GeoGebra software via MOOC. Journal of Personalized Learning. 2016. 2(1), 39-50.
  • Chen B., Bao L., Zhang R., Zhang J., Liu F., Wang S., Li M. A Multi-Strategy Computer-Assisted EFL Writing Learning System With Deep Learning Incorporated and Its Effects on Learning: A Writing Feedback Perspective. Journal of Educational Computing Research. 2023. 61(8), 60-102.
  • Park W., Kwon H. Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education. 2023. p.1-27.
  • Kearney M., Maher D. Mobile learning in pre-service teacher education: Examining the use of professional learning networks. Australasian Journal of Educational Technology. 2019. 35(1), 135-148.
  • Aksu H. H. Mathematics teachers' opinions on distance education using the Educational Informatics Network (EBA). Turkish Online Journal of Educational Technology. 2021. 20(2), 88-97.
  • Chassignol M., Khoroshavin A., Klimova A., Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science. 2018. 136, 16-24.
  • Shumway J. F., Welch L. E., Kozlowski J. S., Clarke-Midura J., Lee V. R. Kindergarten students’ mathematics knowledge at work: the mathematics forprogramming robot toys. Mathematical Thinking and Learning. 2021. p.1-29.
  • Iskrenovic-Momcilovic O. Improving geometry teaching with scratch. International Electronic Journal of Mathematics Education. 2020. 15(2), 5-8.
  • Roll I., Wylie R. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education. 2016. 26(2), 582-599.
  • Bussi M. B., Mariotti M. A. Semiotic mediation in the mathematics classroom: Artifacts and signs after a Vygotskian perspective. Handbook of international research in mathematics education. 2nd ed., 2008. pp. 746–805.
  • Bolliger D. U., Wasilik O. ( Factors influencing faculty satisfaction with online teaching and learning in higher education. Distance Education. 2009. 30(1), 103-116.
  • Jumadi A., Nasrudin F. S. M., Arunah N. S. K., Mohammad S. A., Abd Ghafar N., Zainuddin N. A. Students’ and lecturers’ perceptions of students’ difficulties in geometry courses. International Journal of Advanced Research in Education and Society. 2022. 4(2), 50-64.
  • Neubauer A. C. The future of intelligence research in the coming age of artificial intelligence–With a special consideration of the philosophical movements of trans-and posthumanism. Intelligence. 2021. 87, 101563.
  • Goralski M. A., Tan T. K. Artificial intelligence and sustainable development. The International Journal of Management Education. 2020. 18(1), 100330.
  • Zhang C., Lu Y. Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration. 2021.23, 100224.
  • Wang P., Liu K., Dougherty Q. Conceptions of artificial intelligence and singularity. Information. 2018. 9.4: 79.
  • Rapaport W. J. What is artificial intelligence? Journal of Artificial General Intelligence. 2020. 11(2), 52–56.
  • Bundy A. Preparing for the future of Artificial Intelligence. AI & Soc. 2017. 32, 285–287.
  • Mohamad Nasri N., Husnin H., Mahmud S. N. D., Halim L. Mitigating the COVID-19 pandemic: a snapshot from Malaysia into the coping strategies for pre-service teachers’ education. Journal of Education for Teaching. 2020. 46(4), 546-553.
  • Van Vaerenbergh, Steven; Pérez-Suay, Adrián. A classification of artificial intelligence systems for mathematics education. In: Mathematics education in the age of artificial intelligence: How artificial intelligence can serve mathematical human learning. Cham: Springer International Publishing. 2022. p. 89-106.
  • Sulistiowati D. L., Herman T., Jupri A. Student difficulties in solving geometry problem based on Van Hiele thinking level. In: Journal of Physics: Conference Series. IOP Publishing. 2019. p. 042118.
  • Wong W. K., Yin S. K., Yang H. H., Cheng Y. H. Using computer-assisted multiple representations in learning geometry proofs. Journal of Educational Technology & Society. 2011. 14(3), 43-54.
  • Fujita T., Kondo Y., Kumakura H., Kunimune S., Jones K. Spatial reasoning skills about 2D representations of 3D geometrical shapes in grades 4 to 9. Mathematics Education Research Journal. 2020. 32(2), 235-255.
  • Cohen C. A., Hegarty M. Sources of difficulty in imagining cross sections of 3D objects. In Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 29. No. 29. 2007.
  • Parmanov A. A., Mamatov J. A., Kahhorov M. J., Fayzullaev S. U. Main tasks of teaching geometry at the secondary school. Eastern European Scientific Journal. 2020. 1(2).57-59.
  • Sarama, J., Clements, D. H.. Clements. Early childhood mathematics education research: Learning trajectories for young children. Routledge, 2009.
  • Wojtowicz A., Wojtowicz B., Kopec K. Descriptive geometry in the time of COVID-19: Preliminary assessment of distance education during pandemic social isolation. Advances in Engineering Education. 2020. 8(4), 1-10.
  • Yurniwati Y., Utomo E. Problem-based learning flipped classroom design for developing higher-order thinking skills during the COVID-19 pandemic in geometry domain. In: Journal of Physics: Conference Series. IOP Publishing. 2020. p. 012057.
