Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Rahman, A. K. M. Mahbubur"'
Autor:
Hossain, Mir Sazzat, Roy, Sugandha, Asad, K. M. B., Momen, Arshad, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, A. K. M. Mahbubur
Publikováno v:
Procedia Computer Science, Volume 222, 2023, Pages 601-612
Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and un
Externí odkaz:
http://arxiv.org/abs/2306.00031
This paper aims to detect rice field damage from natural disasters in Bangladesh using high-resolution satellite imagery. The authors developed ground truth data for rice field damage from the field level. At first, NDVI differences before and after
Externí odkaz:
http://arxiv.org/abs/2304.00622
Autor:
Deb, Tonmoay, Sadmanee, Akib, Bhaumik, Kishor Kumar, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, A K M Mahbubur
While describing Spatio-temporal events in natural language, video captioning models mostly rely on the encoder's latent visual representation. Recent progress on the encoder-decoder model attends encoder features mainly in linear interaction with th
Externí odkaz:
http://arxiv.org/abs/2201.00985
Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs. However, traditional GNNs suffer from two fundamental shortcomings due to their local ($l$-hop neighborhood) agg
Externí odkaz:
http://arxiv.org/abs/2104.13014
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. For these tasks, different pooling strategies have been proposed to generate a graph-level representation by downsampling and summa
Externí odkaz:
http://arxiv.org/abs/2104.13012
Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, most state-of-the-art approache
Externí odkaz:
http://arxiv.org/abs/2104.12518
To capture spatial relationships and temporal dynamics in traffic data, spatio-temporal models for traffic forecasting have drawn significant attention in recent years. Most of the recent works employed graph neural networks(GNN) with multiple layers
Externí odkaz:
http://arxiv.org/abs/2104.00055
Autor:
Tonmoy, M Tanjid Hasan, Mahmud, Saif, Rahman, A K M Mahbubur, Amin, M Ashraful, Ali, Amin Ahsan
Wearable sensor based human activity recognition is a challenging problem due to difficulty in modeling spatial and temporal dependencies of sensor signals. Recognition models in closed-set assumption are forced to yield members of known activity cla
Externí odkaz:
http://arxiv.org/abs/2103.04279
Autor:
Onim, Md. Saif Hassan, Ehtesham, Aiman Rafeed, Anbar, Amreen, Islam, A. K. M. Nazrul, Rahman, A. K. M. Mahbubur
This paper analyses how well a Fast Fully Convolutional Network (FastFCN) semantically segments satellite images and thus classifies Land Use/Land Cover(LULC) classes. Fast-FCN was used on Gaofen-2 Image Dataset (GID-2) to segment them in five differ
Externí odkaz:
http://arxiv.org/abs/2011.06825
Autor:
Pramanik, Md. Aktaruzzaman, Rahman, Md Mahbubur, Anam, ASM Iftekhar, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, A K M Mahbubur
Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, spe
Externí odkaz:
http://arxiv.org/abs/2011.02359