{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:16:46Z","timestamp":1768094206144,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665785","type":"print"},{"value":"9789819665792","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-6579-2_17","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T12:07:12Z","timestamp":1750680432000},"page":"242-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Wave-RVFL: A Randomized Neural Network Based on\u00a0Wave Loss Function"],"prefix":"10.1007","author":[{"given":"M.","family":"Sajid","sequence":"first","affiliation":[]},{"given":"A.","family":"Quadir","sequence":"additional","affiliation":[]},{"given":"M.","family":"Tanveer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Adam, S.P., Alexandropoulos, S.A.N., Pardalos, P.M., Vrahatis, M.N.: No free lunch theorem: a review. In: Approximation and Optimization: Algorithms, Complexity and Applications, pp. 57\u201382 (2019)","DOI":"10.1007\/978-3-030-12767-1_5"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Akhtar, M., Tanveer, M., Arshad, M., Alzheimer\u2019s Disease Neuroimaging Initiative.: Advancing supervised learning with the wave loss function: a robust and smooth approach. Pattern Recogn. 155, 110637 (2024)","DOI":"10.1016\/j.patcog.2024.110637"},{"key":"17_CR3","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR4","unstructured":"Dua, D., Graff, C.: UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml (2017)"},{"issue":"9","key":"17_CR5","doi-asserted-by":"publisher","first-page":"11671","DOI":"10.1109\/TNNLS.2024.3353531","volume":"35","author":"MA Ganaie","year":"2024","unstructured":"Ganaie, M.A., Sajid, M., Malik, A.K., Tanveer, M.: Graph embedded intuitionistic fuzzy random vector functional link neural network for class imbalance learning. IEEE Trans. Neural Netw. Learn. Syst. 35(9), 11671\u201311680 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1\u20133","key":"17_CR6","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"11","key":"17_CR7","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1109\/TCSVT.2013.2269774","volume":"23","author":"A Iosifidis","year":"2013","unstructured":"Iosifidis, A., Tefas, A., Pitas, I.: Minimum class variance extreme learning machine for human action recognition. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1968\u20131979 (2013)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/TCYB.2015.2401973","volume":"46","author":"A Iosifidis","year":"2015","unstructured":"Iosifidis, A., Tefas, A., Pitas, I.: Graph embedded extreme learning machine. IEEE Trans. Cybern. 46(1), 311\u2013324 (2015)","journal-title":"IEEE Trans. Cybern."},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Jain, A., et al.: LSTMSE-Net: Long short term speech enhancement network for audio-visual speech enhancement. In: INTERSPEECH 2024 (2024). https:\/\/arxiv.org\/abs\/2409.02266","DOI":"10.21437\/AVSEC.2024-8"},{"key":"17_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)"},{"key":"17_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106507","volume":"211","author":"X Li","year":"2021","unstructured":"Li, X., Yang, Y., Hu, N., Cheng, Z., Cheng, J.: Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis. Knowl.-Based Syst. 211, 106507 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"17_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119951","volume":"657","author":"M Lu","year":"2024","unstructured":"Lu, M., Xu, X.: TRNN: an efficient time-series recurrent neural network for stock price prediction. Inf. Sci. 657, 119951 (2024)","journal-title":"Inf. Sci."},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Malik, A.K., Ganaie, M.A.,\u00a0Tanveer, M.: Graph embedded intuitionistic fuzzy weighted random vector functional link network. In: 2022 IEEE Symposium Series on Computational Intelligence, pp. 293\u2013299. IEEE (2022)","DOI":"10.1109\/SSCI51031.2022.10022212"},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110377","volume":"143","author":"AK Malik","year":"2023","unstructured":"Malik, A.K., Gao, R., Ganaie, M.A., Tanveer, M., Suganthan, P.N.: Random vector functional link network: Recent developments, applications, and future directions. Appl. Soft Comput. 143, 110377 (2023)","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"17_CR15","doi-asserted-by":"publisher","first-page":"4754","DOI":"10.1109\/TCSS.2022.3146974","volume":"11","author":"AK Malik","year":"2024","unstructured":"Malik, A.K., Ganaie, M.A., Tanveer, M., Suganthan, P.N.: Alzheimer\u2019s disease diagnosis via intuitionistic fuzzy random vector functional link network. IEEE Trans. Comput. Social Syst. 11(4), 4754\u20134765 (2024)","journal-title":"IEEE Trans. Comput. Social Syst."},{"key":"17_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.112005","volume":"164","author":"U Mishra","year":"2024","unstructured":"Mishra, U., Gupta, D., Hazarika, B.B.: An efficient angle-based twin random vector functional link classifier. Appl. Soft Comput. 