TAR: A Highly Accurate Machine-Learning Model to Predict the Cocoon Shell Weight of Tasar Silkworm Antheraea Mylitta
In this paper, we propose a machine-learning model for predicting the shell weight of silkworm cocoons Antheraea mylitta D. ( Saturnidae ) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-...
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| Veröffentlicht in: | Agricultural research (India : Online) 2024, Vol.13 (2), p.375-380 |
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| creator | Alam, Khasru Paik, Jiaul H. Saha, Soumen Suresh, Raviraj V. |
| description | In this paper, we propose a machine-learning model for predicting the shell weight of silkworm cocoons
Antheraea mylitta D.
(
Saturnidae
) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-invasive easy-to-measure cocoon features. We evaluate our model on four datasets from different families of cocoons. The evaluation shows that the proposed model accurately predicts the shell weight and outperforms well-known models, including neural network-based regression. |
| doi_str_mv | 10.1007/s40003-023-00687-2 |
| format | Article |
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Antheraea mylitta D.
(
Saturnidae
) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-invasive easy-to-measure cocoon features. We evaluate our model on four datasets from different families of cocoons. The evaluation shows that the proposed model accurately predicts the shell weight and outperforms well-known models, including neural network-based regression.</description><identifier>ISSN: 2249-720X</identifier><identifier>EISSN: 2249-7218</identifier><identifier>DOI: 10.1007/s40003-023-00687-2</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Biodiversity ; Biomedical and Life Sciences ; Cell Biology ; Cocoons ; Full-Length Research Article ; Learning algorithms ; Life Sciences ; Machine learning ; Neural networks ; Plant Biochemistry ; Plant Ecology ; Plant Genetics and Genomics ; Plant Sciences ; Regression analysis ; Silkworms ; Topology ; Weight</subject><ispartof>Agricultural research (India : Online), 2024, Vol.13 (2), p.375-380</ispartof><rights>The Author(s), under exclusive licence to National Academy of Agricultural Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-ecccecef4d8c82b95a86f1249bd6b1e02483a1ebe51d34a17694ca5b73549fd23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40003-023-00687-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40003-023-00687-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,782,786,27931,27932,41495,42564,51326</link.rule.ids></links><search><creatorcontrib>Alam, Khasru</creatorcontrib><creatorcontrib>Paik, Jiaul H.</creatorcontrib><creatorcontrib>Saha, Soumen</creatorcontrib><creatorcontrib>Suresh, Raviraj V.</creatorcontrib><title>TAR: A Highly Accurate Machine-Learning Model to Predict the Cocoon Shell Weight of Tasar Silkworm Antheraea Mylitta</title><title>Agricultural research (India : Online)</title><addtitle>Agric Res</addtitle><description>In this paper, we propose a machine-learning model for predicting the shell weight of silkworm cocoons
Antheraea mylitta D.
