{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:40:56Z","timestamp":1764175256244,"version":"3.37.3"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07616-4","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T16:49:18Z","timestamp":1659113358000},"page":"21367-21386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hybrid learning model for spatio-temporal forecasting of PM$$_{2.5}$$ using aerosol optical depth"],"prefix":"10.1007","volume":"34","author":[{"given":"Pritthijit","family":"Nath","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biparnak","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratik","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asif Iqbal","family":"Middya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7598-8266","authenticated-orcid":false,"given":"Sarbani","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"7616_CR1","unstructured":"Kennedy D, Bates RR, Watson AY, et\u00a0al. (1988) Air pollution, the automobile, and public health"},{"key":"7616_CR2","unstructured":"Paulos E, Anderson K, Townsend A (2004) Ubicomp in the urban frontier. Speech at the Sixth International Conference on Ubiquitous Computing Workshop"},{"key":"7616_CR3","unstructured":"World Health Organisation (2016) WHO Global Urban Ambient Air Pollution Database. URL https:\/\/www.who.int\/airpollution\/data\/cities-2016\/en\/. Accessed: 2021-06-01"},{"key":"7616_CR4","unstructured":"The Hindustan Times (2017) Delhi gets 18 more monitoring stations to keep tab on air quality. URL https:\/\/tinyurl.com\/3mya2zz3. Accessed: 2021-06-01"},{"issue":"1","key":"7616_CR5","first-page":"E69","volume":"8","author":"YF Xing","year":"2016","unstructured":"Xing YF, Xu YH, Shi MH, Lian YX (2016) The impact of pm2. 5 on the human respiratory system. J thoracic dis 8(1):E69","journal-title":"J thoracic dis"},{"key":"7616_CR6","unstructured":"World Health Organisation (2013) Health effects of Particulate Matter. URL https:\/\/www.euro.who.int\/__data\/assets\/pdf_file\/0006\/189051\/Health-effects-of-particulate-matter-final-Eng.pdf. Accessed: 2021-06-01"},{"key":"7616_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/ICEET.2009.468","volume":"3","author":"W Wang","year":"2009","unstructured":"Wang W, Guo Y (2009) Air pollution pm2.5 data analysis in los angeles long beach with seasonal arima model. 2009 Int Conf Energy and Environ Technol 3:7\u201310","journal-title":"2009 Int Conf Energy and Environ Technol"},{"key":"7616_CR8","doi-asserted-by":"crossref","unstructured":"Lei F, Dong X, Ma X (2020) Prediction of pm2. 5 concentration considering temporal and spatial features: A case study of fushun, liaoning province. Journal of Intelligent & Fuzzy Systems (Preprint), 1\u201311","DOI":"10.3233\/JIFS-201515"},{"issue":"10","key":"7616_CR9","doi-asserted-by":"publisher","first-page":"5111","DOI":"10.1021\/acs.est.5b06001","volume":"50","author":"M Wang","year":"2016","unstructured":"Wang M, Sampson PD, Hu J, Kleeman M, Keller JP, Olives C, Szpiro AA, Vedal S, Kaufman JD (2016) Combining land-use regression and chemical transport modeling in a spatiotemporal geostatistical model for ozone and pm2. 5. Environ sci technol 50(10):5111\u20135118","journal-title":"Environ sci technol"},{"issue":"2","key":"7616_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.apr.2016.09.001","volume":"8","author":"P Shao","year":"2017","unstructured":"Shao P, Xin J, An J, Kong L, Wang B, Wang J, Wang Y, Wu D (2017) The empirical relationship between pm2. 5 and aod in nanjing of the yangtze river delta. Atmos Pollut Res 8(2):233\u2013243","journal-title":"Atmos Pollut Res"},{"key":"7616_CR11","doi-asserted-by":"crossref","unstructured":"Bui TC, Kim J, Kang T, Lee D, Choi J, Yang I, Jung K, Cha SK (2020) Star: Spatio-temporal prediction of air quality using a multimodal approach","DOI":"10.1007\/978-3-030-55187-2_31"},{"key":"7616_CR12","unstructured":"He Z, Chow C, Zhang J (2020) Stnn: A spatio-temporal neural network for traffic predictions. IEEE Transactions on Intelligent Transportation Systems pp 1\u201310"},{"issue":"3","key":"7616_CR13","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1080\/13658816.2019.1664742","volume":"34","author":"Q Pu","year":"2020","unstructured":"Pu Q, Yoo EH (2020) Spatio-temporal modeling of pm2.5 concentrations with missing data problem: a case study in beijing, china. Int J Geogr Inf Sci 34(3):423\u2013447. https:\/\/doi.org\/10.1080\/13658816.2019.1664742","journal-title":"Int J Geogr Inf