Refine your search
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Saha, Amit
- Ethnomedicinal Plants Used by Tribals of Raniganj Coalfield Area of West Bengal
Abstract Views :253 |
PDF Views:0
Authors
Affiliations
1 Department of Botany and Microbiology, Banwarilal Bhalotia College, Asansol Burdwan District, West Bangal-713303., IN
1 Department of Botany and Microbiology, Banwarilal Bhalotia College, Asansol Burdwan District, West Bangal-713303., IN
Source
Indian Forester, Vol 140, No 11 (2014), Pagination: 1118-1122Abstract
An ethnobotanical survey was carried out in Raniganj coalfield area of Burdwan district, West Bengal during the year 2010-2011. The investigations revealed that the region sustains a very rich medicinal plant wealth. The uses of plants, plant parts have been discussed. The study revealed that some plant species show preferential use in the tribal medicine in the study area.Keywords
Ethnobotanical, Raniganj Coalfield Area, Burdwan, Preferential Use, Tribal Medicine.
- Recognizing Bangla Grammar Using Predictive Parser
Abstract Views :200 |
PDF Views:157
Authors
Affiliations
1 Department of Computer Science and Engineering (CSE), Khulna University of Engineering and Technology (KUET), Khulna-9203, BD
1 Department of Computer Science and Engineering (CSE), Khulna University of Engineering and Technology (KUET), Khulna-9203, BD
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 3, No 6 (2011), Pagination: 61-73Abstract
We describe a Context Free Grammar (CFG) for Bangla language and hence we propose a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The proposed parser is a predictive parser and we construct the parse table for recognizing Bangla grammar. Using the parse table we recognize syntactical mistakes of Bangla sentences when there is no entry for a terminal in the parse table. If a natural language can be successfully parsed then grammar checking from this language becomes possible. The proposed scheme is based on Top down parsing method and we have avoided the left recursion of the CFG using the idea of left factoring.Keywords
Context Free Grammar, Predictive Parser, Bangla Language processing, Parse Table, Top down and Bottom up Parser, Left Recursion.Full Text
- Modelling and forecasting cotton production using tuned-support vector regression
Abstract Views :212 |
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
- Box, G. E. P. and Jenkins, G., Time series analysis, forecasting and control. Holden-Day, San Francisco, CA, 1970.
- Ariyo, A. A., Adewumi, A. O. and Ayo, C. K., Stock price prediction using the ARIMA model. In UK Sim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 2014, pp. 106–112.
- Badmus, M. A. and Ariyo, O. S., Forecasting cultivated areas and production of maize in Nigerian using ARIMA Model. Asian J. Agric. Sci., 2011, 3(3), 171–176.
- Bari, S. H., Rahman, M. T., Hussain, M. M. and Ray, S., Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil Environ. Res., 2015, 7(1), 69–77.
- Suresh, K. K. and Priya, S. K., Forecasting sugarcane yield of Tamil Nadu using ARIMA models. Sugar Tech., 2011, 13(1), 23–26.
- Padhan, P. C., Application of ARIMA model for forecasting agricultural productivity in India. J. Agric. Soc. Sci., 2012, 8(2), 50– 56.
- Prabakaran, K. and Sivapragasam, C., Forecasting areas and production of rice in India using ARIMA model. Int. J. Farm Sci., 2014, 4(1), 99–106.
- Sarika, Iquebal, M. A. and Chattopadhyay, C., Modelling and forecasting of pigeonpea (Cajanuscajan) production using autoregressive integrated moving average methodology. Indian J. Agric. Sci., 2011, 81(6), 520–523.
- Cortes, C. and Vapnik, V., Support-vector network. Mach. Learn., 1995, 20, 1–25.
- Vapnik, V., Golowich, S. and Smola, A., Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems (eds Mozer, M., Jordan, M. and Petsche, T.), MIT Press, Cambridge, USA, 1997, vol. 9, pp. 281–287.
- Mattera, D. and Haykin, S., Support vector machines for dynamic reconstruction of achaotic system. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B. et al.), MIT Press, Cambridge, USA, 1999, pp. 211–242.
- Muller, K. R., Smola, A., R¨atsch, G., Schölkopf, B., Kohlmorgen, J. and Vapnik, V., Predicting time series with support vector machines. In Artificial Neural Networks (eds Gerstner, W. et al.), ICANN 1997, Lecture Notes in Computer Science, Springer, Berlin, Germany, 1997, vol. 1327, pp. 999–1004.
- Niu, D., Wang, Y. and Wu, D. D., Power load forecasting using support vector machine and ant colony optimization. Exp. Syst. Appl., 2010, 37, 2531–2539.
- Saha, A., Singh, K. N., Ray, M. and Rathod, S., A hybrid spatiotemporal modelling: an application to space-time rainfall forecasting. Theor. Appl. Climatol., 2020, 142, 1271–1282.
- Saha, A. and Bhattacharyya, S., Artificial insemination for milk production in India: a statistical insight. Indian J. Anim. Sci., 2021, 90, 1186–1190.
- Stitson, M., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C. and Weston, J., Support vector regression with ANOVA decomposition kernels. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B., Burges, C. J. C. and Smola, A. J.), MIT Press, Cambridge, USA, 1999, pp. 285–292.
- Ortiz-Garcia, E. G., Salcedo-Sanz, S. and Casanova-Mateom, C., Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos. Res., 2014, 139, 128–136.
- Kumar, T. L. M. and Prajneshu, Development of hybrid models for forecasting time-series data using nonlinear SVR enhanced by PSO. J. Stat. Theory Prac., 2015, 9(4), 699–711.
- Rathod, S., Singh, K. N., Patil, S. G., Naik, R. H., Ray, M. and Meena, V. S., Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian J. Agric. Sci., 2018, 88(1), 22–27.
- 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.
- Balasundaram, S. and Gupta, D., Lagrangian support vector regression via unconstrained convex minimization. Neural Networks, 2014, 51, 67–79.
- Balasundaram, S. and Gupta, D., On implicit Lagrangian twin support vector regression by Newton method. Int. J. Comput. Intel. Syst., 2014, 7(1), 50–64.
- Balasundaram, S. and Gupta, D., Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowl.-Based Syst., 2014, 59, 85–96.
- Balasundaram, S. and Gupta, D., On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int. J. Mach. Learn. Cybernet., 2016, 7(5), 707–728.
- Gupta, D., Richhariya, B. and Borah, P., A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural. Comput. Appl., 2019, 31(11), 7153–7164.
- 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.
- Hou, Q., Zhang, J., Liu, L., Wang, Y. and Jing, L., Discriminative information-based nonparallel support vector machine. Signal. Process., 2019, 162, 169–179.
- Meyer, D. et al., Package ‘e1071’. The R Journal, 2019.