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Bibliografia publikacji pracowników
Państwowej Szkoły Wyższej w Białej Podlaskiej

Baza tworzona przez Bibliotekę Akademii Bialskiej im. Jana Pawła II.


Zapytanie: KUJAWSKA JUSTYNA
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Nr opisu: ginal-article^bOryginalny artykuł naukowyACPartykuł w czasopi¶mie polskim^a998899^b99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWA70.000PUNKTACJA UWM^a009999.000^b009929.000^c009999.000^d009929.000202320232023Implications of neural network as a decision-making tool in managing Kazakhstan's agricultural ec00000470610000000332AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAABartykuł w czasopi¶mie bez IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://ph.pollub.pl/index.php/acs/article/view/5614100^bQ^e2353-6977^iX^jXY^kQ029769^a003^b003^c2024-02-28, 10:03^d2024-02-28, 10:05^e3028769236^f3028769234^aImplications of neural network as a decision-making tool in managing Kazakhstan's agricultural economy^aApplied Computer Science^a2023^bVol. 19^ciisue 4qp. 121--135^a2353-6977^a2023/2024^a10.35784/acs-2023-39^aLichograj, Piotr^cx^aagriculture^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aThis study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems. © 2023, Polish Association for Knowledge Promotion. All rights reserved.^aartifical neural network^adecision-making^aeconomy^amanagement
Autorzy: , , , Oryginalny artykuł naukowyACPartykuł w czasopi¶mie polskim 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWA70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 19 CC-BY original-article998899009999.000003Implications of neural network as a decision-making tool in managing Kazakhstan's agricultural economyApplied Computer Science20232353-69772023/202410.35784/acs-2023-39Lichograj, PiotragricultureFINAL_PUBLISHEDThis study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems. © 2023, Polish Association for Knowledge Promotion. All rights reserved.artifical neural networkdecision-makingeconomymanagement.
Tytuł pracy:
Tytuł pracy w innym języku: original-article998899009999.000003Implications of neural network as a decision-making tool in managing Kazakhstan's agricultural economyApplied Computer Science20232353-69772023/202410.35784/acs-2023-39Lichograj, PiotragricultureFINAL_PUBLISHEDThis study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems. © 2023, Polish Association for Knowledge Promotion. All rights reserved.artifical neural networkdecision-makingeconomymanagement : Oryginalny artykuł naukowyACPartykuł w czasopi¶mie polskim : 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWA70.000PUNKTACJA UWM : 009929.000 : Q : 003 : Vol. 19 : CC-BY
Tryb dostępu: 998899009999.000003Implications of neural network as a decision-making tool in managing Kazakhstan's agricultural economyApplied Computer Science20232353-69772023/202410.35784/acs-2023-39Lichograj, PiotragricultureFINAL_PUBLISHEDThis study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems. © 2023, Polish Association for Knowledge Promotion. All rights reserved.artifical neural networkdecision-makingeconomymanagement
Tytuł monografii w innym języku: original-article998899009999.000 : Oryginalny artykuł naukowyACPartykuł w czasopi¶mie polskim : 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWA70.000PUNKTACJA UWM : 009929.000
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