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Recognizing Daily Activities of Children with Autism Spectrum Disorder Using Convolutional Neural Network Based on Image Enhancement Cover

Recognizing Daily Activities of Children with Autism Spectrum Disorder Using Convolutional Neural Network Based on Image Enhancement

Open Access
|Mar 2025

References

  1. Kamp-Becker, I. Autism Spectrum Disorder in ICD-11 – A Critical Reflection of its Possible Impact on Clinical Practice and Research. – Molecular Psychiatry, Vol. <bold>29</bold>, 2024, pp. 633-6387.
  2. Sulkes, B., S. Definition of Developmental Disorders. <bold><ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.msdmanuals.com/home/children-s-health-issues/learning-and-developmental-disorders/definition-of-developmental-disorders">https://www.msdmanuals.com/home/children-s-health-issues/learning-and-developmental-disorders/definition-of-developmental-disorders</ext-link></bold>
  3. Daulay, N. Parenting Stress of Mothers in Children with Autism Spectrum Disorder: A Review of the Culture in Indonesia. – In: Proc. of International Conference on Southeast Asia Studies, 2018.
  4. Henry, M., E. Living Life Like It’s Golden with Disability: Case Studies of Independent Living. 2018.
  5. Mohd Kamil, N. K., A. S. Amin, N. Md Akhir, A. R. Ahmad Badayai, I. Mohd Zambri, R. Sutan, K. F. Khairuddin, W. A. Wan Abdullah. Independent Living Skills Needed by Students with Special Educational Needs (SEN) Towards Inclusive Education: A Systematic Literature Review. – Specialists Ugdym, Vol. <bold>1</bold>, 2023, No 44, pp. 610-623.
  6. Volkmar, F. R. Encyclopaedia of Autism Spectrum Disorders. Springer, Switzerland, 2021.
  7. Pramardika, D., D. Susanti, E. Fitriana. Analisis Pola Makan Anak Autis Yayasan Tongkat Musa Indonesia ABK Bangun Rejo Kabupaten Kutai Kartanegara Tahun 2019. – Bunda Edu-Midwifery Journal, Vol. <bold>2</bold>, 2019, No 1, pp. 18-24.
  8. Kurniati, L. Modul Guru Pembelajar SLB Autis. PPPPTK TK DAN PLB, Bandung, 2016.
  9. Beddiar, D. R., B. Nini, M. Sabokrou, A. Hadid. Vision-Based Human Activity Recognition: A Survey. – Multimed Tools Appl Journal, Vol. <bold>79</bold>, 2020, pp. 30509-30555.
  10. Su, X., H. Tong, P. Ji. Activity Recognition with Smartphone Sensors. – Tsinghua Science and Technology, Vol. <bold>19</bold>, 2014, No 3, pp. 235-249.
  11. Jain, A., V. Kanhangad. Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors. – IEEE Sensors Journal, Vol. <bold>18</bold>, 2018, No 3, pp. 1169-1177.
  12. Yao, G., T. Lei, J. Zhong. A Review of Convolutional-Neural-Network-Based Action Recognition. – Pattern Recognition Letters, Vol. <bold>118</bold>, 2019, pp. 14-22.
  13. Dhillon, A., G. K. Verma. Convolutional Neural Network: A Review of Models, Methodologies and Applications to Object Detection. – Progress in Artificial Intelligence, Vol. <bold>9</bold>, 2020, No 2, pp. 85-112.
  14. Alzubaidi, L., J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria, M. A. Fadhel, M. Al‐Amidie, L. Farhan. Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions. – Journal of Big Data, Vol. <bold>8</bold>, 2021, pp. 1-74.
  15. Bhatt, D., C. Patel, H. Talsania, J. Patel, R. Vaghela, S. Pandya, K. Modi, H. Ghayvat. CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. – Electronics, Vol. <bold>10</bold>, 2021, No 2470, pp. 1-28.
  16. Haweel, R., A. Shalaby, A. Mahmoud, N. Seada, S. Ghoneims, M. Ghazal, M. F. Casanova, G. N. Barnes, A. El-Baz. A Robust DWT – CNN-Based CAD System for Early Diagnosis of Autism Using Task-Based fMRI. – Medical Physics, Vol. <bold>48</bold>, 2020, No 5, pp. 2315-2326.
