Zobrazeno 1 - 10
of 190
pro vyhledávání: '"J. Senthilnath"'
Publikováno v:
IEEE Access, Vol 10, Pp 115092-115107 (2022)
Perhaps the most recent controversial topic in network science research is to determine whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] asserted
Externí odkaz:
https://doaj.org/article/d351fb9077e44321a6c0742dd4f2c7ab
Autor:
Yahui Guo, Yongshuo H. Fu, Shouzhi Chen, Christopher Robin Bryant, Xinxi Li, J. Senthilnath, Hongyong Sun, Shuxin Wang, Zhaofei Wu, Kirsten de Beurs
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102435- (2021)
The extraction of phenological events in forest and agriculture commonly relies on Vegetation Indices (VI) composed by visible and near infrared bands from satellite images. However, the textural information playing an important role in image fusion,
Externí odkaz:
https://doaj.org/article/f1b9322073de43fe89924174e139f2cf
Autor:
Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath
Publikováno v:
Ecological Indicators, Vol 120, Iss , Pp 106935- (2021)
Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with the growth of the global population. Precisely predicting rice yields are of vital importance to ensure the food security in countries like China, where ri
Externí odkaz:
https://doaj.org/article/681319c8e6d3410cbf182d64fffda8c6
Autor:
Yahui Guo, Hanxi Wang, Zhaofei Wu, Shuxin Wang, Hongyong Sun, J. Senthilnath, Jingzhe Wang, Christopher Robin Bryant, Yongshuo Fu
Publikováno v:
Sensors, Vol 20, Iss 18, p 5055 (2020)
The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) a
Externí odkaz:
https://doaj.org/article/dbe94e1d08d8427884f156e568434688
Autor:
Yahui Guo, Guodong Yin, Hongyong Sun, Hanxi Wang, Shouzhi Chen, J. Senthilnath, Jingzhe Wang, Yongshuo Fu
Publikováno v:
Sensors, Vol 20, Iss 18, p 5130 (2020)
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing.
Externí odkaz:
https://doaj.org/article/1232f0e1b8734a359d99764ebde03b23
Publikováno v:
Remote Sensing, Vol 12, Iss 2, p 245 (2020)
Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem usin
Externí odkaz:
https://doaj.org/article/430603f3f0e646208171a93aa8feab6e
Publikováno v:
Algorithms, Vol 11, Iss 5, p 56 (2018)
This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms
Externí odkaz:
https://doaj.org/article/5e51fec4641640288f643be625014f33
Scalable methods of storing renewable energy to reduce climate change require high- performance catalysts that can be discovered using Artificial Intelligence (AI), prompting data scientists to train models. In the OC20 dataset (IS2RE), the Graphorme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d360cf2f8d5580089dbe881c53ce05ef
https://doi.org/10.14293/p2199-8442.1.sop-.pibjc1.v1
https://doi.org/10.14293/p2199-8442.1.sop-.pibjc1.v1
With high device integration density and evolving sophisticated device structures in semiconductor chips, detecting defects becomes elusive and complex. Conventionally, machine learning (ML)-guided failure analysis is performed with offline batch mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e58e37a429b9c33bd8be02ff56fab591
http://arxiv.org/abs/2303.07062
http://arxiv.org/abs/2303.07062
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