{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:36:12Z","timestamp":1775324172082,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01196-5","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T10:19:23Z","timestamp":1748686763000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting"],"prefix":"10.1186","volume":"12","author":[{"given":"G. H. Harish","family":"Nayak","sequence":"first","affiliation":[]},{"given":"Md. Wasi","family":"Alam","sequence":"additional","affiliation":[]},{"given":"B. Samuel","family":"Naik","sequence":"additional","affiliation":[]},{"given":"B. S.","family":"Varshini","sequence":"additional","affiliation":[]},{"given":"G.","family":"Avinash","sequence":"additional","affiliation":[]},{"given":"Rajeev Ranjan","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Mrinmoy","family":"Ray","sequence":"additional","affiliation":[]},{"given":"K. N.","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"issue":"1","key":"1196_CR1","first-page":"1","volume":"79","author":"P Kumar","year":"2024","unstructured":"Kumar P. Sustaining food and nutritional security in India: an assessment of agri-food systems and scoping for future. Ind J Agric Econ. 2024;79(1):1\u201346.","journal-title":"Ind J Agric Econ"},{"key":"1196_CR2","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1108\/JFEP-12-2023-0398","volume":"16","author":"AK Sharma","year":"2024","unstructured":"Sharma AK. Volatility dynamics in energy and agriculture markets: an analysis of domestic and global uncertainty factors. J Financ Econ Policy. 2024;16:580\u2013600.","journal-title":"J Financ Econ Policy"},{"key":"1196_CR3","first-page":"271","volume-title":"Cropping systems modeling under changing climate","author":"M Ahmed","year":"2024","unstructured":"Ahmed M, Ahmad S, Abbas G, Hussain S, Hoogenboom G. Potato-potato system. In: Hoogenboom G, Abbas G, Ahmed M, Ahmad S, Hussain S, editors. Cropping systems modeling under changing climate. Berlin: Springer; 2024. p. 271\u2013306."},{"issue":"4","key":"1196_CR4","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s11540-023-09682-0","volume":"67","author":"PK Sahu","year":"2024","unstructured":"Sahu PK, Das M, Sarkar B, VS A, Dey S, Narasimhaiah L, et al. Potato production in India: a critical appraisal on sustainability, forecasting, price and export behaviour. Potato Res. 2024;67(4):1209\u201345.","journal-title":"Potato Res"},{"key":"1196_CR5","first-page":"11","volume-title":"Growth, poverty and developmental aspects of agriculture","author":"S Adhikari","year":"2024","unstructured":"Adhikari S, Chatterjee N. Assessing efficiency of food grain production across Indian states: an investigation based on data envelopment analysis. In: Das RC, editor. Growth, poverty and developmental aspects of agriculture. Leeds: Emerald Publishing Limited; 2024. p. 11\u201325."},{"issue":"4","key":"1196_CR6","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1007\/s11540-024-09717-0","volume":"67","author":"P Mishra","year":"2024","unstructured":"Mishra P, Alhussan AA, Khafaga DS, Lal P, Ray S, Abotaleb M, et al. Forecasting production of potato for a sustainable future: global market analysis. Potato Res. 2024;67(4):1671\u201390.","journal-title":"Potato Res"},{"key":"1196_CR7","doi-asserted-by":"publisher","first-page":"4661","DOI":"10.1007\/s00521-021-06621-3","volume":"34","author":"R Jaiswal","year":"2022","unstructured":"Jaiswal R, Jha GK, Kumar RR, Choudhary K. Deep long short-term memory based model for agricultural price forecasting. Neural Comput Appl. 2022;34:4661\u201376.","journal-title":"Neural Comput Appl"},{"key":"1196_CR8","doi-asserted-by":"publisher","first-page":"111557","DOI":"10.1016\/j.asoc.2024.111557","volume":"158","author":"G Avinash","year":"2024","unstructured":"Avinash G, Ramasubramanian V, Ray M, Paul RK, Godara S, Nayak GHH, et al. Hidden Markov guided deep learning models for forecasting highly volatile agricultural commodity prices. Appl Soft Comput. 2024;158:111557.","journal-title":"Appl Soft Comput"},{"key":"1196_CR9","doi-asserted-by":"publisher","first-page":"105837","DOI":"10.1016\/j.asoc.2019.105837","volume":"86","author":"MHDM Ribeiro","year":"2020","unstructured":"Ribeiro MHDM, dos Santos CL. Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Appl Soft Comput. 