Zobrazeno 1 - 10
of 967
pro vyhledávání: '"V., Krishnakumar"'
We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises of acquired and derived heterogeneous data modalities that we transform to semanticall
Externí odkaz:
http://arxiv.org/abs/2309.15245
Publikováno v:
Circuit World, 2023, Vol. 50, Issue 2/3, pp. 293-307.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/CW-08-2022-0240
Autor:
E. V. Krishnakumar, Sandhra Satish, Joy Augustine, K. K. Ajaykumar, C. Davis Paul, N. A. Arun, P. Unnikrishnan
Publikováno v:
Journal of Advanced Lung Health, Vol 4, Iss 3, Pp 186-188 (2024)
A 28-year-old female, a known case of neurofibromatosis 1, presented to the respiratory medicine department with complaints of breathlessness and cough for 2 weeks’ duration. Clinical examination revealed right-sided moderate pleural effusion. Comp
Externí odkaz:
https://doaj.org/article/003b4126f9674ea0aec1356486cf7002
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this work,
Externí odkaz:
http://arxiv.org/abs/2210.03289
Publikováno v:
Journal of Ayurveda and Integrative Medicine, Vol 15, Iss 4, Pp 101016- (2024)
Precision in personalized medicine is a crucial subject that needs comprehensive discussion and scientific validation. Traditional healthcare approaches like the Ayurvedic Sciences are often contextually linked with personalized medicine. However, it
Externí odkaz:
https://doaj.org/article/c83e4ace69ee43d992cf9765e6b2cdf5
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2110.12521
Autor:
Iyer, C. V. Krishnakumar, Hou, Feili, Wang, Henry, Wang, Yonghong, Oh, Kay, Ganguli, Swetava, Pandey, Vipul
We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for
Externí odkaz:
http://arxiv.org/abs/2106.11756
Publikováno v:
Circuit World, 2020, Vol. 47, Issue 2, pp. 173-183.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/CW-12-2019-0200
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-3-W2-2020, Pp 137-142 (2020)
This work shows two examples on the use of Sentinel-1 data for monitoring different natural processes, like active geohazards or glacier dynamics in the Patagonia region. Sentinel-1 is a two-satellite constellation, launched by the European Space Age
Externí odkaz:
https://doaj.org/article/c75cc181fb414fb188e497d838aa619a
Autor:
M. Crosetto, G. Luzi, O. Monserrat, A. Barra, M. Cuevas-González, R. Palamá, V. Krishnakumar, Y. Wassie, S. M. Mirmazloumi, P. Espín-López, B. Crippa
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2020, Pp 287-292 (2020)
This paper is focused on SAR interferometry for deformation monitoring, based on the use of passive and active reflectors. Such reflectors are needed in all cases where a sufficient response from the ground is not available. In particular, the paper
Externí odkaz:
https://doaj.org/article/dde7c291811d4b92a0a3f9f9d4c328c7