(
Saturnidae
) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-invasive easy-to-measure cocoon features. We evaluate our model on four datasets from different families of cocoons. The evaluation shows that the proposed model accurately predicts the shell weight and outperforms well-known models, including neural network-based regression.</description><subject>Biodiversity</subject><subject>Biomedical and Life Sciences</subject><subject>Cell Biology</subject><subject>Cocoons</subject><subject>Full-Length Research Article</subject><subject>Learning algorithms</subject><subject>Life Sciences</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Plant Biochemistry</subject><subject>Plant Ecology</subject><subject>Plant Genetics and Genomics</subject><subject>Plant Sciences</subject><subject>Regression analysis</subject><subject>Silkworms</subject><subject>Topology</subject><subject>Weight</subject><issn>2249-720X</issn><issn>2249-7218</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWLR_wFPA82q-9svbUtQKLYqt6C1ks7PdrdtNTVKk_97UFb15GGYYnvcd5kXogpIrSkh67QQhhEeEhSJJlkbsCI0YE3mUMpod_87k7RSNnVsHOuxpJtgI-WXxfIMLPG1XTbfHhdY7qzzgudJN20M0A2X7tl_huamgw97gJwtVqz32DeCJ0cb0eNFA1-FXCB4emxovlVMWL9ru_dPYDS76wFoFCs_3Xeu9OkcnteocjH_6GXq5u11OptHs8f5hUswizVLiI9Bag4ZaVJnOWJnHKktqGn4pq6SkQJjIuKJQQkwrLhRNk1xoFZcpj0VeV4yfocvBd2vNxw6cl2uzs304KTlJUkGTmItAsYHS1jhnoZZb226U3UtK5CFgOQQsQ8DyO2B5sOaDyAW4X4H9s_5H9QUbN32m</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Alam, Khasru</creator><creator>Paik, Jiaul H.</creator><creator>Saha, Soumen</creator><creator>Suresh, Raviraj V.</creator><general>Springer India</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2024</creationdate><title>TAR: A Highly Accurate Machine-Learning Model to Predict the Cocoon Shell Weight of Tasar Silkworm Antheraea Mylitta</title><author>Alam, Khasru ; Paik, Jiaul H. ; Saha, Soumen ; Suresh, Raviraj V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-ecccecef4d8c82b95a86f1249bd6b1e02483a1ebe51d34a17694ca5b73549fd23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Biodiversity</topic><topic>Biomedical and Life Sciences</topic><topic>Cell Biology</topic><topic>Cocoons</topic><topic>Full-Length Research Article</topic><topic>Learning algorithms</topic><topic>Life Sciences</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Plant Biochemistry</topic><topic>Plant Ecology</topic><topic>Plant Genetics and Genomics</topic><topic>Plant Sciences</topic><topic>Regression analysis</topic><topic>Silkworms</topic><topic>Topology</topic><topic>Weight</topic><toplevel>online_resources</toplevel><creatorcontrib>Alam, Khasru</creatorcontrib><creatorcontrib>Paik, Jiaul H.</creatorcontrib><creatorcontrib>Saha, Soumen</creatorcontrib><creatorcontrib>Suresh, Raviraj V.</creatorcontrib><collection>CrossRef</collection><jtitle>Agricultural research (India : Online)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alam, Khasru</au><au>Paik, Jiaul H.</au><au>Saha, Soumen</au><au>Suresh, Raviraj V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TAR: A Highly Accurate Machine-Learning Model to Predict the Cocoon Shell Weight of Tasar Silkworm Antheraea Mylitta</atitle><jtitle>Agricultural research (India : Online)</jtitle><stitle>Agric Res</stitle><date>2024</date><risdate>2024</risdate><volume>13</volume><issue>2</issue><spage>375</spage><epage>380</epage><pages>375-380</pages><issn>2249-720X</issn><eissn>2249-7218</eissn><abstract>In this paper, we propose a machine-learning model for predicting the shell weight of silkworm cocoons
Antheraea mylitta D.
(
Saturnidae
) without cutting open the cocoon. Our proposed work uses a topology adaptive kernel regression (TAR) to predict the shell weight of cocoons based on a set of non-invasive easy-to-measure cocoon features. We evaluate our model on four datasets from different families of cocoons. The evaluation shows that the proposed model accurately predicts the shell weight and outperforms well-known models, including neural network-based regression.</abstract><cop>New Delhi</cop><pub>Springer India</pub><doi>10.1007/s40003-023-00687-2</doi><tpages>6</tpages></addata></record> |
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| ispartof | Agricultural research (India : Online), 2024, Vol.13 (2), p.375-380 |
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| source | SpringerNature Journals |
| subjects | Biodiversity Biomedical and Life Sciences Cell Biology Cocoons Full-Length Research Article Learning algorithms Life Sciences Machine learning Neural networks Plant Biochemistry Plant Ecology Plant Genetics and Genomics Plant Sciences Regression analysis Silkworms Topology Weight |
| title | TAR: A Highly Accurate Machine-Learning Model to Predict the Cocoon Shell Weight of Tasar Silkworm Antheraea Mylitta |
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