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Ray, Mrinmoy
- Modelling and forecasting cotton production using tuned-support vector regression
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PDF Views:81
Authors
Affiliations
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN
Source
Current Science, Vol 121, No 8 (2021), Pagination: 1090-1098Abstract
India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton production compared to auto regressive integrated moving average and classical SVR modelsKeywords
ARIMA, cotton production forecasting, SVR, time series, tuned-SVR.Full Text
References
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- De Giorgi, M. G., Campilongo, S., Ficarella, A. and Congedo, P. M., Comparison between wind power rediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energy, 2014, 7, 5251–5272.
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- Gupta, U. and Gupta, D., An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function. Appl. Intell., 2019, 49(10), 3606–3627.
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- Pattern of crop diversification and its implications on undernutrition in India
Abstract Views :195 |
PDF Views:84
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 122, No 10 (2022), Pagination: 1154-1160Abstract
The present study explores the pattern and extent of food-crop diversification and its implications on nutritional indicators in India using district-level data for the most recent period. It relied on data from land-use statistics and the National Family Health Survey 2015–16. We estimated the Simpson index for food-crop diversification and undernutrition index for nutritional status. The association of crop diversification and nutritional status was analysed employing bivariate copula function. The findings show striking regional differences in the extent of food-crop diversification and nutritional outcomes. The results of the copula function indicate a significant inverse relationship between crop diversification and undernutritionKeywords
Bivariate copula, crop diversification, land use, nutritional status, undernutrition index.Full Text
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- Genetic Algorithms-Based Fuzzy Analytical Hierarchical Process (GA-FAHP) for Evaluating Biofortified Crop Promotion Strategies
Abstract Views :47 |
PDF Views:35
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Indian Council of Medical Research, New Delhi 110 029, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Indian Council of Medical Research, New Delhi 110 029, IN
Source
Current Science, Vol 125, No 3 (2023), Pagination: 317-320Abstract
In developing nations such as India, malnutrition is a major nutritional and health challenge. Biofortification has the potential to be an effective instrument in India’s attempts to combat malnutrition. Expert opinion must be used to evaluate the factors related to the promotion, distribution and adoption of biofortified crops. The analytical hierarchy process (AHP) is one of the most often employed decision-making methods. However, conventional AHP is incapable of identifying ambiguity in human judgements. Fuzzy AHP has already been devised to overcome this limitation. Fuzzy AHP necessitates information in pairwise comparisons, which is not always easy to gather. In this context, the Fuzzy AHP technique based on the genetic algorithm has been proposed, which can compute the priority weight without using a pairwise comparison matrix by directly dealing with expert-provided data. The proposed approach has been illustrated using the opinions of 1600 farmers from Odisha, India.Keywords
Biofortified Crops, Fuzzy AHP, Genetic Algorithm, Malnutrition.Full Text
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