{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T03:42:51Z","timestamp":1770954171511,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s42001-025-00450-3","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:34:57Z","timestamp":1767846897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of ANNs for predicting poverty levels with interval estimation: a case study of Odisha, India"],"prefix":"10.1007","volume":"9","author":[{"given":"Sandeep","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4857-644X","authenticated-orcid":false,"given":"S.","family":"Chakraverty","sequence":"additional","affiliation":[]},{"given":"Narayan","family":"Sethi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"450_CR1","unstructured":"Government of India. National multidimensional poverty index baseline report based on nfhs-4. NITI Aayog (2021). https:\/\/www.niti.gov.in\/sites\/default\/files\/2021-11\/National_MPI_India-11242021.pdf. New Delhi, India."},{"key":"450_CR2","unstructured":"Rajan, R., Pandey, T.\u00a0K., Jayal, N.\u00a0G., Ramaswami, B. & Gupta, S. (2013). Report of the committee for evolving a composite development index of states. Ministry of Finance, Govt. of India https:\/\/www.im4change.org\/docs\/203Raghuram%20Rajan.pdf"},{"key":"450_CR3","unstructured":"Government of Odisha. Odisha economic survey 2014-15. Planning and Convergence Department (2015). https:\/\/pc.odisha.gov.in\/sites\/default\/files\/2020-03\/Economic_Survey_2014-15.pdf. Bhubaneswar, Odisha."},{"key":"450_CR4","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1016\/S0305-750X(01)00072-9","volume":"29","author":"M Ravallion","year":"2001","unstructured":"Ravallion, M. (2001). Growth, inequality and poverty: Looking beyond averages. World development, 29, 1803\u20131815.","journal-title":"World development"},{"key":"450_CR5","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/A:1020139631000","volume":"7","author":"D Dollar","year":"2002","unstructured":"Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of economic growth, 7, 195\u2013225.","journal-title":"Journal of economic growth"},{"key":"450_CR6","unstructured":"Dvorak, J. (2015). in European union definition of poverty (ed.Odekon, M.) The SAGE Encyclopedia of World Poverty (SAGE Publications, Inc, Skidmore College, USA)"},{"key":"450_CR7","volume-title":"Poverty: A study of town life","author":"BS Rowntree","year":"1902","unstructured":"Rowntree, B. S. (1902). Poverty: A study of town life. London: Macmillan and Co."},{"key":"450_CR8","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199689491.001.0001","volume-title":"Multidimensional poverty measurement and analysis","author":"S Alkire","year":"2015","unstructured":"Alkire, S., et al. (2015). Multidimensional poverty measurement and analysis. USA: Oxford University Press."},{"key":"450_CR9","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.jpubeco.2010.11.006","volume":"95","author":"S Alkire","year":"2011","unstructured":"Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of public economics, 95, 476\u2013487.","journal-title":"Journal of public economics"},{"key":"450_CR10","doi-asserted-by":"crossref","unstructured":"Sahoo, A.\u00a0K. & Chakraverty, S. (2022). Curriculum learning-based artificial neural network model for solving differential equations. Soft Computing in Interdisciplinary Sciences 129\u2013145","DOI":"10.1007\/978-981-16-4713-0_6"},{"key":"450_CR11","volume-title":"Introduction to artificial neural systems","author":"J Zurada","year":"1992","unstructured":"Zurada, J. (1992). Introduction to artificial neural systems. St. Paul: West Publishing Co."},{"key":"450_CR12","volume-title":"Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications","author":"S Chakraverty","year":"2025","unstructured":"Chakraverty, S., Sahoo, A. K., & Mohapatra, D. (2025). Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications. Cambridge: Elsevier."