  17. Sherkatghanad, Z., M. Akhondzadeh, S. Salari, M. Zomorodi-Moghadam, M. Abdar, U. R. Acharya, R. Khosrowabadi, V. Salari. Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network. – Front Neurosci, Vol. <bold>13</bold>, 2020, pp. 1-17.
  18. Mitschke, N., Y. Ji, M. Heizmann. Task Specific Image Enhancement for Improving the Accuracy of CNNs. – In: Proc. of 10th International Conference on Pattern Recognition Applications and Methods, 2021, pp. 174-181.
  19. Ferdinand, V., A. Henry, G. E. Nawir, V. Anderies., A. Gunawan. Effect of Image Enhancement in CNN-Based Medical Image Classification: A Systematic Literature Review. – In: Proc. of 5th International Conference on Information and Communications Technology, 2022, pp. 87-92.
  20. Gonzalez, R. C., R. E. Woods. Digital Image Processing. New York, Pearson, 2018.
  21. Werdiningsih, I., I. Puspitasari, R. Hendradi. Analysis and Techniques of Enhancing the Video Quality of Children with Autism Spectrum Disorder’s Daily Activities – In: Proc. of 24th International Seminar on Intelligent Technology and Its Applications (ISITIA’24), 2024, pp. 621-626.
  22. Mustaghfirin, F., H. Erwin, K. Putra, U. Yanti, R. Ricadonna. The Comparison of Iris Detection Using Histogram Equalization and Adaptive Histogram Equalization Methods. – In: Proc. of International Conference on Information System Computer Science and Engineering, 2019.
  23. Qi, Y., Z. Yang, W. Sun, M. Lou, J. Lian, W. Zhao, X. Deng, Y. Ma. A Comprehensive Overview of Image Enhancement Techniques. – Archives of Computational Methods in Engineering, Vol. <bold>29</bold>, 2022, pp. 583-607.
  24. Lu, P., B. Song, L. Xu. Human Face Recognition Based on Convolutional Neural Network and Augmented Dataset. – Systems Science &amp; Control Engineering, Vol. <bold>9</bold>, 2021, No 2, pp. 29-37.
  25. Rao Killi, C. B., N. Balakrishnan, C. S. Rao. Deep Fake Image Classification Using VGG-19 Model. – International Information and Engineering Technology Association, Vol. <bold>28</bold>, 2023, No 2, pp. 509-515.
  26. Rusia, M. K., D. K. Singh. A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks. – Cybernetics and Information Technologies, Vol. <bold>22</bold>, 2022, No 3, pp. 127-145.
  27. Habiban, M., F. R. Hamade, N. A. Mohsin. Hybrid Edge Detection Methods in Image Steganography for High Embedding Capacity. – Cybernetics and Information Technologies, Vol. <bold>24</bold>, 2024, No 1, pp. 157-170.
  28. Sara, U., M. Akter, M. S. Uddin. Image Quality Assessment through FSIM, SSIM, MSE and PSNR – A Comparative Study. – Journal of Computer and Communications, Vol. <bold>7</bold>, 2019, No 3, pp. 8-18.
  29. Erwin, D. R. Ningsih, Improving Retinal Image Quality Using the Contrast Stretching, Histogram Equalization, and CLAHE Methods with Median Filter. – International Journal of Image, Graphics and Signal Processing, Vol. <bold>12</bold>, 2020, No 2, pp. 30-41.
  30. Huan, B., K. Zhang, R. Sanchez-Romero, J. Ramsey, M. Glymoury, C. Glymoury. Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. – Imaging and Signal Analysis Journal, Vol. <bold>1</bold>, 2019, pp. 237-267.
DOI: https://doi.org/10.2478/cait-2025-0005 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 78 - 96
Submitted on: Sep 11, 2024
Accepted on: Dec 20, 2024
Published on: Mar 21, 2025
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 Indah Werdiningsih, Ira Puspitasari, Rimuljo Hendradi, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.