- Research Article
- 10.1016/j.patcog.2026.113279
Learning to optimize unsupervised image fusion with learnable loss and fusion strategy
- Sep 01, 2026
- Pattern Recognition
- Liye Mei + 6 more +6
Publications from 2021 to 2026
Showing 10 of 21,293 papers
Learning to optimize unsupervised image fusion with learnable loss and fusion strategy
State-of-the-art evolution of detection, adsorption, and degradation of micro/nano-plastics
Advances in Data Mining for Food Flavor Analysis: A Comprehensive Review of Techniques, Applications and Future Directions
• The application of data mining based on artificial intelligence technology in the field of food flavor has been extensively discussed • The diverse types of data related to food flavors have been systematically organized, and their principal data processing methods have been thoroughly summarized • The data preprocessing steps are discussed in detail, and the impact of data augmentation techniques on enhancing the analytical capabilities of flavor omics is explored • Large models and deep learning are discussed as new data analysis methods for flavor prediction and recipe development In the field of food flavor, there exists a substantial amount of structured and unstructured data originating from flavoromics, databases, and social media. To effectively extract valuable information from these diverse data sources and promote rational application, extensive data mining efforts have been undertaken. This review provides a systematic overview of data mining in the context of food flavor and summarizes various multivariate data processing strategies. This review examines a wide array of current research in flavoromics and discuss pre-processing methods designed to address challenges such as small dataset sizes and complex manual data preparation. Furthermore, this review summarizes innovative approaches based on artificial intelligence and large language models, elucidating their prospective applications in flavor molecules prediction and recipes development. Lastly, we discuss the challenges and opportunities of applying data mining on flavor research.
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