{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:36:48Z","timestamp":1775666208044,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-021-10913-0","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T06:04:30Z","timestamp":1619589870000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":144,"title":["RNN \/ LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting"],"prefix":"10.1007","author":[{"given":"Kiran Kumar","family":"Chandriah","sequence":"first","affiliation":[]},{"given":"Raghavendra V.","family":"Naraganahalli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"issue":"1","key":"10913_CR1","doi-asserted-by":"publisher","first-page":"15221","DOI":"10.1016\/j.ifacol.2017.08.2371","volume":"50","author":"KN Amirkolaii","year":"2017","unstructured":"Amirkolaii KN, Baboli A, Shahzad MK, Tonadre R (2017) Demand forecasting for irregular demands in business aircraft spare parts supply chains by using artificial intelligence (AI). IFAC-PapersOnLine 50(1):15221\u201315226","journal-title":"IFAC-PapersOnLine"},{"issue":"7","key":"10913_CR2","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1007\/s00521-015-2110-x","volume":"28","author":"OA Arqub","year":"2017","unstructured":"Arqub OA (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm\u2013Volterraintegrodifferential equations. Neural Comput & Applic 28(7):1591\u20131610","journal-title":"Neural Comput & Applic"},{"key":"10913_CR3","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.ins.2014.03.128","volume":"279","author":"OA Arqub","year":"2014","unstructured":"Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396\u2013415","journal-title":"Inf Sci"},{"issue":"8","key":"10913_CR4","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1007\/s00500-015-1707-4","volume":"20","author":"OA Arqub","year":"2016","unstructured":"Arqub OA, Mohammed AS, Momani S, Hayat T (2016 Aug 1) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20(8):3283\u20133302","journal-title":"Soft Comput"},{"key":"10913_CR5","doi-asserted-by":"crossref","unstructured":"Ashik AM, Kannan KS (2019) Time series model for stock price forecasting in India. In: Logistics, Supply Chain and Financial Predictive Analytics (pp. 221\u2013231). Singapore: Springer","DOI":"10.1007\/978-981-13-0872-7_17"},{"key":"10913_CR6","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.ijpe.2014.08.019","volume":"157","author":"MZ Babai","year":"2014","unstructured":"Babai MZ, Syntetos A, Teunter R (2014) Intermittent demand forecasting: an empirical study on accuracy and the risk of obsolescence. Int J Prod Econ 157:212\u2013219","journal-title":"Int J Prod Econ"},{"key":"10913_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ijpe.2018.01.026","volume":"209","author":"MZ Babai","year":"2019","unstructured":"Babai MZ, Dallery Y, Boubaker S, Kalai R (2019) A new method to forecast intermittent demand in the presence of inventory obsolescence. Int J Prod Econ 209:30\u201341","journal-title":"Int J Prod Econ"},{"key":"10913_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.omega.2017.09.009","volume":"81","author":"F Costantino","year":"2018","unstructured":"Costantino F, Di Gravio G, Patriarca R, Petrella L (2018) Spare parts management for irregular demand items. Omega 81:57\u201366","journal-title":"Omega"},{"key":"10913_CR9","doi-asserted-by":"crossref","unstructured":"Diaz DAB, Hennequin S, Roy D (2019) Spare parts Management in the Automotive Industry Considering Sustainability. In: World Congress on Global Optimization (pp. 1109-1118). Cham: Springer","DOI":"10.1007\/978-3-030-21803-4_109"},{"key":"10913_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijpe.2018.04.015","volume":"201","author":"J Dombi","year":"2018","unstructured":"Dombi J, J\u00f3n\u00e1s T, T\u00f3th ZE (2018) Modeling and long-term forecasting demand in spare parts logistics businesses. Int J Prod Econ 201:1\u201317","journal-title":"Int J Prod Econ"},{"key":"10913_CR11","doi-asserted-by":"crossref","unstructured":"Fu W, Chien CF, Lin ZH (2018) A hybrid forecasting framework with neural network and time-series method for intermittent demand in semiconductor supply chain. In: IFIP International Conference on Advances in Production Management Systems (pp. 65-72). Cham: Springer","DOI":"10.1007\/978-3-319-99707-0_9"},{"key":"10913_CR12","doi-asserted-by":"crossref","unstructured":"Jifri MH, Hassan EE, Miswan NH (2017) Forecasting performance of time series and regression in modeling electricity load demand. In: 2017 7th IEEE International Conference on System Engineering and Technology (ICSET) (pp. 12-16). IEEE","DOI":"10.1109\/ICSEngT.2017.8123412"},{"key":"10913_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.cie.2016.11.014","volume":"103","author":"TY Kim","year":"2017","unstructured":"Kim TY, Dekker R, Heij C (2017) Spare part demand forecasting for consumer goods using installed base information. Comput Ind Eng 103:201\u2013215","journal-title":"Comput Ind