In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods.

The neural radiance field (NeRF) has demonstrated significant advancements in computer vision. However, the training process for NeRF models necessitates extensive computational resources and ample training data. In the event of unauthorized usage or theft of the model, substantial losses can be incurred by the copyright holder. To address this concern, we present a novel algorithm that leverages the implicit neural representation (INR) watermarking technique to safeguard NeRF model copyrights. By encoding the watermark information implicitly, we integrate its parameters into the NeRF model’s network using a unique key. Through this key, the copyright owner can extract the embedded watermarks from the NeRF model for ownership verification. To the best of our knowledge, this is the pioneering implementation of INR watermarking for the protection of NeRF model copyrights. Our experimental results substantiate that our approach not only offers robustness and preserves high-quality 3D reconstructions but also ensures the flawless (100%) extraction of watermark content, thereby effectively securing the copyright of the NeRF model.