2020;86:105837.","journal-title":"Appl Soft Comput"},{"key":"1196_CR10","doi-asserted-by":"crossref","unstructured":"Zakrani A, Najm A, Marzak A. Support Vector Regression Based on Grid-Search Method for Agile Software Effort Prediction. 2018 IEEE 5th International Congress on Information Science and Technology (CiSt). IEEE; 2018. p. 1\u20136","DOI":"10.1109\/CIST.2018.8596370"},{"key":"1196_CR11","first-page":"11","volume":"09","author":"K Khaled","year":"2025","unstructured":"Khaled K, Singla MK. Predictive analysis of groundwater resources using random forest regression. J Artif Intell Meta. 2025;09:11\u20139.","journal-title":"J Artif Intell Meta"},{"key":"1196_CR12","doi-asserted-by":"publisher","first-page":"3119","DOI":"10.1007\/s40808-023-01944-7","volume":"10","author":"GHH Nayak","year":"2024","unstructured":"Nayak GHH, Alam W, Singh KN, Avinash G, Ray M, Kumar RR. Modelling monthly rainfall of India through transformer-based deep learning architecture. Model Earth Syst Environ. 2024;10:3119\u201336.","journal-title":"Model Earth Syst Environ"},{"key":"1196_CR13","first-page":"28","volume-title":"Trustworthy artificial intelligence in industry and society","author":"D Patil","year":"2024","unstructured":"Patil D, Rane NL, Desai P, Rane J. Machine learning and deep learning: methods, techniques, applications, challenges, and future research opportunities. In: Patil D, Rane NL, Desai P, Rane J, editors. Trustworthy artificial intelligence in industry and society. Mumbai: Deep Science Publishing; 2024. p. 28\u201381."},{"key":"1196_CR14","doi-asserted-by":"publisher","first-page":"15548","DOI":"10.1016\/j.egyr.2022.10.402","volume":"8","author":"A Djaafari","year":"2022","unstructured":"Djaafari A, Ibrahim A, Bailek N, Bouchouicha K, Hassan MA, Kuriqi A, et al. Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions. Energy Rep. 2022;8:15548\u201362.","journal-title":"Energy Rep"},{"key":"1196_CR15","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.ins.2020.01.046","volume":"519","author":"J Li","year":"2020","unstructured":"Li J, Lin J. A probability distribution detection based hybrid ensemble QoS prediction approach. Inf Sci. 2020;519:289\u2013305.","journal-title":"Inf Sci"},{"key":"1196_CR16","first-page":"5998","volume-title":"Advances in neural information processing systems 30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Brain G, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. Attention is all you need. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, editors., et al., Advances in neural information processing systems 30. Long Beach: Neural Information Processing Systems Foundation, Inc. (NeurIPS); 2017. p. 5998\u20136008."},{"key":"1196_CR17","doi-asserted-by":"publisher","first-page":"3695","DOI":"10.1007\/s00477-024-02778-0","volume":"38","author":"HC El","year":"2024","unstructured":"El HC, Belaqziz S, Khabba S, Hssaine BA, Kharrou MH, Chehbouni A. Advancements in weather forecasting for precision agriculture: from statistical modeling to transformer-based architectures. Stoch Env Res Risk Assess. 2024;38:3695\u2013717.","journal-title":"Stoch Env Res Risk Assess"},{"key":"1196_CR18","doi-asserted-by":"publisher","first-page":"100716","DOI":"10.1016\/j.simpa.2024.100716","volume":"22","author":"GHH Nayak","year":"2024","unstructured":"Nayak GHH, Alam MW, Avinash G, Kumar RR, Ray M, Barman S, et al. Transformer-based deep learning architecture for time series forecasting. Softw Impacts. 2024;22:100716.","journal-title":"Softw Impacts"},{"key":"1196_CR19","doi-asserted-by":"publisher","first-page":"107417","DOI":"10.1016\/j.bspc.2024.107417","volume":"102","author":"Z Tarek","year":"2025","unstructured":"Tarek Z, Alhussan AA, Khafaga DS, El-Kenawy E-SM, Elshewey AM. A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages. Biomed Signal Process Control. 2025;102:107417.","journal-title":"Biomed Signal Process Control"},{"key":"1196_CR20","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3233\/AIC-200629","volume":"33","author":"A Roy Choudhury","year":"2020","unstructured":"Roy Choudhury A, Abrishami S, Turek M, Kumar P. Enhancing profit from stock transactions using neural networks. AI Commun. 