Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Abdelguerfi A"'
Autor:
Ferdaus, Md Meftahul, Abdelguerfi, Mahdi, Ioup, Elias, Dobson, David, Niles, Kendall N., Pathak, Ken, Sloan, Steven
We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Co
Externí odkaz:
http://arxiv.org/abs/2410.17172
Autor:
Alshawi, Rasha, Ferdaus, Md Meftahul, Abdelguerfi, Mahdi, Niles, Kendall, Pathak, Ken, Sloan, Steve
Imbalanced datasets are a significant challenge in real-world scenarios. They lead to models that underperform on underrepresented classes, which is a critical issue in infrastructure inspection. This paper introduces the Enhanced Feature Pyramid Net
Externí odkaz:
http://arxiv.org/abs/2408.10181
Autor:
Alshawi, Rasha, Ferdaus, Md Meftahul, Hoque, Md Tamjidul, Niles, Kendall, Pathak, Ken, Sloan, Steve, Abdelguerfi, Mahdi
This paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net integrates a bottom-up pathway featuring Inception-like blocks with varying filter sizes (3x3$ and 5x5), paral
Externí odkaz:
http://arxiv.org/abs/2408.08879
Autor:
Ferdaus, Md Meftahul, Abdelguerfi, Mahdi, Ioup, Elias, Niles, Kendall N., Pathak, Ken, Sloan, Steven
The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and building user trust. This comprehensive review looks at key trust issues in
Externí odkaz:
http://arxiv.org/abs/2407.13934
Publikováno v:
Agricultura, Vol 118, Iss 1-2 (2021)
The chemical composition and nutritional value of 16 perennial alfalfa varieties Medicago sativa L. from different origins, newly introduced in Algeria were determined. The methods of analysis were those of the AOAC. The nutritive value was calculate
Externí odkaz:
https://doaj.org/article/4a46297900f840c7a89ae77c088292b8
Publikováno v:
2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 2920-2929
Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking obs
Externí odkaz:
http://arxiv.org/abs/2404.14388
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3668-3674
This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes.
Externí odkaz:
http://arxiv.org/abs/2404.14357
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
Autor:
Alshawi, Rasha, Hoque, Md Tamjidul, Ferdaus, Md Meftahul, Abdelguerfi, Mahdi, Niles, Kendall, Prathak, Ken, Tom, Joe, Klein, Jordan, Mousa, Murtada, Lopez, Johny Javier
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples. DAU-FI Net integrates multiscale spatial-channel a
Externí odkaz:
http://arxiv.org/abs/2312.14053
Autor:
Manisha Panta, Padam Jung Thapa, Md Tamjidul Hoque, Kendall N. Niles, Steve Sloan, Maik Flanagin, Ken Pathak, Mahdi Abdelguerfi
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2441 (2024)
Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. The
Externí odkaz:
https://doaj.org/article/43069f4516c840a69ebdc64db7c2c0ef
In this paper, we present a novel approach for the prediction of rogue waves in oceans using statistical machine learning methods. Since the ocean is composed of many wave systems, the change from a bimodal or multimodal directional distribution to u
Externí odkaz:
http://arxiv.org/abs/2003.06431