Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System
Sign Up to like & getrecommendations! Published in 2019 at "Water Resources Management"
DOI: 10.1007/s11269-019-02356-y
Abstract: This study presents a novel method for more accurate forecasting freshwater Lake Levels with complex fluctuation patterns due to multiple anthropogenic demands and climate factors. The new method employs the mighty King’s Castle Optimization (KCO)… read more here.
Keywords: castle optimization; adaptive neuro; training sample; king castle ... See more keywords
Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the Indian Society of Remote Sensing"
DOI: 10.1007/s12524-018-0777-z
Abstract: In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB),… read more here.
Keywords: effects training; classification; pixel based; training samples ... See more keywords
Adaptive Sample Weight for Machine Learning Computer Vision Algorithms in V2X Systems
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2018.2888969
Abstract: In machine learning, training sample set management has an important impact on the performance of visual detection and tracking algorithms, as corrupted training samples degrade the tracking performance, especially in practical scenarios such as vehicular… read more here.
Keywords: training sample; training; adaptive sample; training samples ... See more keywords
Iterative Spatial-Spectral Training Sample Augmentation for Effective Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3131373
Abstract: Factors such as insufficient training samples, high-dimensional data features, and unbalanced data classes can degrade the accuracy of hyperspectral classification. To this end, this letter proposes an iterative training sample augmentation (ITSA) algorithm and a… read more here.
Keywords: spatial spectral; classification; sample augmentation; training sample ... See more keywords
Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2912330
Abstract: Hyperspectral image (HSI) classification (HIC) has attracted much attention in the last decade. Spectral–spatial HIC methods have been the state-of-the-art methods in recent years. Small labeled training sample size (SLTSS) problem is still an important… read more here.
Keywords: hyperspectral image; training; sample size; classification ... See more keywords
Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2018.2870343
Abstract: In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar… read more here.
Keywords: stage transfer; multi stage; transfer learning; transfer ... See more keywords
The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Aging Neuroscience"
DOI: 10.3389/fnagi.2021.720636
Abstract: Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals,… read more here.
Keywords: sample size; white matter; volume; training sample ... See more keywords
Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search
Sign Up to like & getrecommendations! Published in 2023 at "Sustainability"
DOI: 10.3390/su15097458
Abstract: For the management of building operations, hourly building energy consumption prediction (HBECP) is critical. Many factors, such as energy types, expected day intervals, and acquired feature types, significantly impact HBECP. However, the existing training sample… read more here.
Keywords: feature search; feature; prediction; training sample ... See more keywords