Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02061
Number of page(s) 12
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802061
Published online 17 November 2023
  1. S. Ray, Role of shrimp farming in socio-economic elevation and professional satisfaction in coastal communities of Southern Bangladesh, Aquac. Reports, 20, June 2020, 100708 (2021) [CrossRef] [Google Scholar]
  2. A. Priadana, A. W. Murdiyanto, and A. W. Murdiyanto, Shrimps clusterization by size using digital image processing with CCA and DBSCAN, 8, April, 106–112 (2020) [Google Scholar]
  3. N. T. N. Anh, F. A. Shayo, N. Nevejan, and N. Van Hoa, Effects of stocking densities and feeding rates on water quality, feed efficiency, and performance of white leg shrimp Litopenaeus vannamei in an integrated system with sea grape Caulerpa lentillifera, J. Appl. Phycol., 33, 5, 3331–3345 (2021) [CrossRef] [Google Scholar]
  4. Chaikaew, N. Rugkarn, V. Pongpipatwattana, and V. Kanokkantapong, Enhancing ecological-economic efficiency of intensive shrimp farm through in-out nutrient budget and feed conversion ratio, Sustain. Environ. Res., 1, 1, 1–11 (2019) [Google Scholar]
  5. L. Yang, A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture, Comput. Electron. Agric., 187, March, 106316 (2021) [CrossRef] [Google Scholar]
  6. F. Chen, J. Xu, Y. Wei, and J. Sun, Establishing an eyeball-weight relationship for Litopenaeus vannamei using machine vision technology, Aquac. Eng., 87, June, 102014 (2019) [CrossRef] [Google Scholar]
  7. N. Ubina, S. C. Cheng, C. C. Chang, and H. Y. Chen, Evaluating fish feeding intensity in aquaculture with convolutional neural networks, Aquac. Eng., 94, (Aug, 2021) [Google Scholar]
  8. H. Liu, A high-density fish school segmentation framework for biomass statistics in a deep-sea cage, Ecol. Inform., 64, July, 101367 (2021) [CrossRef] [Google Scholar]
  9. J. V. C. I. R, Collaborative Distribution Alignment for 2D image-based 3D shape, J. Vis. Commun. Image Represent., 83, September 2021, 103426 (2022) [CrossRef] [Google Scholar]
  10. T. T. Khaing, S. I. N. Nyein, M. S. O. E. Khaing, and K. K. Wai, Dimension Reduction of Images Using Principal Component Analysis Algorithm, 3, 11, 39–42 (2020) [Google Scholar]
  11. A. K. Jain, Fundamentals of digital image processing. Anil K. Jain., Vision, November. 569 (1989) [Google Scholar]
  12. R. A. Osornio-rios, A. Y. Jaen-cuellar, A. I. Alvarado-hernandez, I. Zamudio-ramirez, I. A. Cruz-albarran, and J. A. Antonino-daviu, Fault detection and classification in kinematic chains by means of PCA extraction-reduction of features from thermographic images, Measurement, 197, May, 111340 (2022) [CrossRef] [Google Scholar]
  13. J. V. C. I. R, J. Ma, and Y. Yuan, Dimension reduction of image deep feature using PCA q, 63 (2019) [Google Scholar]
  14. L. Chuen and A. Aziz, On overview of PCA application strategy in processing high dimensionality forensic data, Microchem. J., 169, July, 106608 (2021) [CrossRef] [Google Scholar]
  15. S. Padoan, Organic molecular markers and source contributions in a polluted municipality of north-east Italy : Extended PCA-PMF statistical approach, Environ. Res., 186, March, 109587 (2020) [CrossRef] [Google Scholar]
  16. T. H. Itkonen and E. Lehtonen, Transportation Research Part F Characterisation of motorway driving style using naturalistic driving data, TransRes. Part F Psychol. Behav., 69, January, 72–79 (2020) [CrossRef] [Google Scholar]
  17. T. Particles, B. Ohtani, S. Chandren, and O. J. Gurney-champion, Image Compression and Reconstruction Based on Image Compression and Reconstruction Based on PCA (2021) [Google Scholar]
  18. M. Z. Nasution, S. Komputer, F. Sains, and U. Pancabudi, JITE ( Journal of Informatics and Telecommunication Engineering ) Face Recognition based Feature Extraction using Principal Component Analysis ( PCA ), 3, 2, 182–191 (2020) [Google Scholar]
  19. M. Mustaqeem, Principal component based support vector machine ( PC-SVM ): a hybrid technique for software defect detection, Cluster Comput., 24, 3, 2581–2595 (2021) [CrossRef] [PubMed] [Google Scholar]
  20. K. Islam, S. Ali, S. Miah, and M. Rahman, Machine Learning with Applications Brain tumor detection in MR image using superpixels , principal component analysis and template based K-means clustering algorithm, Mach. Learn. with Appl., 5, May, 100044 (2021) [CrossRef] [Google Scholar]
  21. E. Peretti et al., NeuroImage : Clinical Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis, 30, January, 1–10 (2021) [Google Scholar]
  22. J. Cao, R. Wang, Y. Jia, X. Zhang, S. Wang, and S. Kwong, No-reference image quality assessment for contrast-changed images via a semi-supervised robust PCA model, Inf. Sci. (Ny)., 574, 640–652 (2021) [CrossRef] [Google Scholar]
  23. M. Wang et al., Original papers A PCA-based frame selection method for applying CNN and LSTM to classify postural behaviour in sows, Comput. Electron. Agric., 189, July, 106351 (2021) [CrossRef] [Google Scholar]
  24. X. Shen, H. Hu, X. Li, and S. Li, Study on PCA-SAFT imaging using leaky Rayleigh waves, Measurement, 170, November 2020, 108708 (2021) [CrossRef] [Google Scholar]
  25. N. Iofrida et al., International Journal of Gastronomy and Food Science Italians ’ behavior when dining out : Main drivers for restaurant selection and customers segmentation, 28, March (2022) [Google Scholar]
  26. S. Huancahuire-vega et al., EducaciónMédica, Educ. Médica, 22, 3, 144–148 (2021) [Google Scholar]
  27. Kulshrestha, Performing the KMO and Bartlett ’ s Test for Factors Estimating the Warehouse Efficiency, Inventory and Customer Contentment for E-retail Supply Chain, 09 (2019) [Google Scholar]
  28. N. Shrestha, Factor Analysis as a Tool for Survey Analysis, 9, 1, 4–11 (2021) [Google Scholar]
  29. D. A. L. R. Ubinfeld, Data Standardization, April (2020) [Google Scholar]
  30. H. Jian, Q. Lin, J. Wu, X. Fan, and X. Wang, Design of the color classification system for sunglass lenses using, Measurement, 189, November 2021, 110498 (2022) [CrossRef] [Google Scholar]
  31. D. Kumar Sharma, D. Sreenivasa Chakravarthi, A. Ara Shaikh, A. Al Ayub Ahmed, S. Jaiswal, and M. Naved, The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique, Mater. Today Proc., xxxx (2021) [Google Scholar]
  32. R. Basin, K. Dhali, M. Chakraborty, and M. Sahana, The Egyptian Journal of Remote Sensing and Space Sciences Assessing spatio-temporal growth of urban sub-centre using Shannon ’ s entropy model and principle component analysis : A case from North 24, Egypt. J. Remote Sens. SSci., 22, 1, 25–35 (2019) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.