{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:23:13Z","timestamp":1775845393315,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002352","name":"Ain Shams University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002352","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can be collected in batches or in real time. The problem now is how to benefit from all of this data gathered by sensing and monitoring changes like temperature, light, and position. In this paper, we propose a predictive analytics framework constructed on top of open-source technologies such as Apache Spark and Kafka. The framework focuses on forecasting temperature time series data using traditional and deep learning predictive analytics methods. The analysis and prediction tasks were performed using Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and a novel hybrid model based on Convolution Neural Network (CNN) and LSTM. The purpose of this paper is to determine whether and how recently developed deep learning-based models outperform traditional algorithms in the prediction of time series data.\u00a0The empirical studies conducted and reported in this paper demonstrate that deep learning-based models, specifically LSTM and CNN-LSTM, exhibit superior performance compared to traditional-based algorithms, ARIMA and SARIMA. More specifically, the average reduction in error rates obtained by LSTM and CNN-LSTM models were substantial when compared to other models indicating the superiority of deep learning. Moreover, the CNN-LSTM-based deep learning model exhibits a higher degree of closeness to the actual values when compared to the LSTM-based model.<\/jats:p>","DOI":"10.1007\/s00521-023-09398-9","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T03:02:46Z","timestamp":1705546966000},"page":"6119-6132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A predictive analytics framework for sensor data using time series and deep learning techniques"],"prefix":"10.1007","volume":"36","author":[{"given":"Hend A.","family":"Selmy","sequence":"first","affiliation":[]},{"given":"Hoda K.","family":"Mohamed","sequence":"additional","affiliation":[]},{"given":"Walaa","family":"Medhat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"9398_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10796-014-9492-7","volume":"17","author":"S Li","year":"2014","unstructured":"Li S, Da XuL, Zhao S (2014) The internet of things: a survey. Inf Syst Front 17:243\u2013259. https:\/\/doi.org\/10.1007\/s10796-014-9492-7","journal-title":"Inf Syst Front"},{"key":"9398_CR2","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.1109\/tkde.2017.2740932","volume":"29","author":"SK Jensen","year":"2017","unstructured":"Jensen SK, Pedersen TB, Thomsen C (2017) Time series management systems: a survey. IEEE Trans Knowl Data Eng 29:2581\u20132600. https:\/\/doi.org\/10.1109\/tkde.2017.2740932","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9398_CR3","doi-asserted-by":"publisher","unstructured":"Wang C, Huang X, Qiao J, et al (2020) Apache IoTDB: time-series database for internet of things. Proc VLDB Endow 13:2901\u20132904. https:\/\/doi.org\/10.14778\/3415478.3415504","DOI":"10.14778\/3415478.3415504"},{"key":"9398_CR4","doi-asserted-by":"publisher","first-page":"6141","DOI":"10.3390\/app11136141","volume":"11","author":"E Ghaderpour","year":"2021","unstructured":"Ghaderpour E, Pagiatakis SD, Hassan QK (2021) A survey on change detection and time series analysis with applications. Appl Sci 11:6141. https:\/\/doi.org\/10.3390\/app11136141","journal-title":"Appl Sci"},{"key":"9398_CR5","doi-asserted-by":"crossref","unstructured":"Ninagawa C (2022) LSTM AI Modeling. In: AI Time Series Control System Modelling. Springer Nature Singapore, pp 67\u201390","DOI":"10.1007\/978-981-19-4594-6_4"},{"key":"9398_CR6","unstructured":"Raicharoen T, Lursinsap C, Sanguanbhokai P (2003) Application of critical support vector machine to time series prediction. