ARCHIVES

1. S. C. Radopoulou and I. K. Brilakis, ‘‘Automated detection of multiple pavement defects,’’ J. Computer. Civil Eng., vol. 31, no. 2, Mar.
2017, Art. no. 04016057, doi: 10.1061/(ASCE)CP.1943-5487.0000623.
2. J. Jeong, H. Jo, and G. Ditzler, ‘‘Convolutional neural networks for pavement roughness assessment using calibration-free vehicle
dynamics,’’ Comput.-Aided Civil Infrastruct. Eng., vol. 35, no. 11, pp. 1209–1229, Mar. 2020, doi: 10.1111/mice.12546.
3. H. Y. Ju, W. Li, S. Tighe, Z. C. Xu, and J. Z. Zhai, ‘‘CrackU-Net: A novel deep convolutional neural network for pixelwise pavement
crack detection,’’ Struct. Control Health Monitor., vol. 27, no. 8, Mar. 2020, Art. no. e2551, doi: 10.1002/stc.2551.
4. Ravindra Changala, "Sentiment Analysis in Social Media Using Deep Learning Techniques", International Journal of Intelligent
Systems and Applications In Engineering, 2024, 12(3), 1588–1597.
5. Ravindra Changala, “Integration of IoT and DNN Model to Support the Precision Crop”, International Journal of Intelligent Systems
and Applications in Engineering, Volume 12, Issue 16s), February 2024.
6. E. H. Miller, ‘‘Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and
integrated visualization,’’ J. Comput. Civil Eng., vol. 34, no. 3, May 2020, Art. no. 04020010, doi: 10.1061/(ASCE)CP.1943-
5487.0000890.
7. L. Zhang, F. Yang, Y. Daniel Zhang, and Y. J. Zhu, ‘‘Road crack detection using deep convolutional neural network,’’ in Proc. IEEE
Int. Conf. Image Process. (ICIP), Sep. 2016, pp. 3708–3712, doi: 10.1109/ICIP.2016.7533052.
8. Ravindra Changala, “UI/UX Design for Online Learning approach by Predictive Student Experience”, 7th International Conference
on Electronics, Communication and Aerospace Technology (ICECA 2023), DVD Part Number: CFP23J88-DVD; ISBN: 979-8-3503-
4059-4.
9. Ravindra Changala, “Optimization of Irrigation and Herbicides Using Artificial Intelligence in Agriculture”, International Journal
of Intelligent Systems and Applications in Engineering, Volume 11, Issue 3), July 2023.
10. Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, ‘‘Automatic road crack detection using random structured forests,’’ IEEE Trans. Intell.
Transp. Syst., vol. 17, no. 12, pp. 3434–3445, Dec. 2016, doi: 10.1109/TITS.2016.2552248.
11. G. Yao, F.-J. Wei, J.-Y. Qian, and Z.-G. Wu, ‘‘Crack detection of concrete surface based on
12. convolutional neural networks,’’ in Proc. Int. Conf. Mach. Learn. Cybern. (ICMLC), Jul. 2018, pp. 246–250, doi:
10.1109/ICMLC.2018.8527035.
13. Ravindra Changala ,”Development of Predictive Model for Medical Domains to Predict Chronic Diseases (Diabetes) Using Machine
Learning Algorithms And Classification Techniques”, ARPN Journal of Engineering and Applied Sciences, Volume 14, Issue 6, 2019.
14. Ravindra Changala, “Evaluation and Analysis of Discovered Patterns Using Pattern Classification Methods in Text Mining” in ARPN
Journal of Engineering and Applied Sciences, Volume 13, Issue 11, Pages 3706-3717 with ISSN:1819-6608 in June 2018.
15. Z. Liu, Y. Cao, Y. Wang, and W. Wang, ‘‘Computer visionbased concrete crack detection using U-net fully convolutional networks,’’
Autom. Construct., vol. 104, pp. 129–139, Aug. 2019, doi: 10.1016/j.autcon.2019.04.005.
16. Ravindra Changala “A Survey on Development of Pattern Evolving Model for Discovery of Patterns in Text Mining Using Data
Mining Techniques” in Journal of Theoretical and Applied Information Technology, August 2017. Vol.95. No.16, ISSN: 1817-3195,
pp.3974-3987.
17. S. Dorafshan, R. J. Thomas, and M. Maguire, ‘‘Comparison of deep convolutional neural networks and edge detectors for imagebased crack detection in concrete,’’ Construct. Building Mater., vol. 186, pp. 1031–1045, Oct. 2018, doi:
10.1016/j.conbuildmat.2018.08.011.
18. H. Li, J. Zong, J. Nie, Z. Wu, and H. Han, ‘‘Pavement crack detection algorithm based on densely connected and deeply supervised
network,’’ IEEE Access, vol. 9, pp. 11835–11842, 2021, doi: 10.1109/ACCESS.2021.3050401.
19. H. Lin, B. Li, X. Wang, Y. Shu, and S. Niu, ‘‘Automated defect inspection of LED chip using deep convolutional neural network,’’ J.
Intell. Manuf., vol. 30, no. 6, pp. 2525–2534, Aug. 2019, doi: 10.1007/s10845-018-1415-x.
20. X. Wu, J. Ma, Y. Sun, C. Zhao, and A. Basu, ‘‘Multi-scale deep pixel distribution learning for concrete crack detection,’’ in Proc. 25th
Int. Conf. Pattern Recognit. (ICPR), Jan. 2021, pp. 397–400, doi: 10.1109/ICPR48806.2021.9413312.
21. O. Ronneberger, P. Fischer, and T. Brox, ‘‘U-Net: Convolutional networks for biomedical image segmentation,’’ in Proc. Int. Conf.
Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), vol. 9351. Cham, Switzerland: Springer, Nov. 2015, pp. 234–241.
22. Ravindra Changala, Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services,
International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Volume 11, Issue 11s,
August 2023.
23. Ravindra Changala, Block Chain and Machine Learning Models to Evaluate Faults in the Smart Manufacturing System, International
Journal of Scientific Research in Science and Technology, Volume 10, Issue 5, ISSN: 2395-6011, Page Number 247-255, SeptemberOctober-2023.
24. Z. W. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. M. Liang, ‘‘UNet++: A nested U-Net architecture for medical image
segmentation,’’ in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, vol. 11045.
Cham, Switzerland: Springer, Sep. 2018, pp. 3–11.
25. J. Cheng, W. Xiong, W. Chen, Y. Gu, and Y. Li, ‘‘Pixel-level crack detection using U-Net,’’ in Proc. TENCON IEEE Region 10 Conf.,
Oct. 2018, pp. 462–466.