Neural network-based species identification in venom-interacted cases in India
India is home to a number of venomous species. Every year in harvesting season, a large number of productive citizens are envenomed by such species. For efficient medical management of the victims, identification of the aggressor species as well as assessment of the envenomation degree is necessary....
Published in: | Journal of Venomous Animals and Toxins including Tropical Diseases |
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Language: | English |
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Online Access: | https://doi.org/10.1590/S1678-91992007000400008 https://doaj.org/article/c44c4317125c4d93bb7f81a8d470644e |
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ftdoajarticles:oai:doaj.org/article:c44c4317125c4d93bb7f81a8d470644e 2023-05-15T15:06:17+02:00 Neural network-based species identification in venom-interacted cases in India R. Maheshwari V. Kumar H. K. Verma 2007-01-01T00:00:00Z https://doi.org/10.1590/S1678-91992007000400008 https://doaj.org/article/c44c4317125c4d93bb7f81a8d470644e EN eng SciELO http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1678-91992007000400008 https://doaj.org/toc/1678-9199 doi:10.1590/S1678-91992007000400008 1678-9199 https://doaj.org/article/c44c4317125c4d93bb7f81a8d470644e Journal of Venomous Animals and Toxins including Tropical Diseases, Vol 13, Iss 4, Pp 766-781 (2007) bites and stings symptoms species identification neural network Arctic medicine. Tropical medicine RC955-962 Toxicology. Poisons RA1190-1270 Zoology QL1-991 article 2007 ftdoajarticles https://doi.org/10.1590/S1678-91992007000400008 2022-12-31T01:36:54Z India is home to a number of venomous species. Every year in harvesting season, a large number of productive citizens are envenomed by such species. For efficient medical management of the victims, identification of the aggressor species as well as assessment of the envenomation degree is necessary. Species identification is generally based on the visual description by the victim or a witness and is therefore quite likely to be erroneous. Symptomatic identification remains the only available method. In a previous published work, the authors proposed a classification table for snake species based on manifested symptoms applicable in Indian subcontinent. The classification table serves the purpose to a great deal but as a manual method it demands human expertise. The current paper presents a neural network-based symptomatic species identification system. A symptom vector is fed as input to the neural network and the system yields the most probable species as well as the envenomation severity as the output. The severity status can be very helpful in calculating the antivenom dosage and in deciding the species-specific prognostic measures for efficient medical management. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Indian Journal of Venomous Animals and Toxins including Tropical Diseases 13 4 766 781 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
bites and stings symptoms species identification neural network Arctic medicine. Tropical medicine RC955-962 Toxicology. Poisons RA1190-1270 Zoology QL1-991 |
spellingShingle |
bites and stings symptoms species identification neural network Arctic medicine. Tropical medicine RC955-962 Toxicology. Poisons RA1190-1270 Zoology QL1-991 R. Maheshwari V. Kumar H. K. Verma Neural network-based species identification in venom-interacted cases in India |
topic_facet |
bites and stings symptoms species identification neural network Arctic medicine. Tropical medicine RC955-962 Toxicology. Poisons RA1190-1270 Zoology QL1-991 |
description |
India is home to a number of venomous species. Every year in harvesting season, a large number of productive citizens are envenomed by such species. For efficient medical management of the victims, identification of the aggressor species as well as assessment of the envenomation degree is necessary. Species identification is generally based on the visual description by the victim or a witness and is therefore quite likely to be erroneous. Symptomatic identification remains the only available method. In a previous published work, the authors proposed a classification table for snake species based on manifested symptoms applicable in Indian subcontinent. The classification table serves the purpose to a great deal but as a manual method it demands human expertise. The current paper presents a neural network-based symptomatic species identification system. A symptom vector is fed as input to the neural network and the system yields the most probable species as well as the envenomation severity as the output. The severity status can be very helpful in calculating the antivenom dosage and in deciding the species-specific prognostic measures for efficient medical management. |
format |
Article in Journal/Newspaper |
author |
R. Maheshwari V. Kumar H. K. Verma |
author_facet |
R. Maheshwari V. Kumar H. K. Verma |
author_sort |
R. Maheshwari |
title |
Neural network-based species identification in venom-interacted cases in India |
title_short |
Neural network-based species identification in venom-interacted cases in India |
title_full |
Neural network-based species identification in venom-interacted cases in India |
title_fullStr |
Neural network-based species identification in venom-interacted cases in India |
title_full_unstemmed |
Neural network-based species identification in venom-interacted cases in India |
title_sort |
neural network-based species identification in venom-interacted cases in india |
publisher |
SciELO |
publishDate |
2007 |
url |
https://doi.org/10.1590/S1678-91992007000400008 https://doaj.org/article/c44c4317125c4d93bb7f81a8d470644e |
geographic |
Arctic Indian |
geographic_facet |
Arctic Indian |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Journal of Venomous Animals and Toxins including Tropical Diseases, Vol 13, Iss 4, Pp 766-781 (2007) |
op_relation |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1678-91992007000400008 https://doaj.org/toc/1678-9199 doi:10.1590/S1678-91992007000400008 1678-9199 https://doaj.org/article/c44c4317125c4d93bb7f81a8d470644e |
op_doi |
https://doi.org/10.1590/S1678-91992007000400008 |
container_title |
Journal of Venomous Animals and Toxins including Tropical Diseases |
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13 |
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4 |
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766 |
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781 |
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