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS \u201903. IEEE"},{"key":"9398_CR7","doi-asserted-by":"publisher","first-page":"644","DOI":"10.2307\/3009061","volume":"27","author":"N Ghosh","year":"1976","unstructured":"Ghosh N, Anderson OD (1976) Time series analysis and forecasting (the box-Jenkins approach). Oper Res Q 27:644. https:\/\/doi.org\/10.2307\/3009061","journal-title":"Oper Res Q"},{"key":"9398_CR8","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.ijforecast.2021.11.001","volume":"38","author":"F Petropoulos","year":"2022","unstructured":"Petropoulos F, Apiletti D, Assimakopoulos V et al (2022) Forecasting: theory and practice. Int J Forecast 38:705\u2013871. https:\/\/doi.org\/10.1016\/j.ijforecast.2021.11.001","journal-title":"Int J Forecast"},{"key":"9398_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s12355-011-0071-7","volume":"13","author":"KK Suresh","year":"2011","unstructured":"Suresh KK, Krishna Priya SR (2011) Forecasting sugarcane yield of tamilnadu using ARIMA models. Sugar Tech 13:23\u201326. https:\/\/doi.org\/10.1007\/s12355-011-0071-7","journal-title":"Sugar Tech"},{"key":"9398_CR10","doi-asserted-by":"publisher","first-page":"3330","DOI":"10.1109\/tfuzz.2019.2949767","volume":"28","author":"T Chen","year":"2020","unstructured":"Chen T, Shang C, Yang J et al (2020) A new approach for transformation-based fuzzy rule interpolation. IEEE Trans Fuzzy Syst 28:3330\u20133344. https:\/\/doi.org\/10.1109\/tfuzz.2019.2949767","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9398_CR11","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4009459","author":"TOKSARI MD,","year":"2022","unstructured":"TOKSARI MD, (2022) A hybrid algorithm for forecasting transportation energy demand of turkey. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.4009459","journal-title":"SSRN Electron J"},{"key":"9398_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107161","volume":"103","author":"KE ArunKumar","year":"2021","unstructured":"ArunKumar KE, Kalaga DV, Sai Kumar CM et al (2021) Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Appl Soft Comput 103:107161. https:\/\/doi.org\/10.1016\/j.asoc.2021.107161","journal-title":"Appl Soft Comput"},{"key":"9398_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-021-10383-x","author":"H Qiu","year":"2021","unstructured":"Qiu H, Zhao H, Xiang H et al (2021) Forecasting the incidence of mumps in Chongqing based on a SARIMA model. BMC Public Health. https:\/\/doi.org\/10.1186\/s12889-021-10383-x","journal-title":"BMC Public Health"},{"key":"9398_CR14","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 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"9398_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/big.2020.0159","volume":"9","author":"JF Torres","year":"2021","unstructured":"Torres JF, Hadjout D, Sebaa A et al (2021) Deep learning for time series forecasting: a survey. Big Data 9:3\u201321. https:\/\/doi.org\/10.1089\/big.2020.0159","journal-title":"Big Data"},{"key":"9398_CR16","unstructured":"(2020) No Title. Mach. Learn. Time Ser. Forecast. With Python\u00ae 137\u2013165"},{"key":"9398_CR17","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","volume":"99","author":"B Lindemann","year":"2021","unstructured":"Lindemann B, M\u00fcller T, Vietz H et al (2021) A survey on long short-term memory networks for time series prediction. Procedia CIRP 99:650\u2013655. https:\/\/doi.org\/10.1016\/j.procir.2021.03.088","journal-title":"Procedia CIRP"},{"key":"9398_CR18","doi-asserted-by":"publisher","unstructured":"Aksan F, Li Y, Suresh V, Janik P (2023) CNN-LSTM vs. LSTM-CNN to predict power flow direction: a case study of the high-voltage subnet of northeast Germany. Sensors 23:901. https:\/\/doi.org\/10.3390\/s23020901","DOI":"10.3390\/s23020901"},{"key":"9398_CR19","doi-asserted-by":"publisher","unstructured":"V RT, Gouda KC, Kumar SS (2022) Novel approach for spatiotemporal weather data analysis. Int J Adv Comput Sci Appl https:\/\/doi.org\/10.14569\/ijacsa.2022.0130743","DOI":"10.14569\/ijacsa.2022.0130743"},{"key":"9398_CR20","doi-asserted-by":"publisher","first-page":"52024","DOI":"10.1088\/1757-899x\/394\/5\/052024","volume":"394","author":"P Chen","year":"2018","unstructured":"Chen P, Niu A, Liu D et al (2018) Time series forecasting of temperatures using SARIMA: an example from Nanjing. IOP Conf Ser Mater Sci Eng 394:52024. https:\/\/doi.org\/10.1088\/1757-899x\/394\/5\/052024","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"9398_CR21","doi-asserted-by":"crossref","unstructured":"Srivastava A, S A (2022) Weather prediction using LSTM neural networks. In: 2022 IEEE 7th International conference for Convergence in Technology (I2CT). IEEE","DOI":"10.1109\/I2CT54291.2022.9824268"},{"key":"9398_CR22","doi-asserted-by":"publisher","first-page":"930","DOI":"10.3844\/jcssp.2018.930.938","volume":"14","author":"AG Salman","year":"2018","unstructured":"Salman AG, Heryadi Y, Abdurahman E, Suparta W (2018) Weather forecasting using merged long short-term memory model (LSTM) and autoregressive integrated moving average (ARIMA) model. J Comput Sci 14:930\u2013938. https:\/\/doi.org\/10.3844\/jcssp.2018.930.938","journal-title":"J Comput