  • Carter Jr R. A., Rice M., Yang S., Jackson H. A. Self-regulated learning in online learning environments: strategies for remote learning. Information and Learning Sciences.2020. 121 (5/6), 321-329.
  • Lowrie T., Jorgensen R. Teaching mathematics remotely: Changed practices in distance education. Mathematics Education Research Journal, 24(3).2012. 371-383.
  • Haj-Yahya A. Do prototypical constructions and self-attributes of presented drawings affect the construction and validation of proofs? Mathematics Education Research Journal. 2020. 32(4), 685-718.
  • Murugan A., Tadesse F. W., Wondirad E. N., Bizuneh M. T., Firew A. Machine learning in identification of polygon shapes for recognition of mechanical engineering drawings. International Journal of Mechanical Engineering. 2022. 7(2), 2282-2288.
  • Patkin D., Plaksin O. Procedural and relational understanding of pre-service mathematics teachers regarding spatial perception of angles in pyramids. International Journal of Mathematical Education in Science and Technology. 2019. 50(1), 121-140.
  • Kepceoglu, I. Effect of dynamic geometry software on 3-dimensional geometric shape drawing skills. Journal of Education and Training Studies. 2018. 6(10), 98-106.
  • Šafhalter A., Glodež S., Šorgo A., Ploj Virtič M. Development of spatial thinking abilities in engineering 3D modeling course aimed at lower secondary students. International Journal of Technology and Design Education. 2022. 32, 167-184.
  • Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Shuai B., Liu T., Wang X., Wang G., Cai J., Chen T. Recent advances in convolutional neural networks. Pattern Recognition. 2018. 77, 354-377.
  • Min S., Lee B., Yoon S. Deep learning in bioinformatics. Briefings in Bioinformatics. 2017. 18(5), 851-869.
  • Hanbay K. Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform. Journal of the Faculty of Engineering and Architecture of Gazi University. 2020. 35(1), 443-456.
  • Kurt F. Analysis of the effects of hyperparameters in convolutıonal neural networks. (Publication No. 519157) Master’s thesis, Hacettepe University; 2018.
  • Niepert M., Ahmed M., Kutzkov K. Learning convolutional neural networks for graphs. In Proceedings of the 33rd international conference on machine learning. 2016. p. 2014-2023
  • Mateen M., Wen J., Nasrullah Song S., Huang Z. Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry. 2018. 11(1), 1-12.
  • Lin Min, Chen Qiang, Yan Shuicheng. Network in network. arXiv preprint arXiv:1312.4400.2013.
  • Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. p.2818–2826.
  • Chollet F. Xception: Deep learning with depthwise separable convolutions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 1251-1258.
  • Patil S., Golellu A. Classification of COVID-19 CT Images using transfer learning models. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). 2021. p. 116-119.
  • Özdemir D., Arslan N. N. Analysis of deep transfer learning methods for early diagnosis of the Covid-19 disease with chest X-ray images. Düzce University Journal of Science & Technology. 2022. 10(2), 628-640.
  • Humayun M., Sujatha R., Almuayqil S. N., Jhanjhi N. Z. A transfer learning approach with a convolutional neural network for the classification of lung carcinoma. In: Healthcare. MDPI, 2022. p. 1058.
  • Garin A., Tauzin G. A topological" reading" lesson: Classification of MNIST using TDA. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). 2019. p. 1551-1556.
  • Grover D., Toghi B. MNIST dataset classification utilizing k-NN classifier with modified sliding-window metric. In Science and Information Conference. 2019. p. 583-591
  • Iyer L. R., Chua Y., Li, H. Is neuromorphic mnist neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain. Frontiers in Neuroscience. 2021. 15, 1-21.
  • Kadam S. S., Adamuthe A. C., Patil A. B. CNN model for image classification on MNIST and fashion-MNIST dataset. Journal of Scientific Research. 2020. 64(2), 374-384.
  • Prabhu V. U. Kannada-MNIST: A new handwritten digits dataset for the Kannada language. arXiv preprintarXiv:1908.01242, 2019.
  • Audibert R. B., Maschio V. M. FINNger--Applying artificial intelligence to ease math learning for children. arXiv preprint arXiv:2105.12281, 2021.
  • Zhang L. Hand-drawn sketch recognition with a double-channel convolutional neural network. EURASIP Journal on Advances in Signal Processing. 2021.1: 73.
  • Hayat S., She K., Yu Y., Mateen M. Deep cnn-based features for hand-drawn sketch recognition via transfer learning approach. Editorial Preface From the Desk of Managing Editor, 2019, 10.9.
  • Ali S., Aslam N., Kim D., Abbas A., Tufail S., Azhar B.Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT. PeerJ Computer Science, 2023, 9: e1186.
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları
Bölüm Makaleler
Yazarlar

