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Hunger and poverty: Eliciting the facts through artificial neural network

Abstract

India is a country of agrarian economy. Nearly 70 per cent of its population is directly or indirectly depends on agriculture. Having 2/3rd of its population in villages, India’s economy and its problems is largely affected by its people residing in villages. The villages of India are quite wholesome in nature and it has a proper comprehensive ecosystem comprising of farm and off farm sector. Here in this study the farm locality is Beraberi gram panchayat, Habra II Block, North 24 pgs and the non-farm locality is Bilkanda gram panchayat, Barrackpur 2 block, North 24 pgs. 13 independent variables were studied against 4 dependent variables Hunger, Poverty, Voice and Cognitive Differentials. The respondents were women under the age group of 15 to 60. Total 150 respondents were selected. The statistical tool used in this study was Artificial Neural Network Analysis (ANN). The study revealed that communication variables, homestead land, stress perception and energy consumption, these were the parameters which were the most significant as far as the dependents variables were concerned.

Agricultural Economics and PracticesArtificial Intelligence and Decision Support SystemsPovertyAgrarian societyAgricultureGeographySocioeconomicsPopulationLocalityEconomic growthEconomicsSociology
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0.00
field-weighted impact
References
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References
The application of artificial neural networks to the analysis of remotely sensed data
International Journal of Remote Sensing · 2007 · 611 citations
Introduction Neural networks in remote sensing
International Journal of Remote Sensing · 1997 · 1,018 citations
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