Sci"},{"key":"9398_CR23","doi-asserted-by":"publisher","first-page":"107908","DOI":"10.1016\/j.epsr.2022.107908","volume":"208","author":"A Agga","year":"2022","unstructured":"Agga A, Abbou A, Labbadi M et al (2022) CNN-LSTM: an efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electr Power Syst Res 208:107908. https:\/\/doi.org\/10.1016\/j.epsr.2022.107908","journal-title":"Electr Power Syst Res"},{"key":"9398_CR24","doi-asserted-by":"crossref","unstructured":"Han C, Park H, Kim Y, Gim G (2023) Hybrid CNN-LSTM based time series data prediction model study. In: Big Data, Cloud Computing, and Data Science Engineering. Springer International Publishing, pp. 43\u201354","DOI":"10.1007\/978-3-031-19608-9_4"},{"key":"9398_CR25","doi-asserted-by":"crossref","unstructured":"Raksha S, Graceline JS, Anbarasi J, et al (2021) Weather forecasting framework for time series data using intelligent learning models. In: 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE","DOI":"10.1109\/ICEECCOT52851.2021.9707971"},{"key":"9398_CR26","doi-asserted-by":"crossref","unstructured":"Hao J, Jinming C, Yajuan G (2018) Data-driven lean management for distribution network. In: 2018 China International Conference on Electricity Distribution (CICED). IEEE","DOI":"10.1109\/CICED.2018.8592556"},{"key":"9398_CR27","unstructured":"Warren J. & MN (2015) Big data: principles and best practices of scalable realtime data systems. Simon and Schuster"},{"key":"9398_CR28","unstructured":"sparkTM\u2014unified engine for large-scale data analytics. URL https:\/\/spark.apache.org\/ A No Title"},{"key":"9398_CR29","doi-asserted-by":"crossref","unstructured":"Vyas S, Tyagi RK, Jain C, Sahu S (2022) Performance evaluation of apache kafka\u2014a modern platform for real time data streaming. In: 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE","DOI":"10.1109\/ICIPTM54933.2022.9754154"},{"key":"9398_CR30","unstructured":"From https:\/\/kafka.apache.org\/ AK (n. d.). R"},{"key":"9398_CR31","unstructured":"From https:\/\/hadoop.apache.org\/ AHR"},{"key":"9398_CR32","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?\u2014Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247\u20131250. https:\/\/doi.org\/10.5194\/gmd-7-1247-2014","journal-title":"Geosci Model Dev"},{"key":"9398_CR33","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102:359\u2013378. https:\/\/doi.org\/10.1198\/016214506000001437","journal-title":"J Am Stat Assoc"},{"key":"9398_CR34","volume-title":"TSAF, control","author":"BGGM Jenkins","year":"1976","unstructured":"Jenkins BGGM (1976) TSAF, control, Rev. Holden-Day. No Title, San Francisco","edition":"Rev"},{"key":"9398_CR35","first-page":"47","volume-title":"Introduction to financial forecasting in investment analysis","author":"JB Guerard","year":"2012","unstructured":"Guerard JB (2012) An Introduction to Time Series Modeling and Forecasting. Introduction to financial forecasting in investment analysis. Springer, New York, pp 47\u201372"},{"key":"9398_CR36","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/forecast3010010","volume":"3","author":"JL Castle","year":"2021","unstructured":"Castle JL, Doornik JA, Hendry DF (2021) Forecasting principles from experience with forecasting competitions. Forecasting 3:138\u2013165. https:\/\/doi.org\/10.3390\/forecast3010010","journal-title":"Forecasting"},{"key":"9398_CR37","unstructured":"pmdarima: ARIMA estimators for Python\u2014pmdarima 2.0.3 documentation. (n.d.). Retrieved from https:\/\/alkaline-ml.com\/pmdarima\/index.html"},{"key":"9398_CR38","doi-asserted-by":"publisher","unstructured":"Li G, Wang Y (2013) Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks. EURASIP J Wirel Commun Netw 2013 https:\/\/doi.org\/10.1186\/1687-1499-2013-85","DOI":"10.1186\/1687-1499-2013-85"},{"key":"9398_CR39","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1111\/j.1467-9892.2009.00643.x","volume":"31","author":"G Janacek","year":"2010","unstructured":"Janacek G (2010) Time series analysis forecasting and control. J Time Ser Anal 31:303. https:\/\/doi.org\/10.1111\/j.1467-9892.2009.00643.x","journal-title":"J Time Ser Anal"},{"key":"9398_CR40","doi-asserted-by":"publisher","unstructured":"Patowary AN (2017) monthly temperature prediction based on arima model: a case study in Dibrugarh station of Assam, India. Int J Adv Res Comput Sci 8:292\u2013298. https:\/\/doi.org\/10.26483\/ijarcs.v8i8.4590","DOI":"10.26483\/ijarcs.v8i8.4590"},{"key":"9398_CR41","doi-asserted-by":"publisher","first-page":"427","DOI":"10.2307\/2286348","volume":"74","author":"DA Dickey","year":"1979","unstructured":"Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427. https:\/\/doi.org\/10.2307\/2286348","journal-title":"J