Abdullah Şener 0000-0002-8927-5638

Serdal Poçan 0000-0001-6901-0889

Burhan Ergen 0000-0003-3244-2615

Yayımlanma Tarihi 27 Haziran 2025
Gönderilme Tarihi 13 Şubat 2025
Kabul Tarihi 4 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 2

Kaynak Göster

APA Şener, A., Poçan, S., & Ergen, B. (2025). Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. Türk Doğa Ve Fen Dergisi, 14(2), 247-258. https://doi.org/10.46810/tdfd.1639446
AMA Şener A, Poçan S, Ergen B. Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. TDFD. Haziran 2025;14(2):247-258. doi:10.46810/tdfd.1639446
Chicago Şener, Abdullah, Serdal Poçan, ve Burhan Ergen. “Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization”. Türk Doğa Ve Fen Dergisi 14, sy. 2 (Haziran 2025): 247-58. https://doi.org/10.46810/tdfd.1639446.
EndNote Şener A, Poçan S, Ergen B (01 Haziran 2025) Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. Türk Doğa ve Fen Dergisi 14 2 247–258.
IEEE A. Şener, S. Poçan, ve B. Ergen, “Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization”, TDFD, c. 14, sy. 2, ss. 247–258, 2025, doi: 10.46810/tdfd.1639446.
ISNAD Şener, Abdullah vd. “Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization”. Türk Doğa ve Fen Dergisi 14/2 (Haziran 2025), 247-258. https://doi.org/10.46810/tdfd.1639446.
JAMA Şener A, Poçan S, Ergen B. Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. TDFD. 2025;14:247–258.
MLA Şener, Abdullah vd. “Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization”. Türk Doğa Ve Fen Dergisi, c. 14, sy. 2, 2025, ss. 247-58, doi:10.46810/tdfd.1639446.
Vancouver Şener A, Poçan S, Ergen B. Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. TDFD. 2025;14(2):247-58.
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