PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Almisreb, A. A., Tahir, N. M., Turaev, S., Saleh, M. A., & Al Junid, S. A. M. (2022). Arabic handwriting classification using deep transfer learning techniques. Pertanika Journal of Science and Technology, 30(1), 641–654. https://doi.org/10.47836/PJST.30.1.35

• Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, Article 53. https://doi.org/10.1186/s40537-021-00444-8

• Asselborn, T., Chapatte, M., & Dillenbourg, P. (2020). Extending the spectrum of dysgraphia: A data driven strategy to estimate handwriting quality. Scientific Reports, 10(1), Article 3140. https://doi.org/10.1038/s41598-020-60011-8

• Biotteau, M., Danna, J., Baudou, É., Puyjarinet, F., Velay, J. L., Albaret, J. M., & Chaix, Y. (2019). Developmental coordination disorder and dysgraphia: Signs and symptoms, diagnosis, and rehabilitation. Neuropsychiatric Disease and Treatment, 15, 1873–1885. https://doi.org/10.2147/NDT.S120514

• Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, Article 100134. https://doi.org/10.1016/j.mlwa.2021.100134

• Chung, P. J., Patel, D. R., & Nizami, I. (2020). Disorder of written expression and dysgraphia: Definition, diagnosis, and management. Translational Pediatrics, 9(Suppl 1), S46–S54. https://doi.org/10.21037/TP.2019.11.01

• Dankovicova, Z., Hurtuk, J., & Fecilak, P. (2019, September 12-14). Evaluation of digitalized handwriting for dysgraphia detection using random forest classification method. [Paper presentation]. IEEE 17th International Symposium on Intelligent Systems and Informatics, Proceedings (SISY), Subotica, Serbia. https://doi.org/10.1109/SISY47553.2019.9111567

• Deuel, R. K. (1995). Developmental dysgraphia and motor skills disorders. Journal of Child Neurology, 10(1_suppl), S6-S8. https://doi.org/10.1177/08830738950100S103

• Devi, A., & Kavya, G. (2023). Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 120, Article 110647. https://doi.org/10.1016/j.pnpbp.2022.110647

• Devillaine, L., Lambert, R., Boutet, J., Aloui, S., Brault, V., Jolly, C., & Labyt, E. (2021). Analysis of graphomotor tests with machine learning algorithms for an early and universal pre-diagnosis of dysgraphia. Sensors, 21(21), Article 7026. https://doi.org/10.3390/s21217026

• Dimauro, G., Bevilacqua, V., Colizzi, L., & Di Pierro, D. (2020). TestGraphia, a software system for the early diagnosis of dysgraphia. IEEE Access, 8, 19564–19575. https://doi.org/10.1109/ACCESS.2020.2968367

• Ghouse, F., Paranjothi, K., & Vaithiyanathan, R. (2022). Dysgraphia classification based on the non-discrimination regularization in rotational region convolutional neural network. International Journal of Intelligent Engineering and Systems, 15(1), 55–63. https://doi.org/10.22266/IJIES2022.0228.06

• Kunhoth, J., Maadeed, S. A., Saleh, M., & Akbari, Y. (2023). Biomedical signal processing and control exploration and analysis of on-surface and in-air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods. Biomedical Signal Processing and Control, 83, Article 104715. https://doi.org/10.1016/j.bspc.2023.104715

• Masood, F., Khan, W. U., Ullah, K., Khan, A., Alghamedy, F. H., & Aljuaid, H. (2023). A hybrid CNN-LSTM random forest model for dysgraphia classification from hand-written characters with uniform/normal distribution. Applied Sciences, 13(7), Article 4275. https://doi.org/10.3390/app13074275

• Mohammed, A. B., Al-Mafrji, A. A. M., Yassen, M. S., & Sabry, A. H. (2022). Developing plastic recycling classifier by deep learning and directed acyclic graph residual network. Eastern-European Journal of Enterprise Technologies, 2(10), 42–49. https://doi.org/10.15587/1729-4061.2022.254285

• Qiao, J., Lv, Y., Cao, C., Wang, Z., & Li, A. (2018). Multivariate deep learning classification of alzheimer’s disease based on hierarchical partner matching independent component analysis. Frontiers in Aging Neuroscience, 10, Article 417. https://doi.org/10.3389/fnagi.2018.00417

• Ramlan, S. A., Isa, I. S., Osman, M. K., Ismail, A. P., & Soh, Z. H. C. (2022). Investigating the impact of CNN layers on dysgraphia handwriting image classification performance. Journal of Electrical and Electronic Systems Research, 21, 73–83. https://doi.org/https://doi.org/10.24191/jeesr.v21i1.010

• Rosli, M. S. A., Isa, I. S., Ramlan, S. A., Sulaiman, S. N., & Maruzuki, M. I. F. (2021, August 27-28). Development of CNN transfer learning for dyslexia handwriting recognition. [Paper presentation]. 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia. https://doi.org/10.1109/iccsce52189.2021.9530971

• Šafárová, K., Mekyska, J., & Zvončák, V. (2021). Developmental dysgraphia: A new approach to diagnosis. The International Journal of Assessment and Evaluation, 28(1), 143–160. https://doi.org/10.18848/2327-7920/CGP/v28i01/143-160

• Sihwi, S. W., Fikri, K., & Aziz, A. (2019). Dysgraphia identification from handwriting with support vector machine method. Journal of Physics: Conference Series, 1201(1), Article 012050. https://doi.org/10.1088/1742-6596/1201/1/012050

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• Vilasini, V., Rekha, B. B., Sandeep, V., & Venkatesh, V. C. (2022, August 11-12). Deep learning techniques to detect learning disabilities among children using handwriting. [Paper presentation]. Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India. https://doi.org/10.1109/ICICICT54557.2022.9917890

• Vaivre-Douret, L., Lopez, C., Dutruel, A., & Vaivre, S. (2021). Phenotyping features in the genesis of pre-scriptural gestures in children to assess handwriting developmental levels. Scientific Reports, 11(1), Article 731. https://doi.org/10.1038/s41598-020-79315-w

• Vlachos, F., & Avramidis, E. (2020). The difference between developmental dyslexia and dysgraphia: Recent neurobiological evidence. International Journal of Neuroscience and Behavioral Science, 8(1), 1–5. https://doi.org/10.13189/ijnbs.2020.080101

• Zolna, K., Asselborn, T., Jolly, C., Casteran, L., Johal, W., & Dillenbourg, P. (2019). The dynamics of handwriting improves the automated diagnosis of dysgraphia. arXiv:1906.07576, Article 1906.07576. https://doi.org/10.48550/arXiv.1906.07576