A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

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2. Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects G. Alam et al. https://doi.org/10.1016/j.cej.2021.130011
3. Prediction for Biodiesel Quality using the Dielectric Properties . Rahmawati https://doi.org/10.1088/1757-899X/536/1/012057
4. Human Activities and Postural Transitions Classification using Support Vector Machine and K-Nearest Neighbor Methods I. Yulita & S. Saori https://doi.org/10.1088/1755-1315/248/1/012025
5. PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION S. Prasad et al. https://doi.org/10.21307/ijssis-2018-020
6. A New Methodology to Arrive at Membership Weights for Fuzzy SVM . Maruthamuthu A. et al. https://doi.org/10.4018/IJFSA.285556
7. GPU parallel implementation and optimisation of SAR target recognition method H. Quan et al. https://doi.org/10.1049/joe.2019.0669
8. Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays M. Maia et al. https://doi.org/10.3390/info11120548
9. Exploring the Capability of Compact Polarimetry (Hybrid Pol) C Band RISAT-1 Data for Land Cover Classification K. Dasari & A. Lokam https://doi.org/10.1109/ACCESS.2018.2873348
10. MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data I. Khosravi et al. https://doi.org/10.1080/01431161.2018.1425564
11. Data-driven surrogate modeling for performance prediction and sensitivity analysis of transport properties in proton exchange membrane water electrolyzers K. Raman et al. https://doi.org/10.1016/j.apenergy.2025.125529
12. Change Detection of Polarimetric SAR Images Based on the Integration of Improved Watershed and MRF Segmentation Approaches M. Omati & M. Sahebi https://doi.org/10.1109/JSTARS.2018.2874517
13. Application of active learning in DNA microarray data for cancerous gene identification S. Begum et al. https://doi.org/10.1016/j.eswa.2021.114914
14. Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco S. Hajaj et al. https://doi.org/10.3390/min13060766
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17. Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction D. NURWAHA https://doi.org/10.31593/ijeat.630789
18. Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges J. Wani et al. https://doi.org/10.1007/s11831-021-09588-5
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22. SVM and SVM Ensembles in Breast Cancer Prediction M. Huang et al. https://doi.org/10.1371/journal.pone.0161501
23. A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets S. Seydi et al. https://doi.org/10.3390/rs12122010
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25. Sustainability planning in the US response to the opioid crisis: An examination using expert and text mining approaches C. Gallo et al. https://doi.org/10.1371/journal.pone.0245920
26. Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague’s centre P. Golej et al. https://doi.org/10.2478/geosc-2022-0009
27. Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns N. Kranjčić et al. https://doi.org/10.3390/rs11060655
28. A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges J. Xie et al. https://doi.org/10.1109/COMST.2018.2866942
29. Improved automated detection of glaucoma by correlating fundus and SD‐OCT image analysis T. Shehryar et al. https://doi.org/10.1002/ima.22413
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31. Timely assessment of maize lodging severity with limited samples using multi-temporal Sentinel-1 and Sentinel-2 data across large spatial extents C. Cai et al. https://doi.org/10.1016/j.compag.2025.110671
32. Big picture on coagulation-flocculation technology integrated artificial intelligence for wastewater treatment application: A systematic mapping review M. Mohamed Noor et al. https://doi.org/10.1016/j.psep.2026.108445
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