{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:58:57Z","timestamp":1758056337246,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051752","type":"print"},{"value":"9783032051769","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05176-9_36","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:29Z","timestamp":1757943629000},"page":"464-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A New Proposal of\u00a0Layer Insertion in\u00a0Stacked Autoencoder Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6912-8449","authenticated-orcid":false,"given":"Francisco","family":"dos Santos Viana","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-0849","authenticated-orcid":false,"given":"Bianca Val\u00e9ria","family":"Lopes Pereira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1541-8333","authenticated-orcid":false,"given":"Mois\u00e9s","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-8917","authenticated-orcid":false,"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4497-4416","authenticated-orcid":false,"given":"Areolino","family":"de Almeida Neto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","unstructured":"Alibrahim, H., Ludwig, S.A.: Hyperparameter optimization: comparing genetic algorithm against grid search and Bayesian optimization. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1551\u20131559 (2021). https:\/\/doi.org\/10.1109\/CEC45853.2021.9504761","DOI":"10.1109\/CEC45853.2021.9504761"},{"issue":"8","key":"36_CR2","doi-asserted-by":"publisher","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani, M.M., Ghatee, M.: A systematic review on overfitting control in shallow and deep neural networks. Artif. Intell. Rev. 54(8), 6391\u20136438 (2021). https:\/\/doi.org\/10.1007\/s10462-021-09975-1","journal-title":"Artif. Intell. Rev."},{"key":"36_CR3","unstructured":"Bischl, B., et al.: OpenML benchmarking suites. arXiv:1708.03731v2 [stat.ML] (2019)"},{"issue":"2","key":"36_CR4","doi-asserted-by":"publisher","first-page":"149","DOI":"10.2478\/jaiscr-2022-0010","volume":"12","author":"C Brunner","year":"2021","unstructured":"Brunner, C., K\u0151, A., Fodor, S.: An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection. J. Artif. Intell. Soft Comput. Res. 12(2), 149\u2013163 (2021)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Buarque, T.M.T., Marinho, M.B.L., Junior, F.M.B.: Genetic algorithm and PSO applied to the choice of hyperparameters of an MLP neural network for non-functional requirements classification. Res. Soc. Dev. 11(3), e55411326984 (2022)","DOI":"10.33448\/rsd-v11i3.26984"},{"issue":"2","key":"36_CR6","doi-asserted-by":"publisher","first-page":"164","DOI":"10.3390\/axioms12020164","volume":"12","author":"J Deng","year":"2023","unstructured":"Deng, J., Li, Q., Wei, W.: Improved cascade correlation neural network model based on group intelligence optimization algorithm. Axioms 12(2), 164 (2023). https:\/\/doi.org\/10.3390\/axioms12020164","journal-title":"Axioms"},{"key":"36_CR7","unstructured":"Hammad, M.M.: Artificial neural network and deep learning: fundamentals and theory. arXiv preprint arXiv:2408.16002 (2024)"},{"key":"36_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2022.107738","volume":"160","author":"LD Hansen","year":"2022","unstructured":"Hansen, L.D., Stokholm-Bjerregaard, M., Durdevic, P.: Modeling phosphorous dynamics in a wastewater treatment process using Bayesian optimized LSTM. Comput. Chem. Eng. 160, 107738 (2022)","journal-title":"Comput. Chem. Eng."},{"issue":"3","key":"36_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3506695","volume":"31","author":"L Liao","year":"2022","unstructured":"Liao, L., Li, H., Shang, W., Ma, L.: An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks. ACM Trans. Softw. Eng. Methodol. (TOSEM) 31(3), 1\u201340 (2022)","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"},{"key":"36_CR10","doi-asserted-by":"publisher","unstructured":"Mienye, I.D., Swart, T.G.: A comprehensive review of deep learning: architectures, recent advances, and applications. Information 