Recent Papers

The SVM, CNN, HCNNRF model represents the most recent advance in machine learning, featuring the integrated structure of Hybrid Convolutional Neural Networks, Random Forests, Support Vector Machines, and Convolutional Neural Networks. This newly presented architecture leverages the HCNN capability to process both structured and unstructured data by integrating the feature extraction ability of CNN with the ensemble learning characteristics of Random Forests. In particular, it integrates SVM to further improve the model's performance on high-dimensional space and adds an extra component of CNN to bolster the data recognition capability. The proposed model utilizes a multi-stage training process for the model involving transfer learning, proposing an adaptive feature fusion mechanism. Its empirical evaluations show notable improvements in performance relative to existing models by showing enhanced accuracy, generalization, and robustness. Besides, in this work, the interpretability challenge of complex models in machine learning is presented, with some insight into the decision-making process. This versatile approach may promise to drive forward the field of predictive modelling in quite varied arenas are computer vision, natural language processing, and multimodal data analysis.

Copyright © 2024 S.Vasanthakumar. This is an open access article distributed under the Creative Commons Attribution License.