164, 112005 (2024)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"17_CR17","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","volume":"6","author":"YH Pao","year":"1994","unstructured":"Pao, Y.H., Park, G.H., Sobajic, D.J.: Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163\u2013180 (1994)","journal-title":"Neurocomputing"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Quadir, A., Sajid, M., \u00a0Tanveer, M.: Multiview random vector functional link network for predicting DNA-binding proteins (2024). https:\/\/arxiv.org\/abs\/2409.02588","DOI":"10.2139\/ssrn.4998565"},{"issue":"8","key":"17_CR19","doi-asserted-by":"publisher","first-page":"4460","DOI":"10.1109\/TFUZZ.2024.3400898","volume":"32","author":"M Sajid","year":"2024","unstructured":"Sajid, M., Malik, A.K., Tanveer, M.: Intuitionistic fuzzy broad learning system: enhancing robustness against noise and outliers. IEEE Trans. Fuzzy Syst. 32(8), 4460\u20134469 (2024)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"5","key":"17_CR20","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.1109\/TFUZZ.2024.3359652","volume":"32","author":"M Sajid","year":"2024","unstructured":"Sajid, M., Malik, A.K., Tanveer, M., Suganthan, P.N.: Neuro-fuzzy random vector functional link neural network for classification and regression problems. IEEE Trans. Fuzzy Syst. 32(5), 2738\u20132749 (2024)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"5","key":"17_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1546","volume":"14","author":"M Sajid","year":"2024","unstructured":"Sajid, M., Sharma, R., Beheshti, I., Tanveer, M.: Decoding cognitive health using machine learning: a comprehensive evaluation for diagnosis of significant memory concern. WIREs Data Min. Knowl. Discovery 14(5), e1546 (2024). https:\/\/doi.org\/10.1002\/widm.1546","journal-title":"WIREs Data Min. Knowl. Discovery"},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Sajid, M., \u00a0Tanveer, M., Suganthan, P.N.: Ensemble deep random vector functional link neural network based on fuzzy inference system. In: IEEE Transactions on Fuzzy Systems, pp. 1\u201312 (2024d). https:\/\/doi.org\/10.1109\/TFUZZ.2024.3411614","DOI":"10.1109\/TFUZZ.2024.3411614"},{"issue":"3","key":"17_CR23","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TCDS.2023.3254209","volume":"15","author":"R Sharma","year":"2023","unstructured":"Sharma, R., Goel, T., Tanveer, M., Lin, C.T., Murugan, R.: Deep-learning-based diagnosis and prognosis of Alzheimer\u2019s disease: a comprehensive review. IEEE Trans. Cogn. Develop. Syst. 15(3), 1123\u20131138 (2023)","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"key":"17_CR24","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.asoc.2018.07.013","volume":"70","author":"PN Suganthan","year":"2018","unstructured":"Suganthan, P.N.: On non-iterative learning algorithms with closed-form solution. Appl. Soft Comput. 70, 1078\u20131082 (2018)","journal-title":"Appl. Soft Comput."},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Tanveer, M., et al.: Ensemble deep learning for Alzheimer\u2019s disease characterization and estimation. Nature Mental Health 2, 655\u2013667 (2024a). https:\/\/doi.org\/10.1038\/s44220-024-00237-x","DOI":"10.1038\/s44220-024-00237-x"},{"key":"17_CR26","doi-asserted-by":"publisher","unstructured":"Tanveer, M., et\u00a0al.: Fuzzy deep learning for the diagnosis of Alzheimer\u2019s disease: Approaches and challenges. IEEE Trans. Fuzzy Syst. 1\u201320 (2024b). https:\/\/doi.org\/10.1109\/TFUZZ.2024.3409412","DOI":"10.1109\/TFUZZ.2024.3409412"},{"issue":"11","key":"17_CR27","doi-asserted-by":"publisher","first-page":"7069","DOI":"10.1016\/j.jfranklin.2020.05.027","volume":"357","author":"K Wang","year":"2020","unstructured":"Wang, K., Pei, H., Cao, J., Zhong, P.: Robust regularized extreme learning machine for regression with non-convex loss function via DC program. J. Franklin Inst. 357(11), 7069\u20137091 (2020)","journal-title":"J. Franklin Inst."},{"key":"17_CR28","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s40745-020-00253-5","volume":"9","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Ma, Y., Zhao, K., Tian, Y.: A comprehensive survey of loss functions in machine learning. Ann. Data Sci. 9, 187\u2013212 (2020)","journal-title":"Ann. Data Sci."},{"key":"17_CR29","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","volume":"367","author":"L Zhang","year":"2016","unstructured":"Zhang, L., Suganthan, P.N.: A comprehensive evaluation of random vector functional link networks. Inf. Sci. 367, 1094\u20131105 (2016)","journal-title":"Inf. Sci."},{"key":"17_CR30","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2019.01.007","volume":"112","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Wu, J., Cai, Z., Du, B., Philip, S.Y.: An unsupervised parameter learning model for RVFL neural network. Neural Netw. 112, 85\u201397 (2019)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6579-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T12:07:14Z","timestamp":1750680434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6579-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665785","9789819665792"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6579-2_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}