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- Multiple Changepoint Detection via Genetic Algorithms97
Genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series are studied, via a genetic algorithm that randomly walks through a subset of good models in an intelligent manner.
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- A Low-Cost and Fast Vehicle Detection Algorithm With a Monocular Camera for Adaptive Driving Beam Systems17
A new and efficient algorithm for detecting and tracking based on video collected by a low-cost camera that has low time cost and can be applied in embedded devices of the vehicle is proposed.
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- MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation3
A novel multi-scale token fusion algorithm that selectively fuses only the most important tokens, enabling the model to better capture multi-scale characteristics in important regions and introduces a token learning algorithm based on contrastive loss.
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- Multimodal Learning for Fake News Detection in Short Videos Using Linguistically Verified Data and Heterogeneous Modality Fusion1
HFN is presented, Heterogeneous Fusion Net, a novel multimodal framework that integrates video, audio, and text data to evaluate the authenticity of short video content and establishes a robust solution capable of effectively identifying fake news in the complex landscape of short video platforms.
- Genetic Algorithm Techniques in Climate Changepoint Problems–
Genetic algorithms are studied as a means of estimating the number of changepoints and their locations in a climatic time series and trend estimation techniques are developed for monthly maximum and minimum temperatures observed in the conterminous 48 United States over the last century.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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