Sci"},{"key":"7616_CR14","doi-asserted-by":"publisher","unstructured":"Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y, Mickley LJ, Schwartz J (2019) An ensemble-based model of pm2.5 concentration across the contiguous united states with high spatiotemporal resolution. Environment International 130:104,909. https:\/\/doi.org\/10.1016\/j.envint.2019.104909. URL https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0160412019300650","DOI":"10.1016\/j.envint.2019.104909"},{"key":"7616_CR15","doi-asserted-by":"publisher","unstructured":"Stafoggia M, Bellander T, Bucci S, Davoli M, de Hoogh K, de\u2019 Donato F, Gariazzo C, Lyapustin A, Michelozzi P, Renzi M, Scortichini M, Shtein A, Viegi G, Kloog I, Schwartz J (2019) Estimation of daily pm10 and pm2.5 concentrations in italy, 2013\u20132015, using a spatiotemporal land-use random-forest model. Environment International 124:170\u2013179. https:\/\/doi.org\/10.1016\/j.envint.2019.01.016. URL www.sciencedirect.com\/science\/article\/pii\/S0160412018327685","DOI":"10.1016\/j.envint.2019.01.016"},{"issue":"1","key":"7616_CR16","doi-asserted-by":"publisher","first-page":"25","DOI":"10.4209\/aaqr.2017.12.0568","volume":"19","author":"RK Krishna","year":"2019","unstructured":"Krishna RK, Ghude SD, Kumar R, Beig G, Kulkarni R, Nivdange S, Chate D (2019) Surface PM2.5 estimate using satellite-derived aerosol optical depth over india. Aerosol and Air Quality Res 19(1):25\u201337. https:\/\/doi.org\/10.4209\/aaqr.2017.12.0568","journal-title":"Aerosol and Air Quality Res"},{"key":"7616_CR17","doi-asserted-by":"publisher","unstructured":"Wu Z, Wang Y, Zhang L (2019) Msstn: Multi-scale spatial temporal network for air pollution prediction. In: 2019 IEEE International Conference on Big Data (Big Data), pp 1547\u20131556. https:\/\/doi.org\/10.1109\/BigData47090.2019.9005574","DOI":"10.1109\/BigData47090.2019.9005574"},{"key":"7616_CR18","doi-asserted-by":"publisher","unstructured":"Lindstr\u00f6m J, Szpiro A, Sampson P, Sheppard L, Oron A, Richards M, Larson T (2011) A flexible spatio-temporal model for air pollution with spatio-temporal covariates. ISEE Conference Abstracts 2011. https:\/\/doi.org\/10.1289\/isee.2011.00165","DOI":"10.1289\/isee.2011.00165"},{"issue":"1","key":"7616_CR19","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TNNLS.2015.2411629","volume":"27","author":"SB Taieb","year":"2016","unstructured":"Taieb SB, Atiya AF (2016) A bias and variance analysis for multistep-ahead time series forecasting. IEEE Trans Neural Netw Learn Sys 27(1):62\u201376. https:\/\/doi.org\/10.1109\/TNNLS.2015.2411629","journal-title":"IEEE Trans Neural Netw Learn Sys"},{"issue":"12","key":"7616_CR20","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","volume":"26","author":"R Chandra","year":"2015","unstructured":"Chandra R (2015) Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction. IEEE Trans Neural Netw Learn Sys 26(12):3123\u20133136. https:\/\/doi.org\/10.1109\/TNNLS.2015.2404823","journal-title":"IEEE Trans Neural Netw Learn Sys"},{"issue":"6","key":"7616_CR21","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1109\/TNNLS.2018.2869131","volume":"30","author":"M Xu","year":"2019","unstructured":"Xu M, Yang Y, Han M, Qiu T, Lin H (2019) Spatio-temporal interpolated echo state network for meteorological series prediction. IEEE Trans Neural Netw Learn Sys 30(6):1621\u20131634","journal-title":"IEEE Trans Neural Netw Learn Sys"},{"key":"7616_CR22","doi-asserted-by":"publisher","first-page":"38,186","DOI":"10.1109\/ACCESS.2018.2849820","volume":"6","author":"P Soh","year":"2018","unstructured":"Soh P, Chang J, Huang J (2018) Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. IEEE Access 6:38,186-38,199","journal-title":"IEEE Access"},{"issue":"3","key":"7616_CR23","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/TBDATA.2017.2651898","volume":"3","author":"JY Zhu","year":"2017","unstructured":"Zhu JY, Sun C, Li VO (2017) An extended spatio-temporal granger causality model for air quality estimation with heterogeneous urban big data. IEEE Trans on Big Data 3(3):307\u2013319","journal-title":"IEEE Trans on Big Data"},{"key":"7616_CR24","doi-asserted-by":"crossref","unstructured":"Sahu SK, Gelfand AE, Holland DM (2006) Spatio-temporal modeling of fine particulate matter. Journal of Agricultural, Biological, and Environmental Statistics 11(1):61\u201386. URL http:\/\/www.jstor.org\/stable\/27595586","DOI":"10.1198\/108571106X95746"},{"key":"7616_CR25","doi-asserted-by":"publisher","unstructured":"Cesario