15(12) (2024). https:\/\/doi.org\/10.3390\/info15120755. https:\/\/www.mdpi.com\/2078-2489\/15\/12\/755","DOI":"10.3390\/info15120755"},{"issue":"5","key":"36_CR11","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/a14050158","volume":"14","author":"SAEM Mohamed","year":"2021","unstructured":"Mohamed, S.A.E.M., Mohamed, M.H., Farghally, M.F.: A new cascade-correlation growing deep learning neural network algorithm. Algorithms 14(5), 158 (2021). https:\/\/doi.org\/10.3390\/a14050158","journal-title":"Algorithms"},{"key":"36_CR12","doi-asserted-by":"publisher","unstructured":"Montesinos L\u00f3pez, O.A., Montesinos L\u00f3pez, A., Crossa, J.: Fundamentals of artificial neural networks and deep learning. In: Multivariate Statistical Machine Learning Methods for Genomic Prediction, pp. 379\u2013425. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-89010-0_10","DOI":"10.1007\/978-3-030-89010-0_10"},{"key":"36_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2023.113036","volume":"289","author":"A Morteza","year":"2023","unstructured":"Morteza, A., Yahyaeian, A.A., Mirzaeibonehkhater, M., Sadeghi, S., Mohaimeni, A., Taheri, S.: Deep learning hyperparameter optimization: application to electricity and heat demand prediction for buildings. Energy Build. 289, 113036 (2023)","journal-title":"Energy Build."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Ravikumar, A., Sriraman, H.: Mitigating vanishing gradient in SGD optimization in neural networks. In: International Conference on Information, Communication and Computing Technology, pp. 1\u201311. Springer, Cham (2023)","DOI":"10.1007\/978-981-99-5166-6_1"},{"key":"36_CR15","unstructured":"dos Santos\u00a0Viana, F., Pereira, B.V.L., Santos, M., Soares, C., de\u00a0Almeida\u00a0Neto, A.: Algorithm for layer insertion in stacked autoencoder neural networks with activation function change (2025). https:\/\/github.com\/frahncky\/RNAStacked-learning"},{"key":"36_CR16","doi-asserted-by":"publisher","unstructured":"Silva, R.E., Camata, J.J.: Hyperparameter optimization of physics-guided neural networks in convective-diffusive problems. In: Simp\u00f3sio em Sistemas Computacionais de Alto Desempenho (SSCAD), pp. 137\u2013144. SBC (2024). https:\/\/doi.org\/10.5753\/sscad_estendido.2024.244373","DOI":"10.5753\/sscad_estendido.2024.244373"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Sun, Y., Xue, B., Zhang, M., Yen, G.G.: An experimental study on hyper-parameter optimization for stacked auto-encoders. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp.\u00a01\u20138. IEEE (2018)","DOI":"10.1109\/CEC.2018.8477921"},{"issue":"5","key":"36_CR18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/computers12050091","volume":"12","author":"MM Taye","year":"2023","unstructured":"Taye, M.M.: Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5), 91 (2023)","journal-title":"Computers"},{"issue":"3","key":"36_CR19","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1007\/s10462-021-10049-5","volume":"55","author":"HT \u00dcnal","year":"2022","unstructured":"\u00dcnal, H.T., Ba\u015f\u00e7ift\u00e7i, F.: Evolutionary design of neural network architectures: a review of three decades of research. Artif. Intell. Rev. 55(3), 1723\u20131802 (2022). https:\/\/doi.org\/10.1007\/s10462-021-10049-5","journal-title":"Artif. Intell. Rev."},{"key":"36_CR20","unstructured":"Yu, T., Zhu, H.: Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)"},{"key":"36_CR21","volume-title":"Dive into Deep Learning","author":"A Zhang","year":"2023","unstructured":"Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into Deep Learning. Cambridge University Press, Cambridge (2023)"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05176-9_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:41Z","timestamp":1757943641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05176-9_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"ISBN":["9783032051752","9783032051769"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05176-9_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"15 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests relevant to this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}