2020;33:75\u201392.","journal-title":"AI Commun"},{"key":"1196_CR21","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1007\/s10614-022-10325-8","volume":"62","author":"B Tripathi","year":"2023","unstructured":"Tripathi B, Sharma RK. Modeling bitcoin prices using signal processing methods, Bayesian optimization, and deep neural networks. Comput Econ. 2023;62:1919\u201345.","journal-title":"Comput Econ"},{"key":"1196_CR22","doi-asserted-by":"publisher","first-page":"13319","DOI":"10.1007\/s00521-022-07143-2","volume":"34","author":"DrM Durairaj","year":"2022","unstructured":"Durairaj DrM, Mohan BHK. A convolutional neural network based approach to financial time series prediction. Neural Comput Appl. 2022;34:13319\u201337.","journal-title":"Neural Comput Appl"},{"key":"1196_CR23","doi-asserted-by":"publisher","first-page":"116583","DOI":"10.1016\/j.eswa.2022.116583","volume":"195","author":"E Paquet","year":"2022","unstructured":"Paquet E, Soleymani F. QuantumLeap: hybrid quantum neural network for financial predictions. Expert Syst Appl. 2022;195:116583.","journal-title":"Expert Syst Appl"},{"key":"1196_CR24","doi-asserted-by":"publisher","first-page":"e1519","DOI":"10.1002\/widm.1519","volume":"14","author":"C Zhang","year":"2024","unstructured":"Zhang C, Sjarif NNA, Ibrahim R. Deep learning models for price forecasting of financial time series: a review of recent advancements: 2020\u20132022. Wiley Interdiscip Rev Data Min Knowl Discov. 2024;14:e1519.","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"1196_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.5334\/dsj-2019-016","volume":"18","author":"H Wang","year":"2019","unstructured":"Wang H, Bai Y, Li C, Guo Z, Zhang J. Time series prediction model of grey wolf optimized echo state network. Data Sci J. 2019;18:16.","journal-title":"Data Sci J"},{"key":"1196_CR26","doi-asserted-by":"publisher","first-page":"23784","DOI":"10.1038\/s41598-024-72013-x","volume":"14","author":"E-SM Elkenawy","year":"2024","unstructured":"Elkenawy E-SM, Alhussan AA, Khafaga DS, Tarek Z, Elshewey AM. Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification. Sci Rep. 2024;14:23784.","journal-title":"Sci Rep"},{"key":"1196_CR27","doi-asserted-by":"publisher","first-page":"457","DOI":"10.3390\/biomimetics8060457","volume":"8","author":"EH Alkhammash","year":"2023","unstructured":"Alkhammash EH, Assiri SA, Nemenqani DM, Althaqafi RMM, Hadjouni M, Saeed F, et al. Application of machine learning to predict COVID-19 spread via an optimized BPSO model. Biomimetics. 2023;8:457.","journal-title":"Biomimetics"},{"key":"1196_CR28","doi-asserted-by":"publisher","first-page":"354","DOI":"10.30534\/ijatcse\/2019\/04832019","volume":"8","author":"YHT Louis","year":"2019","unstructured":"Louis YHT, Kuok KK, Imteaz M, Lai WY, Derrick KXL. Development of whale optimization neural network for daily water level forecasting. Int J Adv Trends Comput Sci Eng. 2019;8:354\u201362.","journal-title":"Int J Adv Trends Comput Sci Eng"},{"key":"1196_CR29","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s11269-019-02473-8","volume":"34","author":"L Diop","year":"2020","unstructured":"Diop L, Samadianfard S, Bodian A, Yaseen ZM, Ghorbani MA, Salimi H. Annual rainfall forecasting using hybrid artificial intelligence model: integration of multilayer perceptron with whale optimization algorithm. Water Resour Manage. 2020;34:733\u201346.","journal-title":"Water Resour Manage"},{"key":"1196_CR30","doi-asserted-by":"publisher","first-page":"20717","DOI":"10.1038\/s41598-022-25208-z","volume":"12","author":"T Fu","year":"2022","unstructured":"Fu T, Li X. Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation. Sci Rep. 2022;12:20717.","journal-title":"Sci Rep"},{"key":"1196_CR31","first-page":"200138","volume":"16","author":"AC Cinar","year":"2022","unstructured":"Cinar AC, Natarajan N. An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India. Intell Syst Appl. 2022;16:200138.","journal-title":"Intell Syst Appl"},{"key":"1196_CR32","doi-asserted-by":"publisher","first-page":"3352","DOI":"10.3390\/su14063352","volume":"14","author":"HC Kilinc","year":"2022","unstructured":"Kilinc HC, Yurtsever A. Short-term streamflow forecasting using hybrid deep learning model based on grey wolf algorithm for hydrological time series. Sustainability. 