Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model
Neural networks (NNs) benefit biomedicine and agriculture, especially when relying on the specificity and implementation of stochastic fractal-supported models. In the poultry industry, a particular challenge is the search for an ideal sorbent-based complex additive to minimize the loss of valuable...
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| Format: | Article |
| Language: | English |
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Gorgan University of Agricultural Sciences and Natural Resources
2025-06-01
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| Series: | Poultry Science Journal |
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| Online Access: | https://psj.gau.ac.ir/article_7267_5402a5b4c0f41073532c66ff456e2df5.pdf |
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| author | Larisa Karpenko Alesya Bakhta Olga Babich Stanislav Sukhikh Nikolay Vorobyov Ilya Nikonov Solomonida Borisova Darren Griffin Michael Romanov |
| author_facet | Larisa Karpenko Alesya Bakhta Olga Babich Stanislav Sukhikh Nikolay Vorobyov Ilya Nikonov Solomonida Borisova Darren Griffin Michael Romanov |
| author_sort | Larisa Karpenko |
| collection | DOAJ |
| description | Neural networks (NNs) benefit biomedicine and agriculture, especially when relying on the specificity and implementation of stochastic fractal-supported models. In the poultry industry, a particular challenge is the search for an ideal sorbent-based complex additive to minimize the loss of valuable feed components that can be tailored to groups of gastrointestinal microorganisms. The aim of this study was thus to develop and apply a mathematical model and Gaussian NN to analyze productivity and blood parameters of laying hens when administering a complex feed additive from the mineral shungite sorbent, plus a nutritive supplement of brown seaweed meal. We developed and built a computational NN that modelled the stochastic ManyToOne relationship of an array of hens’ main blood parameters and performance traits. The results presented herein were that the artificial computational stochastic fractal-based NN (EuclidNN) first effectively analyzed the profiles of operational taxonomic units (OTUs) of the physiological/biochemical blood parameters. Also, correlation coefficients were highly positive in relation to certain zootechnical indicators, suggesting that feed additive intake may have led to changes in these performance traits. Calculations suggested that when implementing the feed additive, the values of the Cognitive Salience Index (CSI) vector vCSI2 declined. Hereby, this vector correlates with, and affects the egg production trait. Moreover, there was a certain relationship between the feed additive intake and feed and water consumption. Further, EuclidNN computed the respective bioconsolidation indices of hens and, simultaneously, processed several profiles of OTUs for all experimental variants. It also contributed to the calculation of bioconsolidation index values for each variant, i.e., a quantitative assessment of the physiological/biochemical blood descriptors, depending on diet. Collectively, the poultry productivity prediction based on the developed model and NN is pivotal as an initial step for future improvements of economically important traits in chickens when using novel and efficient complex feed additives. |
| format | Article |
| id | doaj-art-6b686f0945b840fdb8708e14db72233f |
| institution | Kabale University |
| issn | 2345-6604 2345-6566 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Gorgan University of Agricultural Sciences and Natural Resources |
| record_format | Article |
| series | Poultry Science Journal |
| spelling | doaj-art-6b686f0945b840fdb8708e14db72233f2025-08-20T03:32:16ZengGorgan University of Agricultural Sciences and Natural ResourcesPoultry Science Journal2345-66042345-65662025-06-0113224325210.22069/psj.2025.23008.22077267Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network ModelLarisa Karpenko0Alesya Bakhta1Olga Babich2Stanislav Sukhikh3Nikolay Vorobyov4Ilya Nikonov5Solomonida Borisova6Darren Griffin7Michael Romanov8Federal State-Funded Educational Institution of Higher Education “St. Petersburg State University of Veterinary Medicine”, St. Petersburg, 196084, RussiaFederal State-Funded Educational Institution of Higher Education “St. Petersburg State University of Veterinary Medicine”, St. Petersburg, 196084, RussiaResearch and Education Center “Industrial Biotechnologies”, Immanuel Kant Baltic Federal University, Kaliningrad, 236016, RussiaResearch and Education Center “Industrial Biotechnologies”, Immanuel Kant Baltic Federal University, Kaliningrad, 236016, RussiaAll-Russian Research Institute for Agricultural Microbiology, Pushkin, St. Petersburg , 196608, Russia 4School of Natural Sciences, University of Kent, Canterbury, CT2 7NJ, UKFederal State-Funded Educational Institution of Higher Education “St. Petersburg State University of Veterinary Medicine”, St. Petersburg, 196084, RussiaFederal State-Funded Educational Institution of Higher Education “St. Petersburg State University of Veterinary Medicine”, St. Petersburg, 196084, RussiaSchool of Natural Sciences, University of Kent, Canterbury, CT2 7NJ, UKSchool of Natural Sciences, University of Kent, Canterbury, CT2 7NJ, UKNeural networks (NNs) benefit biomedicine and agriculture, especially when relying on the specificity and implementation of stochastic fractal-supported models. In the poultry industry, a particular challenge is the search for an ideal sorbent-based complex additive to minimize the loss of valuable feed components that can be tailored to groups of gastrointestinal microorganisms. The aim of this study was thus to develop and apply a mathematical model and Gaussian NN to analyze productivity and blood parameters of laying hens when administering a complex feed additive from the mineral shungite sorbent, plus a nutritive supplement of brown seaweed meal. We developed and built a computational NN that modelled the stochastic ManyToOne relationship of an array of hens’ main blood parameters and performance traits. The results presented herein were that the artificial computational stochastic fractal-based NN (EuclidNN) first effectively analyzed the profiles of operational taxonomic units (OTUs) of the physiological/biochemical blood parameters. Also, correlation coefficients were highly positive in relation to certain zootechnical indicators, suggesting that feed additive intake may have led to changes in these performance traits. Calculations suggested that when implementing the feed additive, the values of the Cognitive Salience Index (CSI) vector vCSI2 declined. Hereby, this vector correlates with, and affects the egg production trait. Moreover, there was a certain relationship between the feed additive intake and feed and water consumption. Further, EuclidNN computed the respective bioconsolidation indices of hens and, simultaneously, processed several profiles of OTUs for all experimental variants. It also contributed to the calculation of bioconsolidation index values for each variant, i.e., a quantitative assessment of the physiological/biochemical blood descriptors, depending on diet. Collectively, the poultry productivity prediction based on the developed model and NN is pivotal as an initial step for future improvements of economically important traits in chickens when using novel and efficient complex feed additives.https://psj.gau.ac.ir/article_7267_5402a5b4c0f41073532c66ff456e2df5.pdfcomplex feed additiveadsorbent shungitebrown algaeblood parametergaussian neural network |
| spellingShingle | Larisa Karpenko Alesya Bakhta Olga Babich Stanislav Sukhikh Nikolay Vorobyov Ilya Nikonov Solomonida Borisova Darren Griffin Michael Romanov Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model Poultry Science Journal complex feed additive adsorbent shungite brown algae blood parameter gaussian neural network |
| title | Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model |
| title_full | Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model |
| title_fullStr | Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model |
| title_full_unstemmed | Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model |
| title_short | Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model |
| title_sort | effects of a complex feed additive on productivity and blood parameters of laying hens using stochastic fractal based neural network model |
| topic | complex feed additive adsorbent shungite brown algae blood parameter gaussian neural network |
| url | https://psj.gau.ac.ir/article_7267_5402a5b4c0f41073532c66ff456e2df5.pdf |
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