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Showing 1 to 7 of 7 for “"automatic feature extraction"”.

  1. Automatic feature extraction for time series analysis using deep and machine learning

    … through continuous wavelet transform, serve as feature extractors. The trained model accurately distinguishes ECGs with fall activity from those without at an accuracy of 98.02%. The robustness of the algorithm is verified by augmenting the experimental dataset with publicly available datasets, …

    cadiz Repository record for Automatic feature extraction for time series analysis using deep and machine learning (opens in a new tab)

  2. Feature extraction and matching of palmprints using level I detail

    Current Automatic Palmprint Identification Systems (APIS) closely follow the matching philosophy of Automatic Fingerprint Identification Systems (AFIS), in that they exclusively use a small subset of Level II palmar detail, when matching a latent to an exemplar palm print. However, due the …

    wlv Repository record for Feature extraction and matching of palmprints using level I detail (opens in a new tab)

  3. A Data Fusion Framework for Floodplain Analysis using GIS and Remotely Sensed Data

    … phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are …

    unt Repository record for A Data Fusion Framework for Floodplain Analysis using GIS and Remotely Sensed Data (opens in a new tab)

  4. Deep Learning-based Time Series Forecasting: Models and Applications

    … intelligence, deep learning has efficient automatic feature extraction and robust representation learning capabilities. Using deep learning to enhance time series forecasting performance has become an important research direction. This dissertation studies the point estimation and …

    uts Repository record for Deep Learning-based Time Series Forecasting: Models and Applications (opens in a new tab)

  5. Deep learning-based seagrass detection and classification from underwater digital images

    … seagrass identification or mapping due to their automatic feature extraction ability and higher performance over machine learning techniques. Making a deep learning-based model for all domain users (not only computer vision experts or engineers) is also a challenging task because CNNs development …

    edithcowan Repository record for Deep learning-based seagrass detection and classification from underwater digital images (opens in a new tab)

  6. Face recognition using Hidden Markov Models

    … HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding …

    cambridge Repository record for Face recognition using Hidden Markov Models (opens in a new tab)

  7. Controlling the effect of crowd noisy annotations in NLP Tasks

    … with the aim of studying problems in the automatic generation and understanding of natural language. It involves identifying and exploiting linguistic rules and variation with code to translate unstructured language data into information with a schema. Empirical methods in NLP employ …

    trento Repository record for Controlling the effect of crowd noisy annotations in NLP Tasks (opens in a new tab)