Eng"},{"key":"10913_CR14","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.techfore.2018.11.014","volume":"139","author":"N Kim","year":"2019","unstructured":"Kim N, Park Y, Lee D (2019) Differences in consumer intention to use on-demand automobile-related services in accordance with the degree of face-to-face interactions. Technol Forecast Soc Chang 139:277\u2013286","journal-title":"Technol Forecast Soc Chang"},{"issue":"5","key":"10913_CR15","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1109\/TFUZZ.2018.2831637","volume":"27","author":"Y Liu","year":"2018","unstructured":"Liu Y, Zhang Q, Fan ZP, You TH, Wang LX (2018) Maintenance spare parts demand forecasting for automobile 4S shop considering weather data. IEEE Trans Fuzzy Syst 27(5):943\u2013955","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"10913_CR16","doi-asserted-by":"publisher","first-page":"105673","DOI":"10.1016\/j.cie.2019.01.047","volume":"139","author":"M Mehdizadeh","year":"2020","unstructured":"Mehdizadeh M (2020) Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts. Comput Ind Eng 139:105673","journal-title":"Comput Ind Eng"},{"issue":"5","key":"10913_CR17","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/s11518-018-5390-8","volume":"27","author":"W Pannakkong","year":"2018","unstructured":"Pannakkong W, Sriboonchitta S, Huynh VN (2018) An ensemble model of Arima and ann with restricted Boltzmann machine based on decomposition of discrete wavelet transform for time series forecasting. J Syst Sci Syst Eng 27(5):690\u2013708","journal-title":"J Syst Sci Syst Eng"},{"key":"10913_CR18","doi-asserted-by":"crossref","unstructured":"Savastano M, Amendola C, Fabrizio D, Massaroni E (2016) 3-D printing in the spare parts supply chain: an explorative study in the automotive industry. In: digitally supported innovation (pp. 153\u2013170). Cham: Springer","DOI":"10.1007\/978-3-319-40265-9_11"},{"issue":"3","key":"10913_CR19","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s00521-015-2089-3","volume":"28","author":"VB Semwal","year":"2017","unstructured":"Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput & Applic 28(3):565\u2013574","journal-title":"Neural Comput & Applic"},{"key":"10913_CR20","doi-asserted-by":"crossref","unstructured":"Semwal VB, Gaud N, Nandi GC (2019) Human gait state prediction using cellular automata and classification using ELM. Machine intelligence and signal analysis (pp. 135\u2013145). Singapore: Springer","DOI":"10.1007\/978-981-13-0923-6_12"},{"issue":"2","key":"10913_CR21","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.ijforecast.2016.11.008","volume":"33","author":"RD Snyder","year":"2017","unstructured":"Snyder RD, Ord JK, Koehler AB, McLaren KR, Beaumont AN (2017) Forecasting compositional time series: a state space approach. Int J Forecast 33(2):502\u2013512","journal-title":"Int J Forecast"},{"issue":"6","key":"10913_CR22","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TSMCC.2012.2215319","volume":"42","author":"AA Sodemann","year":"2012","unstructured":"Sodemann AA, Ross MP, Borghetti BJ (2012) A review of anomaly detection in automated surveillance. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1257\u20131272","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"2","key":"10913_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1108\/JOSM-09-2016-0250","volume":"29","author":"K Stormi","year":"2018","unstructured":"Stormi K, Laine T, Suomala P, Elomaa T (2018) Forecasting sales in industrial services: modeling business potential with installed base information. J Serv Manag 29(2):277\u2013300","journal-title":"J Serv Manag"},{"key":"10913_CR24","doi-asserted-by":"crossref","unstructured":"Widmer T, Klein A, Wachter P, Meyl S (2019) Predicting material requirements in the automotive industry using data mining. In: International Conference on Business Information Systems (pp. 147-161). Cham: Springer","DOI":"10.1007\/978-3-030-20482-2_13"},{"key":"10913_CR25","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"issue":"1","key":"10913_CR26","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.ejor.2017.01.053","volume":"261","author":"S Zhu","year":"2017","unstructured":"Zhu S, Dekker R, Van Jaarsveld W, Renjie RW, Koning AJ (2017) An improved method for forecasting spare parts demand using extreme value theory. Eur J Oper Res 261(1):169\u2013181","journal-title":"Eur J Oper Res"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10913-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10913-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10913-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T06:21:39Z","timestamp":1619590899000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10913-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":26,"alternative-id":["10913"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10913-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]},"assertion":[{"value":"26 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}