2022;14:3352.","journal-title":"Sustainability"},{"key":"1196_CR33","doi-asserted-by":"publisher","DOI":"10.2523\/AJEE.2022.040301","author":"Wu Haoqian","year":"2022","unstructured":"Haoqian Wu, Zhaozhengyang Li. Research on wind speed prediction model based on WOA-LSTM. Acad J Environ Earth Sci. 2022. https:\/\/doi.org\/10.2523\/AJEE.2022.040301.","journal-title":"Acad J Environ Earth Sci"},{"key":"1196_CR34","first-page":"47","volume":"3","author":"M Mahmoud","year":"2025","unstructured":"Mahmoud M. A review on waste management techniques for sustainable energy production. Meta Optim Rev. 2025;3:47\u201358.","journal-title":"Meta Optim Rev"},{"key":"1196_CR35","doi-asserted-by":"publisher","first-page":"74449","DOI":"10.1109\/ACCESS.2022.3190508","volume":"10","author":"DS Khafaga","year":"2022","unstructured":"Khafaga DS, Alhussan AA, El-Kenawy E-SM, Ibrahim A, Eid MM, Abdelhamid AA. Solving optimization problems of metamaterial and double T-shape antennas using advanced meta-heuristics algorithms. IEEE Access. 2022;10:74449\u201371.","journal-title":"IEEE Access"},{"key":"1196_CR36","doi-asserted-by":"publisher","first-page":"749","DOI":"10.32604\/cmc.2022.029605","volume":"73","author":"D Sami Khafaga","year":"2022","unstructured":"Sami Khafaga D, Ali Alhussan A, El-Kenawy E-SM, Takieldeen AE, Hassan TM, Hegazy EA, et al. Meta-heuristics for feature selection and classification in diagnostic breast cancer. Comput, Mater Continua. 2022;73:749\u201365.","journal-title":"Comput, Mater Continua"},{"key":"1196_CR37","doi-asserted-by":"publisher","first-page":"24489","DOI":"10.1038\/s41598-024-74475-5","volume":"14","author":"AM Elshewey","year":"2024","unstructured":"Elshewey AM, Alhussan AA, Khafaga DS, Elkenawy E-SM, Tarek Z. EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm. Sci Rep. 2024;14:24489.","journal-title":"Sci Rep"},{"key":"1196_CR38","doi-asserted-by":"publisher","first-page":"7433","DOI":"10.1007\/s00034-023-02454-8","volume":"42","author":"S Ahmed","year":"2023","unstructured":"Ahmed S, Nielsen IE, Tripathi A, Siddiqui S, Ramachandran RP, Rasool G. Transformers in time-series analysis: a tutorial. Circuits Syst Signal Process. 2023;42:7433\u201366.","journal-title":"Circuits Syst Signal Process"},{"key":"1196_CR39","doi-asserted-by":"crossref","unstructured":"Zerveas G, Jayaraman S, Patel D, Bhamidipaty A, Eickhoff C. A Transformer-based Framework for Multivariate Time Series Representation Learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery; 2021. p. 2114\u201324.","DOI":"10.1145\/3447548.3467401"},{"key":"1196_CR40","doi-asserted-by":"crossref","unstructured":"Muhammad T, Aftab AB, Ahsan MdM, Muhu MM, Ibrahim M, Khan SI, et al. Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market. 2022; Available from: http:\/\/arxiv.org\/abs\/2208.08300","DOI":"10.1142\/S146902682350013X"},{"key":"1196_CR41","doi-asserted-by":"publisher","first-page":"104217","DOI":"10.1016\/j.advwatres.2022.104217","volume":"164","author":"T Bai","year":"2022","unstructured":"Bai T, Tahmasebi P. Characterization of groundwater contamination: a transformer-based deep learning model. Adv Water Resour. 2022;164:104217.","journal-title":"Adv Water Resour"},{"key":"1196_CR42","first-page":"695","volume":"68","author":"SA Alzakari","year":"2024","unstructured":"Alzakari SA, Alhussan AA, Qenawy A-ST, Elshewey AM. Early detection of potato disease using an enhanced convolutional neural network-long short-term memory deep learning model. Potato Res. 2024;68:695\u2013713.","journal-title":"Potato Res"},{"key":"1196_CR43","doi-asserted-by":"publisher","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","volume":"9","author":"P Agrawal","year":"2021","unstructured":"Agrawal P, Abutarboush HF, Ganesh T, Mohamed AW. Metaheuristic algorithms on feature selection: a survey of one decade of research (2009\u20132019). IEEE Access. 2021;9:26766\u201391.","journal-title":"IEEE Access"},{"key":"1196_CR44","first-page":"207","volume-title":"Metaheuristic and evolutionary computation: algorithms and applications","author":"N Khanduja","year":"2021","unstructured":"Khanduja N, Bhushan B. Recent advances and application of metaheuristic algorithms: a survey (2014\u20132020). In: Malik H, Iqbal A, Joshi P, Agrawal S, Bakhsh FI, editors. Metaheuristic and evolutionary computation: algorithms and applications. Singapore: Springer; 2021. p. 207\u201328."},{"key":"1196_CR45","first-page":"863","volume":"2","author":"TM Shami","year":"2022","unstructured":"Shami TM, Grace D, Burr A, Mitchell PD. Single candidate optimizer: a novel optimization algorithm. Evol Intell. 