Extreme learning machines for weather-based modelling of silk cocoon production

Autor: PRAMIT PANDIT, BISHVAJIT BAKSHI, SHILPA M.

Publikováno v: Journal of Agrometeorology, Vol 24, Iss 1 (2022)

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the lin

Externí odkaz: https://doaj.org/article/9d5f47397e3d4cf69788ec16387b5efe

​​ARIMA-Genetic Algorithm Approach for Forecasting Milk Production in India

Autor: Pramit Pandit, Bishvajit Bakshi, Moumita Paul, B.S. Pooja

Publikováno v: Asian Journal of Dairy and Food Research.

Background: Dairying in India has witnessed a radical transformation from a largely unorganised activity into a thriving organised industry. However, there are only a limited number of earlier attempts that specifically evaluated the milk production

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b413266df8f38e1aa7078c5b6882ed19
https://doi.org/10.18805/ajdfr.dr-1782

Contributors

Autor: Biswaranjan Acharya, Charles Oluwaseun Adetunji, Osikemekha Anthony Anani, Tadesse Hailu Ayane, Bishvajit Bakshi, Prasanta Kumar Barik, Ricardo A. Barrera-Cámara, Sitanath Biswas, Suparna Biswas, Israel Campero-Jurado, Monojit Chakraborty, Sumanta Chakraborty, Pushpa Choudhary, Chandreyee Chowdhury, Nivedita Das, Satya Ranjan Dash, Sujata Dash, Bhupesh Deka, Ocotlán Díaz-Parra, Moxa Doshi, Alejandro Fuentes-Penna, Kyvalya Garikapati, Demissie Jobir Gelmecha, Sourav K. Giri, Nikita Goel, Abhishek Gupta, Shavika Gupta, Yasha Hasija, Daniel Ingo Hefft, José Alberto Hernández-Aguilar, Arush Jain, Shweta Katkar, Sugamya Katta, Sumit Kaur, Farjad Khan, K.N. Krishnamurthy, Yogesh Kumar, Ujjwal Maulik, Josue Roman Mireles, Josue Roman Mireles (Martinez), Krishna Kumar Mishra, Arup Kumar Mukherjee, Reshmi S. Nair, Samaleswari Prasad Nayak, W. Nwankwo, Alberto Ochoa-Zezzatti, Akinola Samson Olayinka, Olaniyan T. Olugbemi, Manjusha Pandey, P.K. Pandey, Preeti Pandey, Pramit Pandit, Jyotiprakash Panigrahi, Priyanka Pattnaik, S. Premkumar, Satyananda Champati Rai, Sangita Ramatenki, Julio C. Ramos-Fernández, Siddharth Swarup Rautaray, Jazmin Rodriguez-Flores, Kailash Rout, Chandrima Roy, Priya Roy, Reek Roy, Miguel A. Ruiz-Jaimes, Jorge A. Ruiz-Vanoye, Himadri Nath Saha, Biswajit Sahoo, Sohail Saif, Sarita Samal, Harika Sammeta, Chiranmay Sarkar, Vinod Kumar Shukla, A.N. Sigappi, Arun Kumar Singh, Jitendra Singh, K. Rupabanta Singh, Ram Sewak Singh, Devendra Kumar Sinha, Yadira Toledo-Navarro, Francisco R. Trejo-Macotela, Ashish Tripathi, Kingsley Eghonghon Ukhurebor, C. Umezuruike, Madhavi Vaidya, Akson Varghese, Prem Chand Vashist, Nwankwo Wilson, Anuradha Yarlagadda

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f748ce3ef0e63fd45a60e735ce0e22e5
https://doi.org/10.1016/b978-0-12-823694-9.09992-8

FORECASTING OF AREA AND PRODUCTION OF CASHEW NUT IN DAKSHINA KANNADA USING ARIMA AND EXPONENTIAL SMOOTHING MODELS

Autor: Bishvajit Bakshi, Pramit Pandit, M Chaithra

Publikováno v: Journal of Reliability and Statistical Studies. :61-76

The cultivation and marketing of cashew nut involve a considerable amount of work force. Hence, it plays a vital role in the Indian economic scenario. In this context, an attempt has been made to forecast the area and production of cashew nut with a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f5757da9dbed9add4f7067ebd0fa1bef
https://doi.org/10.13052/jrss2229-5666.1226

Improved Estimation in Logistic Regression Through Quadratic Bootstrap Approach: An Application in Indian Agricultural E-learning System During COVID-19 Pandemic

Autor: K. N. Krishnamurthy, Bishvajit Bakshi, Pramit Pandit

Publikováno v: Algorithms for Intelligent Systems ISBN: 9789811615733

Logistic regression model is so far the most widely used model for analysis of binary response data. The usual logistic regression, by considering two distinct responses as two groups, can be reformulated as a classification technique in the line of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f448765724ce2db1b27baf6e52957a3a
https://doi.org/10.1007/978-981-16-1574-0_10