Am Stat Assoc"},{"key":"9398_CR42","unstructured":"statsmodels 0.14.0. (n.d.). Retrieved from https:\/\/www.statsmodels.org\/stable\/index.html No Title"},{"key":"9398_CR43","doi-asserted-by":"publisher","unstructured":"Meenakshi D, Shanavas ARM (2022) Novel Shared Input Based LSTM for Semantic Similarity Prediction. J Adv Inf Technol https:\/\/doi.org\/10.12720\/jait.13.4.387-392","DOI":"10.12720\/jait.13.4.387-392"},{"key":"9398_CR44","doi-asserted-by":"publisher","unstructured":"Albeladi K, Zafar B, Mueen A (2023) Time Series Forecasting using LSTM and ARIMA. Int J Adv Comput Sci Appl https:\/\/doi.org\/10.14569\/ijacsa.2023.0140133","DOI":"10.14569\/ijacsa.2023.0140133"},{"key":"9398_CR45","doi-asserted-by":"crossref","unstructured":"Verma P, Chafe C (2021) A generative model for raw audio using transformer architectures. In: 2021 24th International Conference on Digital Audio Effects (DAFx). IEEE","DOI":"10.23919\/DAFx51585.2021.9768298"},{"key":"9398_CR46","doi-asserted-by":"crossref","unstructured":"Liu P (2022) Time Series Forecasting Based on ARIMA and LSTM. In: Advances in Economics, Business and Management Research. Atlantis Press","DOI":"10.2991\/aebmr.k.220603.195"},{"key":"9398_CR47","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/taslp.2016.2520371","volume":"24","author":"H Palangi","year":"2016","unstructured":"Palangi H, Deng L, Shen Y et al (2016) Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE\/ACM Trans Audio, Speech, Lang Process 24:694\u2013707. https:\/\/doi.org\/10.1109\/taslp.2016.2520371","journal-title":"IEEE\/ACM Trans Audio, Speech, Lang Process"},{"key":"9398_CR48","doi-asserted-by":"publisher","first-page":"4504","DOI":"10.1109\/tsp.2016.2557301","volume":"64","author":"H Palangi","year":"2016","unstructured":"Palangi H, Ward R, Deng L (2016) Distributed compressive sensing: a deep learning approach. IEEE Trans Signal Process 64:4504\u20134518. https:\/\/doi.org\/10.1109\/tsp.2016.2557301","journal-title":"IEEE Trans Signal Process"},{"key":"9398_CR49","doi-asserted-by":"publisher","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12:e0180944. https:\/\/doi.org\/10.1371\/journal.pone.0180944","journal-title":"PLoS ONE"},{"key":"9398_CR50","unstructured":"Keras: Deep Learning for humans. (n.d.). Retrieved from https:\/\/keras.io\/"},{"key":"9398_CR51","doi-asserted-by":"publisher","unstructured":"Lee H, Song J (2019) Introduction to convolutional neural network using Keras; an understanding from a statistician. Commun Stat Appl Methods 26:591\u2013610. https:\/\/doi.org\/10.29220\/csam.2019.26.6.591","DOI":"10.29220\/csam.2019.26.6.591"},{"key":"9398_CR52","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"9398_CR53","doi-asserted-by":"publisher","unstructured":"Douglass MJJ (2020) Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aur\u00e9lien G\u00e9ron: O\u2019 Reilly Media, Phys Eng Sci Med 43:1135\u20131136. https:\/\/doi.org\/10.1007\/s13246-020-00913-z","DOI":"10.1007\/s13246-020-00913-z"},{"key":"9398_CR54","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611\u2013629. https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"key":"9398_CR55","unstructured":"Sumanthvrao. \u201cDaily Climate Time Series Data.\u201d Kaggle 23 Aug. 2019 www. kaggle.com\/datasets\/sumanthvrao\/daily-climate-time-series-data"},{"key":"9398_CR56","doi-asserted-by":"crossref","unstructured":"Chen L, Lai X (2011) Comparison between ARIMA and ANN models used in short-term wind speed forecasting. In: 2011 Asia-Pacific Power and Energy Engineering Conference. IEEE","DOI":"10.1109\/APPEEC.2011.5748446"},{"key":"9398_CR57","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS (1990) STL: a seasonal-trend decomposition procedure based on loess. J Offic Statist 6:3\u201333","journal-title":"J Offic Statist"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09398-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09398-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09398-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T09:11:42Z","timestamp":1710580302000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09398-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,18]]},"references-count":57,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9398"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09398-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,18]]},"assertion":[{"value":"5 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","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 have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}