2022;2:863\u201387.","journal-title":"Evol Intell"},{"key":"1196_CR46","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46\u201361.","journal-title":"Adv Eng Softw"},{"key":"1196_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13198-020-00995-8","volume":"12","author":"G Negi","year":"2021","unstructured":"Negi G, Kumar A, Pant S, Ram M. GWO: a review and applications. Int J Syst Assur Eng Manag. 2021;12:1\u20138.","journal-title":"Int J Syst Assur Eng Manag"},{"key":"1196_CR48","doi-asserted-by":"publisher","first-page":"105658","DOI":"10.1016\/j.asoc.2019.105658","volume":"83","author":"FB Ozsoydan","year":"2019","unstructured":"Ozsoydan FB. Effects of dominant wolves in grey wolf optimization algorithm. Appl Soft Comput. 2019;83:105658.","journal-title":"Appl Soft Comput"},{"key":"1196_CR49","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51\u201367.","journal-title":"Adv Eng Softw"},{"key":"1196_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2019.03.004","volume":"48","author":"FS Gharehchopogh","year":"2019","unstructured":"Gharehchopogh FS, Gholizadeh H. A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput. 2019;48:1\u201324.","journal-title":"Swarm Evol Comput"},{"key":"1196_CR51","doi-asserted-by":"publisher","first-page":"8003","DOI":"10.3390\/s21238003","volume":"21","author":"A Brodzicki","year":"2021","unstructured":"Brodzicki A, Piekarski M, Jaworek-Korjakowska J. The whale optimization algorithm approach for deep neural networks. Sensors. 2021;21:8003.","journal-title":"Sensors"},{"key":"1196_CR52","doi-asserted-by":"publisher","first-page":"14701","DOI":"10.1007\/s00521-020-04823-9","volume":"32","author":"H Mohammed","year":"2020","unstructured":"Mohammed H, Rashid T. A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design. Neural Comput Appl. 2020;32:14701\u201318.","journal-title":"Neural Comput Appl"},{"key":"1196_CR53","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN\u201995 - International Conference on Neural Networks. IEEE; p. 1942\u20138.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1196_CR54","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/technologies9030052","volume":"9","author":"M Ahsan","year":"2021","unstructured":"Ahsan M, Mahmud M, Saha P, Gupta K, Siddique Z. Effect of data scaling methods on machine learning algorithms and model performance. Technologies. 2021;9:52.","journal-title":"Technologies"},{"key":"1196_CR55","doi-asserted-by":"publisher","first-page":"100121","DOI":"10.1016\/j.dscb.2024.100121","volume":"13","author":"MO Miah","year":"2024","unstructured":"Miah MO, Habiba U, Kabir MF. ODL-BCI: optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning. Brain Disord. 2024;13:100121.","journal-title":"Brain Disord"},{"key":"1196_CR56","doi-asserted-by":"publisher","first-page":"420","DOI":"10.3390\/s20020420","volume":"20","author":"X Liu","year":"2020","unstructured":"Liu X, Huang H, Xiang J. A personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an FEM simulation driving support vector machine. Sensors. 2020;20:420.","journal-title":"Sensors"},{"key":"1196_CR57","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2020.07.008","volume":"413","author":"X Fei","year":"2020","unstructured":"Fei X, Wang J, Ying S, Hu Z, Shi J. Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease. Neurocomputing. 2020;413:271\u201383.","journal-title":"Neurocomputing"},{"key":"1196_CR58","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s13638-019-1477-2","volume":"2019","author":"X Jin","year":"2019","unstructured":"Jin X, He T, Lin Y. Multi-objective model selection algorithm for online sequential ultimate learning machine. EURASIP J Wirel Commun Netw. 2019;2019:156.","journal-title":"EURASIP J Wirel Commun Netw"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01196-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01196-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01196-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T11:03:07Z","timestamp":1748689387000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01196-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,31]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1196"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01196-5","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,31]]},"assertion":[{"value":"11 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"138"}}