},{"key":"450_CR13","first-page":"533","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323, 533\u2013536.","journal-title":"Learning representations by back-propagating errors. nature"},{"key":"450_CR14","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/S0161-8938(02)00108-4","volume":"24","author":"D Bigman","year":"2002","unstructured":"Bigman, D., & Srinivasan, P. (2002). Geographical targeting of poverty alleviation programs: methodology and applications in rural india. Journal of Policy Modeling, 24, 237\u2013255.","journal-title":"Journal of Policy Modeling"},{"key":"450_CR15","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.apgeog.2009.05.001","volume":"30","author":"O Erenstein","year":"2010","unstructured":"Erenstein, O., Hellin, J., & Chandna, P. (2010). Poverty mapping based on livelihood assets: A meso-level application in the indo-gangetic plains, india. Applied Geography, 30, 112\u2013125.","journal-title":"Applied Geography"},{"key":"450_CR16","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D. & Ermon, S. (2016). Transfer learning from deep features for remote sensing and poverty mapping, Vol.\u00a030","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"450_CR17","unstructured":"Kshirsagar, V., Wieczorek, J., Ramanathan, S. & Wells, R. (2017). Household poverty classification in data-scarce environments: A machine learning approach. arXiv:1711.06813"},{"key":"450_CR18","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1007\/s00521-017-2889-8","volume":"30","author":"A Azcarraga","year":"2018","unstructured":"Azcarraga, A., & Setiono, R. (2018). Neural network rule extraction for gaining insight into the characteristics of poverty. Neural Computing and Applications, 30, 2795\u20132806.","journal-title":"Neural Computing and Applications"},{"key":"450_CR19","first-page":"481","volume":"37","author":"H Wang","year":"2019","unstructured":"Wang, H., & Islam, K. (2019). An optimization model for poverty alleviation fund audit mode based on bp neural network. Journal of Intelligent & Fuzzy Systems, 37, 481\u2013491.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"450_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-14108-y","volume":"11","author":"R Vinuesa","year":"2020","unstructured":"Vinuesa, R., et al. (2020). The role of artificial intelligence in achieving the sustainable development goals. Nature communications, 11, 1\u201310.","journal-title":"Nature communications"},{"key":"450_CR21","first-page":"11","volume":"20","author":"J Manajemen","year":"2020","unstructured":"Manajemen, J. (2020). Poerwanto, B. & Fajriani, F. Resilient backpropagation neural network on prediction of poverty levels in south sulawesi. MATRIK. Teknik Informatika dan Rekayasa Komputer, 20, 11\u201318.","journal-title":"Teknik Informatika dan Rekayasa Komputer"},{"key":"450_CR22","doi-asserted-by":"publisher","first-page":"447","DOI":"10.17576\/jsm-2020-4902-24","volume":"49","author":"A Abu","year":"2020","unstructured":"Abu, A., Hamdan, R., & Sani, N. (2020). Ensemble learning for multidimensional poverty classification. Sains Malaysiana, 49, 447\u2013459.","journal-title":"Sains Malaysiana"},{"key":"450_CR23","doi-asserted-by":"crossref","unstructured":"Wanto, A. & Hardinata, J.\u00a0T. (2020). Estimations of indonesian poor people as poverty reduction efforts facing industrial revolution 4.0, Vol. 725, 012114 (IOP Publishing)","DOI":"10.1088\/1757-899X\/725\/1\/012114"},{"key":"450_CR24","doi-asserted-by":"publisher","first-page":"5788","DOI":"10.3390\/su13115788","volume":"13","author":"D Mhlanga","year":"2021","unstructured":"Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13, 5788.","journal-title":"Sustainability"},{"key":"450_CR25","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.jbusres.2020.10.035","volume":"131","author":"MB Ferreira","year":"2021","unstructured":"Ferreira, M. B., et al. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411\u2013425.","journal-title":"Journal of Business Research"},{"key":"450_CR26","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.3390\/su13031412","volume":"13","author":"A Alsharkawi","year":"2021","unstructured":"Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., & Alyaman, M. (2021). Poverty classification using machine learning: The case of jordan. Sustainability, 13, 1412.","journal-title":"Sustainability"},{"key":"450_CR27","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.jrurstud.2019.01.008","volume":"93","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., & Liu, Y. (2022). The geography of poverty: Review and research prospects. Journal of Rural Studies, 93, 408\u2013416.","journal-title":"Journal of Rural Studies"},{"key":"450_CR28","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3390\/ijgi11010050","volume":"11","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., Chen, N., & Lin, S. (2022). A poverty measurement method