E, Comito C, Talia D (2017) An approach for the discovery and validation of urban mobility patterns. Pervasive and Mobile Computing 42:77\u201392. https:\/\/doi.org\/10.1016\/j.pmcj.2017.09.006. URL www.sciencedirect.com\/science\/article\/pii\/S157411921630390X","DOI":"10.1016\/j.pmcj.2017.09.006"},{"key":"7616_CR26","doi-asserted-by":"publisher","unstructured":"Comito C (2020) Next: A framework for next-place prediction on location based social networks. Knowledge-Based Systems 204:106,205. https:\/\/doi.org\/10.1016\/j.knosys.2020.106205. URL www.sciencedirect.com\/science\/article\/pii\/S095070512030424X","DOI":"10.1016\/j.knosys.2020.106205"},{"key":"7616_CR27","doi-asserted-by":"publisher","unstructured":"Yang Q, Yuan Q, Yue L, Li T, Shen H, Zhang L (2019) The relationships between pm2.5 and aerosol optical depth (aod) in mainland china: About and behind the spatio-temporal variations. Environmental Pollution 248. https:\/\/doi.org\/10.1016\/j.envpol.2019.02.071","DOI":"10.1016\/j.envpol.2019.02.071"},{"key":"7616_CR28","doi-asserted-by":"publisher","unstructured":"Ni X, Cao C, Zhou Y, Cui X, P.\u00a0Singh R (2018) Spatio-temporal pattern estimation of pm2.5 in beijing-tianjin-hebei region based on modis aod and meteorological data using the back propagation neural network. Atmosphere 9(3). https:\/\/doi.org\/10.3390\/atmos9030105. URL https:\/\/www.mdpi.com\/2073-4433\/9\/3\/105","DOI":"10.3390\/atmos9030105"},{"issue":"6","key":"7616_CR29","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.apr.2017.04.002","volume":"8","author":"X Mao","year":"2017","unstructured":"Mao X, Shen T, Feng X (2017) Prediction of hourly ground-level pm2. 5 concentrations 3 days in advance using neural networks with satellite data in eastern china. Atmos Pollut Res 8(6):1005\u20131015","journal-title":"Atmos Pollut Res"},{"key":"7616_CR30","doi-asserted-by":"publisher","unstructured":"Kloog I, Chudnovsky AA, Just AC, Nordio F, Koutrakis P, Coull BA, Lyapustin A, Wang Y, Schwartz J (2014) A new hybrid spatio-temporal model for estimating daily multi-year pm2.5 concentrations across northeastern usa using high resolution aerosol optical depth data. Atmospheric Environment 95:581\u2013590. https:\/\/doi.org\/10.1016\/j.atmosenv.2014.07.014. URL www.sciencedirect.com\/science\/article\/pii\/S1352231014005354","DOI":"10.1016\/j.atmosenv.2014.07.014"},{"key":"7616_CR31","unstructured":"Rao R. Air quality data in india (2015 - 2020). URL https:\/\/www.kaggle.com\/rohanrao\/air-quality-data-in-india. Accessed: 2021-06-01"},{"key":"7616_CR32","unstructured":"Ministry of Environment, Forest and Climate Change. Central control room for air quality management. https:\/\/cpcb.nic.in\/. Accessed: 2021-06-01"},{"key":"7616_CR33","unstructured":"NASA. MODIS - Moderate Resolution Imaging Spectroradiometer. URL https:\/\/terra.nasa.gov\/about\/terra-instruments\/modis. Accessed: 2021-06-01"},{"key":"7616_CR34","unstructured":"NASA. LAADS DAAC. URL https:\/\/ladsweb.modaps.eosdis.nasa.gov\/. Accessed: 2021-06-01"},{"issue":"2","key":"7616_CR35","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/72.279188","volume":"5","author":"JT Connor","year":"1994","unstructured":"Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction. IEEE trans neural netw 5(2):240\u2013254","journal-title":"IEEE trans neural netw"},{"issue":"3","key":"7616_CR36","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1287\/mnsc.6.3.324","volume":"6","author":"PR Winters","year":"1960","unstructured":"Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6(3):324\u2013342. https:\/\/doi.org\/10.1287\/mnsc.6.3.324","journal-title":"Manag Sci"},{"issue":"6088","key":"7616_CR37","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. nature 323(6088):533\u2013536","journal-title":"nature"},{"issue":"6","key":"7616_CR38","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/TITS.2018.2867042","volume":"20","author":"X Ma","year":"2018","unstructured":"Ma X, Zhang J, Du B, Ding C, Sun L (2018) Parallel architecture of convolutional bi-directional lstm neural networks for network-wide metro ridership prediction. IEEE Trans Intell Transp Sys 20(6):2278\u20132288","journal-title":"IEEE Trans Intell Transp Sys"},{"issue":"1","key":"7616_CR39","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach learn 45(1):5\u201332","journal-title":"Mach learn"},{"issue":"3","key":"7616_CR40","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Statistics and comput 14(3):199\u2013222","journal-title":"Statistics and comput"},{"issue":"3","key":"7616_CR41","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","volume":"27","author":"JR Quinlan","year":"1987","unstructured":"Quinlan JR (1987) Simplifying decision trees. Int j man-mach stud 27(3):221\u2013234","journal-title":"Int j man-mach stud"},{"key":"7616_CR42","doi-asserted-by":"publisher","unstructured":"Griffith DA (2003) Spatial Autocorrelation and Spatial Filtering. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-24806-4","DOI":"10.1007\/978-3-540-24806-4"},{"key":"7616_CR43","unstructured":"Exploratory spatial data analysis (esda) and spatial autocorrelation. URL https:\/\/cran.r-project.org\/web\/packages\/lctools\/vignettes\/SpatialAutocorrelation.html. Accessed: 2021-06-01"},{"key":"7616_CR44","unstructured":"Goldberger AS (1964) Classical linear regression. Econometric theory pp 156\u2013212"},{"issue":"818","key":"7616_CR45","first-page":"518","volume":"131","author":"GT Walker","year":"1931","unstructured":"Walker GT (1931) On periodicity in series of related terms. Proc R Soc London. Series A, Containing Papers of a Math Phys Character 131(818):518\u2013532","journal-title":"Proc R Soc London. Series A, Containing Papers of a Math Phys Character"},{"key":"7616_CR46","doi-asserted-by":"publisher","unstructured":"Chang CC, Lin CJ (2011) Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3). https:\/\/doi.org\/10.1145\/1961189.1961199","DOI":"10.1145\/1961189.1961199"},{"key":"7616_CR47","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC press"},{"issue":"8","key":"7616_CR48","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"issue":"10","key":"7616_CR49","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2017) Lstm: A search space odyssey. IEEE Trans Neural Netw Learn Sys 28(10):2222\u20132232. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924","journal-title":"IEEE Trans Neural Netw Learn Sys"},{"issue":"11","key":"7616_CR50","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681","journal-title":"IEEE Trans Signal Process"},{"key":"7616_CR51","volume-title":"Python tutorial","author":"G Van Rossum","year":"1995","unstructured":"Van Rossum G, Drake FL Jr (1995) Python tutorial. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands"},{"key":"7616_CR52","unstructured":"Mart\u00edn\u00a0Abadi et al. (2015) Tensorflow:large-scale machine learning on heterogeneous systems"},{"key":"7616_CR53","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J (2010) statsmodels: Econometric and statistical modeling with python","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"7616_CR54","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"19","key":"7616_CR55","doi-asserted-by":"publisher","first-page":"12551","DOI":"10.1007\/s00521-021-05901-2","volume":"33","author":"P Nath","year":"2021","unstructured":"Nath P, Saha P, Middya AI, Roy S (2021) Long-term time-series pollution forecast using statistical and deep learning methods. Neural Comput Appl 33(19):12551\u201312570. https:\/\/doi.org\/10.1007\/s00521-021-05901-2","journal-title":"Neural Comput Appl"},{"key":"7616_CR56","unstructured":"Mean squared logarithmic error (msle): Peltarion platform. URL https:\/\/peltarion.com\/knowledge-center\/documentation\/modeling-view\/build-an-ai-model\/loss-functions\/mean-squared-logarithmic-error-(msle). Accessed: 2021-06-01"},{"key":"7616_CR57","doi-asserted-by":"publisher","unstructured":"Sammut C, Webb GI (eds) (2010) Mean Absolute Error, pp 652\u2013652. Springer US, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_525","DOI":"10.1007\/978-0-387-30164-8_525"},{"key":"7616_CR58","unstructured":"Herald D (2020) How bad is bengaluru air?. URL https:\/\/www.deccanherald.com\/metrolife\/metrolife-your-bond-with-bengaluru\/how-bad-is-bengaluru-air-909370.html. Accessed: 2021-06-01"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07616-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07616-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07616-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T23:34:52Z","timestamp":1667864092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07616-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":58,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7616"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07616-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,7,29]]},"assertion":[{"value":"2 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2022","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}