[HTML][HTML] A robust hybrid machine learning model for Bengali cyber bullying detection in social media
Social networking platforms give users countless opportunities to share information,
collaborate, and communicate positively. The same platform can be extended to a fabricated …
collaborate, and communicate positively. The same platform can be extended to a fabricated …
A comprehensive survey of imbalance correction techniques for hyperspectral data classification
ME Paoletti, O Mogollon-Gutierrez… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data
exploitation. In this regard, HS classifiers have to face important challenges, such as the …
exploitation. In this regard, HS classifiers have to face important challenges, such as the …
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
A novel projection-based learning method named UnrollingNet is developed to conduct a
multi-label segmentation of various objects including seepage from 3D point clouds of …
multi-label segmentation of various objects including seepage from 3D point clouds of …
A spectral–spatial 3D-convolutional capsule network for hyperspectral image classification with limited training samples
In past few years, hyperspectral image classification (HSIC) has been one of the most
sparkling fields of research in the area of remote sensing. The presence of very complex …
sparkling fields of research in the area of remote sensing. The presence of very complex …
Effective segmentation of land-use and land-cover from hyperspectral remote sensing image
VP Yele, S Alegavi, RR Sedamkar - International Journal of Information …, 2024 - Springer
Hyperspectral images (HSI) provide valuable data for Land-Use and Land-Cover (LU/LC)
segmentation. Detecting buildings, roads, and LU/LC labels in satellite images is crucial for …
segmentation. Detecting buildings, roads, and LU/LC labels in satellite images is crucial for …
DesU-NetAM: optimized DenseU-Net with attention mechanism for hyperspectral image classification
K Balaji, V Nirosha, S Yallamandaiah, S Karthik… - International Journal of …, 2023 - Springer
The utilization of hyperspectral images (HSI) is expanding rapidly with the advancement of
remote sensing technology. Accurately categorizing ground features using HSI is a crucial …
remote sensing technology. Accurately categorizing ground features using HSI is a crucial …
A hypered deep-learning-based model of hyperspectral images generation and classification for imbalanced data
HAH Naji, T Li, Q Xue, X Duan - Remote Sensing, 2022 - mdpi.com
Recently, hyperspectral image (HSI) classification has become a hot topic in the
geographical images research area. Sufficient samples are required for image classes to …
geographical images research area. Sufficient samples are required for image classes to …
A synergistic fusion of shallow and deep generative model to enhance machine learning efficacy and classification performance in data-scarce environments
Data-constrained environments present a significant challenge to the effectiveness of
machine learning and deep learning algorithms. The performance of these algorithms is …
machine learning and deep learning algorithms. The performance of these algorithms is …
Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series
A Fonseca, MT Marshall, S Salama - Remote Sensing, 2024 - mdpi.com
Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of
deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury …
deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury …
Exploring Machine Learning Techniques for Music Emotion Classification: A Comprehensive Review
Music Emotion Recognition (MER) has made substantial advancements in recent years,
driven by improvements in signal processing, machine learning, and interdisciplinary …
driven by improvements in signal processing, machine learning, and interdisciplinary …