Open Access
Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
---|---|---|
Article Number | 00103 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/bioconf/20249700103 | |
Published online | 05 April 2024 |
- B.G. Mkinson. “Results and implications of a study of fifteen years of satellite image classification experi-ments.” IEEE Transactions on Geoscience and Remote Sensing (Vol. 43, No. 3). 2015. [Google Scholar]
- Sunitha Abburu, Suresh Babu Golla. “Satellite Image Classification Methods and Techniques: A Review”. In-ernational Journal of Computer Applications, (Vol. 119, No. 8). 2015. [Google Scholar]
- Sayali Jog, Mrudul Dixit. “Supervised classification of satellite images”. Conference on Advances in Signal Processing (CASP), 2017. [Google Scholar]
- George, F. Hepner. “Artificial neural network classifi-cation using a minimal training set. Comparison toconventional supervised classification”. Photogrammet-ric Engineering and Remote Sensing, (Vol. 56, No. 4). 2019. [Google Scholar]
- Celik Turgay. “Unsupervised Change Detection in Satel-lite Images Using Principal Component Analysis and k-Means Clustering”. IEEE Geoscience and Remote Sens-ing Letters, (Vol. 6, No. 4). 2019. [Google Scholar]
- Arc, G.I.S. “What Is Image Classification?” ArcGIS 10.5 Help Site, 2017. [Google Scholar]
- A. McCallum. “Multi-label text classification with a mixture model trained by EM. AAAI99”, Workshop on Text Learning. 1999. [Google Scholar]
- Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu. “CNN RNN: A Unified Frame-work for Multi-Label Image Classification”. The, I.E.EE Conference on Computer Vision and Pattern Recogniion (CVPR), 2016, pp. 2285–2294. [Google Scholar]
- Andrej Karpathy. Transfer Learning, 2017. CS231n: Transfer Learning. [Google Scholar]
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna. “Rethinking the Inception Architecture for Computer Vision”. Computer Vision and Pattern Recognition. 2015. [Google Scholar]
- Scott Wallace. Amazon Rainforest, Deforestation, Forest Conservation. National Geographic. Farming the Amazon [Google Scholar]
- Robinson Meyer. Terra Bella and Planet Labs’ Most Consequential Year Yet. The Atlantic, 2016. Terra Bella and Planet Labs’ Most Consequential Year Yet Planet: Understanding the Amazon from Space. Kaggle. Challenge link [Google Scholar]
- Meenakshi, K., Safa, M., Krthick, T., Sivaranjani, N. “A novel study of machine learning algorithms for classifying health care data”, Research Journal of Pharmacy and Technolgy, Research Journal of Phamacy and Technology, 2017. [Google Scholar]
- Agarwal, N. Meenakshi, K., Maragatham, G., Ghosh, I., “A Data mining Technique for analysing and predicting the success of movie”, Journal of Physics: Conference Series, Vol 1000, Issue 1, 2022 [Google Scholar]
- Saranya, G., & Pravin, A. (2020). A comprehensive study on disease risk predictions in machine learning. International Journal of Electrical and Computer Engineering (IJECE), 10(4),4217. [CrossRef] [Google Scholar]
- Saranya, G., Geetha, G., & Safa, M. (2017). E-antenatal assistance care using decision tree analytics and cluster analytics based supervised machine learning. 2017 International Conference on IoT and Application (ICIOT). [Google Scholar]
- G. Geetha, M. Safa, C. Fancy, D. Saranya “A hybrid approach using collaborative filtering and content-based filtering for recommender system”, Journal of Physics: Conference Series Vol 1000, Issue 1, 2018. [Google Scholar]
- M. Srivastava, S. Pallavi, S. Chandra, G. Geetha, “Comparison of optimizers implemented in Generative Adversarial Network (GAN)” International Journal of Pure and Applied Mathematics Volume 119 Issue 12, 2018. [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.