incorporating spatial correlation: A case study in yangtze river economic belt, china. ISPRS International Journal of Geo-Information, 11, 50.","journal-title":"ISPRS International Journal of Geo-Information"},{"key":"450_CR29","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1126\/science.aac4420","volume":"350","author":"J Blumenstock","year":"2015","unstructured":"Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350, 1073\u20131076.","journal-title":"Science"},{"key":"450_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdeveco.2022.103016","volume":"161","author":"EL Aiken","year":"2023","unstructured":"Aiken, E. L., Bedoya, G., Blumenstock, J. E., & Coville, A. (2023). Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in afghanistan. Journal of Development Economics, 161, Article 103016.","journal-title":"Journal of Development Economics"},{"key":"450_CR31","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1126\/science.aaf7894","volume":"353","author":"N Jean","year":"2016","unstructured":"Jean, N., et al. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353, 790\u2013794.","journal-title":"Science"},{"key":"450_CR32","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1007\/s00181-022-02199-4","volume":"63","author":"A Garbero","year":"2022","unstructured":"Garbero, A., & Letta, M. (2022). Predicting household resilience with machine learning: preliminary cross-country tests. Empirical Economics, 63, 2057\u20132070.","journal-title":"Empirical Economics"},{"key":"450_CR33","first-page":"531","volume":"32","author":"L McBride","year":"2018","unstructured":"McBride, L., & Nichols, A. (2018). Retooling poverty targeting using out-of-sample validation and machine learning. The World Bank Economic Review, 32, 531\u2013550.","journal-title":"The World Bank Economic Review"},{"key":"450_CR34","doi-asserted-by":"publisher","first-page":"20160690","DOI":"10.1098\/rsif.2016.0690","volume":"14","author":"JE Steele","year":"2017","unstructured":"Steele, J. E., et al. (2017). Mapping poverty using mobile phone and satellite data. Journal of The Royal Society Interface, 14, 20160690.","journal-title":"Journal of The Royal Society Interface"},{"key":"450_CR35","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.foodpol.2019.03.001","volume":"84","author":"M Hossain","year":"2019","unstructured":"Hossain, M., Mullally, C., & Asadullah, M. N. (2019). Alternatives to calorie-based indicators of food security: An application of machine learning methods. Food policy, 84, 77\u201391.","journal-title":"Food policy"},{"key":"450_CR36","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1038\/s41586-022-04484-9","volume":"603","author":"E Aiken","year":"2022","unstructured":"Aiken, E., Bellue, S., Karlan, D., Udry, C., & Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603, 864\u2013870.","journal-title":"Nature"},{"key":"450_CR37","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.worlddev.2019.06.008","volume":"122","author":"EC Lentz","year":"2019","unstructured":"Lentz, E. C., Michelson, H., Baylis, K., & Zhou, Y. (2019). A data-driven approach improves food insecurity crisis prediction. World Development, 122, 399\u2013409.","journal-title":"World Development"},{"key":"450_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.worlddev.2019.04.010","volume":"121","author":"E Knippenberg","year":"2019","unstructured":"Knippenberg, E., Jensen, N., & Constas, M. (2019). Quantifying household resilience with high frequency data: Temporal dynamics and methodological options. World Development, 121, 1\u201315.","journal-title":"World Development"},{"key":"450_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255519","volume":"16","author":"C Browne","year":"2021","unstructured":"Browne, C., et al. (2021). Multivariate random forest prediction of poverty and malnutrition prevalence. PloS one, 16, Article e0255519.","journal-title":"PloS one"},{"key":"450_CR40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0283938","volume":"18","author":"FS Agyemang","year":"2023","unstructured":"Agyemang, F. S., Memon, R., Wolf, L. J., & Fox, S. (2023). High-resolution rural poverty mapping in pakistan with ensemble deep learning. PLoS One, 18, Article e0283938.","journal-title":"PLoS One"},{"key":"450_CR41","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s42001-025-00405-8","volume":"8","author":"S Mariyah","year":"2025","unstructured":"Mariyah, S., & Wobcke, W. (2025). Evaluating