Authors :
Archith Shankar; Arushi R Kadam; Nishita Senthilkumar; Shradha A Venkatachalam; Shivandappa; Narendra Kumar
Volume/Issue :
Volume 9 - 2024, Issue 9 - September
Google Scholar :
https://tinyurl.com/4e7nrhyj
Scribd :
https://tinyurl.com/588xk5r4
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24SEP354
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The classification of Dry Beans using various
techniques such as Support Vector Machine (SVM)
classification, K-means clustering, Decision Trees and
Random Forest (RF) classification using an ipython
notebook. To refine the model, performance matrix graphs
of Cross entropy vs Epoch number, True value vs Predictive
value and Accuracy vs Epoch. This analysis is often used in
agricultural practices for improved crop management,
increasing yield, resource optimization, enhancing
sustainability etc.
Keywords :
Dry Beans, Phaseolus Vulgaris L, Machine Learning, Classification Methods, KNN Cluster, Support Vector Machine.
References :
- Dry Bean [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C50S4B.
- Geng, Y., Li, Q., Yang, G., Qiu, W. (2024). Logistic Regression. In: Practical Machine Learning Illustrated with KNIME. Springer, Singapore. https://doi.org/10.1007/978-981-97-3954-7_4
- Cantemir, E., Kandemir, O. Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks. Neural Comput & Applic 36, 6195–6207 (2024). https://doi.org/10.1007/s00521-023-09395-y
- Hu, J., & Szymczak, S. (2023). A review on longitudinal data analysis with random forest. Briefings in bioinformatics, 24(2), bbad002. https://doi.org/10.1093/bib/bbad002
- Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of Support Vector Machine(SVM) Learning in Cancer Genomics. Cancer genomics & proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063
The classification of Dry Beans using various
techniques such as Support Vector Machine (SVM)
classification, K-means clustering, Decision Trees and
Random Forest (RF) classification using an ipython
notebook. To refine the model, performance matrix graphs
of Cross entropy vs Epoch number, True value vs Predictive
value and Accuracy vs Epoch. This analysis is often used in
agricultural practices for improved crop management,
increasing yield, resource optimization, enhancing
sustainability etc.
Keywords :
Dry Beans, Phaseolus Vulgaris L, Machine Learning, Classification Methods, KNN Cluster, Support Vector Machine.
