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Showing 1 to 4 of 4 for “"multi-modal data fusion"”.

  1. Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking

    … While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial …

    chapman Repository record for Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking (opens in a new tab)

  2. Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying

    … scale drying processes and systems involve multiple interacting process parameters, conflicting production objectives, and highly uncertain sample characteristics, which make process control extremely challenging. Current industrial practice lacks the necessary decision-making tools to …

    uiuc Repository record for Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying (opens in a new tab)

  3. Transformer Networks for Smart Cities: Framework and Application to Makassar Smart Garden Alleys

    … system design challenges pertaining to the fusion of the diverse, heterogeneous datasets available to IoT environments, and the ability to learn multiple S&CC problem sets concurrently. Attention-based Transformer networks are of particular interest given their success across diverse fields …

    vt Repository record for Transformer Networks for Smart Cities: Framework and Application to Makassar Smart Garden Alleys (opens in a new tab)

  4. Efficient Distributed and Multi-Modal Machine Learning in Wireless Networks

    … and the scarce and private nature of wireless data. First, ML models at the application layer, e.g., on-device AI, often require private data from distributed devices. One can resort to distributed ML algorithms such as federated learning (FL) by communicating only ML model parameters over …

    vt Repository record for Efficient Distributed and Multi-Modal Machine Learning in Wireless Networks (opens in a new tab)