area-level features for proxy means test models: Evidence from rural, semi-urban and urban districts in poverty targeting. Journal of Computational Social Science, 8, 74.","journal-title":"Journal of Computational Social Science"},{"issue":"3","key":"450_CR42","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s42001-025-00384-w","volume":"8","author":"M Gupta","year":"2025","unstructured":"Gupta, M., Alpana, G. P., & Varshney, N. A. (2025). comparative study of automated undergraduate engineering admission prediction in an Indian university using machine learning. Journal of Computational Social Science, 8(3), 58.","journal-title":"Journal of Computational Social Science"},{"key":"450_CR43","unstructured":"Sahoo, A.\u00a0K. & Klein, I. (2025). Morpi-pinn: A physics-informed framework for mobile robot pure inertial navigation. arXiv:2507.18206."},{"key":"450_CR44","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1080\/19331681.2022.2097974","volume":"20","author":"ASM Alavi","year":"2023","unstructured":"Alavi, A., & S. M., Ebadati E, O. M., Alavi A, S. M. & Firoozan Sarnaghi, T. (2023). Determination of households benefits from subsidies by using data mining approaches. Journal of Information Technology & Politics, 20, 303\u2013322.","journal-title":"Journal of Information Technology & Politics,"},{"key":"450_CR45","volume-title":"in Comparative study of chebyshev and legendre polynomial-based neural models for approximating multidimensional poverty for an indian state 2053\u20132563","author":"S Kumar","year":"2022","unstructured":"Kumar, S., Sahoo, A. K., & Chakraverty, S. (2022). in Comparative study of chebyshev and legendre polynomial-based neural models for approximating multidimensional poverty for an indian state 2053\u20132563. Bristol, UK: IOP Publishing."},{"issue":"1","key":"450_CR46","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s11205-023-03181-y","volume":"169","author":"S Kumar","year":"2023","unstructured":"Kumar, S., Chakraverty, S., & Sethi, N. (2023). Multidimensional poverty: Cmpi development, spatial analysis and clustering. Social Indicators Research, 169(1), 647\u2013670. 1\u201324.","journal-title":"Social Indicators Research"},{"key":"450_CR47","doi-asserted-by":"publisher","first-page":"113347","DOI":"10.1016\/j.asoc.2025.113347","volume":"180","author":"S Kumar","year":"2025","unstructured":"Kumar, S., Chakraverty, S., & Sethi, N. (2025). A comparative study of center-radius and lower-upper type interval neural network methods in uncertainty modeling. Applied Soft Computing, 180, 113347.","journal-title":"Applied Soft Computing"},{"key":"450_CR48","unstructured":"GISdivision NIC Bhubneswar. District map odisha. https:\/\/gisodisha.nic.in\/Statem\/District.pdf Last accessed 30 Auguest 2023."},{"key":"450_CR49","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","volume":"39","author":"D Svozil","year":"1997","unstructured":"Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and intelligent laboratory systems, 39, 43\u201362.","journal-title":"Chemometrics and intelligent laboratory systems"},{"key":"450_CR50","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.neunet.2019.07.005","volume":"118","author":"J Sadeghi","year":"2019","unstructured":"Sadeghi, J., De Angelis, M., & Patelli, E. (2019). Efficient training of interval neural networks for imprecise training data. Neural Networks, 118, 338\u2013351.","journal-title":"Neural Networks"},{"key":"450_CR51","doi-asserted-by":"crossref","unstructured":"Campi, M.\u00a0C., Garatti, S. & Ramponi, F.\u00a0A. (2015). Non-convex scenario optimization with application to system identification, 4023\u20134028 (IEEE)","DOI":"10.1109\/CDC.2015.7402845"},{"key":"450_CR52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-025-00450-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42001-025-00450-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-025-00450-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T02:25:42Z","timestamp":1770949542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42001-025-00450-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,8]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["450"],"URL":"https:\/\/doi.org\/10.1007\/s42001-025-00450-3","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,8]]},"assertion":[{"value":"5 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"19"}}