{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:22:32Z","timestamp":1776939752476,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819983872","type":"print"},{"value":"9789819983889","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8388-9_31","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T16:02:21Z","timestamp":1701014541000},"page":"379-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Integrated Federated Learning and Meta-Learning Approach for Mining Operations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2128-7576","authenticated-orcid":false,"given":"Venkat","family":"Munagala","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9540-3994","authenticated-orcid":false,"given":"Sankhya","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7848-9008","authenticated-orcid":false,"given":"Srikanth","family":"Thudumu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0143-3812","authenticated-orcid":false,"given":"Irini","family":"Logothetis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2091-8233","authenticated-orcid":false,"given":"Sushil","family":"Bhandari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Bhandari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-5166","authenticated-orcid":false,"given":"Kon","family":"Mouzakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4805-1467","authenticated-orcid":false,"given":"Rajesh","family":"Vasa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"issue":"7","key":"31_CR1","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476\u20135497 (2020)","journal-title":"IEEE Internet Things J."},{"key":"31_CR2","unstructured":"Arambakam, M., Beel, J.: Federated meta-learning: democratizing algorithm selection across disciplines and software libraries. In: 7th ICML Workshop on Automated Machine Learning (AutoML) (2020)"},{"key":"31_CR3","doi-asserted-by":"publisher","first-page":"4369","DOI":"10.1007\/s10064-020-01834-7","volume":"79","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani, D.J., Koopialipoor, M., Bahri, M., Hasanipanah, M., Tahir, M.: A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bull. Eng. Geol. Env. 79, 4369\u20134385 (2020)","journal-title":"Bull. Eng. Geol. Env."},{"key":"31_CR4","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.jafrearsci.2017.07.024","volume":"134","author":"N Bilim","year":"2017","unstructured":"Bilim, N., \u00c7elik, A., Keke\u00e7, B.: A study in cost analysis of aggregate production as depending on drilling and blasting design. J. Afr. Earth Sci. 134, 564\u2013572 (2017)","journal-title":"J. Afr. Earth Sci."},{"key":"31_CR5","first-page":"374","volume":"1","author":"K Bonawitz","year":"2019","unstructured":"Bonawitz, K., et al.: Towards federated learning at scale: system design. Proc. Mach. Learn. Syst. 1, 374\u2013388 (2019)","journal-title":"Proc. Mach. Learn. Syst."},{"issue":"10","key":"31_CR6","doi-asserted-by":"publisher","first-page":"5269","DOI":"10.3390\/app12105269","volume":"12","author":"NS Chandrahas","year":"2022","unstructured":"Chandrahas, N.S., Choudhary, B.S., Teja, M.V., Venkataramayya, M., Prasad, N.K.: XG boost algorithm to simultaneous prediction of rock fragmentation and induced ground vibration using unique blast data. Appl. Sci. 12(10), 5269 (2022)","journal-title":"Appl. Sci."},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Dong, F., et al.: PADP-FedMeta: a personalized and adaptive differentially private federated meta-learning mechanism for AIoT. J. Syst. Archit. 134, 102754 (2023)","DOI":"10.1016\/j.sysarc.2022.102754"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Feurer, M., Hutter, F.: Hyperparameter optimization. Autom. Mach. Learn. Methods Syst. Challenges 3\u201333 (2019)","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"31_CR9","unstructured":"Hard, A., et al.: Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)"},{"issue":"9","key":"31_CR10","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning: Methods, Systems, Challenges. Springer Nature, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5","DOI":"10.1007\/978-3-030-05318-5"},{"key":"31_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrmms.2020.104595","volume":"138","author":"AI Lawal","year":"2021","unstructured":"Lawal, A.I.: A new modification to the Kuz-Ram model using the fragment size predicted by image analysis. Int. J. Rock Mech. Min. Sci. 138, 104595 (2021)","journal-title":"Int. J. Rock Mech. Min. Sci."},{"issue":"1","key":"31_CR13","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.jrmge.2020.05.010","volume":"13","author":"AI Lawal","year":"2021","unstructured":"Lawal, A.I., Kwon, S.: Application of artificial intelligence to rock mechanics: an overview. J. Rock Mech. Geotech. Eng. 13(1), 248\u2013266 (2021)","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: meta-learning for domain generalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"issue":"3","key":"31_CR15","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020). https:\/\/doi.org\/10.1109\/MSP.2020.2975749","journal-title":"IEEE Signal Process. Mag."},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Li, W., Wang, S.: Federated meta-learning for spatial-temporal prediction. Neural Comput. Appl. 34(13), 10355\u201310374 (2022). https:\/\/doi.org\/10.1007\/s00521-021-06861-3","DOI":"10.1007\/s00521-021-06861-3"},{"key":"31_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.mineng.2018.12.004","volume":"132","author":"JT McCoy","year":"2019","unstructured":"McCoy, J.T., Auret, L.: Machine learning applications in minerals processing: a review. Miner. Eng. 132, 95\u2013109 (2019)","journal-title":"Miner. Eng."},{"key":"31_CR18","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"31_CR19","unstructured":"McMahan, H.B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 (2017)"},{"key":"31_CR20","unstructured":"Mitchell, T.M., et al.: Machine Learning, vol. 1. McGraw-Hill, New York (2007)"},{"issue":"5","key":"31_CR21","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.ijrmms.2011.04.005","volume":"48","author":"M Monjezi","year":"2011","unstructured":"Monjezi, M., Khoshalan, H.A., Varjani, A.Y.: Optimization of open pit blast parameters using genetic algorithm. Int. J. Rock Mech. Min. Sci. 48(5), 864\u2013869 (2011)","journal-title":"Int. J. Rock Mech. Min. Sci."},{"issue":"13","key":"31_CR22","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","volume":"375","author":"Z Obermeyer","year":"2016","unstructured":"Obermeyer, Z., Emanuel, E.J.: Predicting the future-big data, machine learning, and clinical medicine. N. Engl. J. Med. 375(13), 1216 (2016)","journal-title":"N. Engl. J. Med."},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 399\u2013410. IEEE (2016)","DOI":"10.1109\/DSAA.2016.49"},{"key":"31_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107872","volume":"113","author":"D Po\u0142ap","year":"2021","unstructured":"Po\u0142ap, D., Wo\u017aniak, M.: Meta-heuristic as manager in federated learning approaches for image processing purposes. Appl. Soft Comput. 113, 107872 (2021)","journal-title":"Appl. Soft Comput."},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Qi, C.C.: Big data management in the mining industry. Int. J. Miner. Metall. Mater. 27(2), 131\u2013139 (2020)","DOI":"10.1007\/s12613-019-1937-z"},{"key":"31_CR26","doi-asserted-by":"publisher","unstructured":"Qiu, Y., Zhou, J., Khandelwal, M., et al.: Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost, and BO-XGBoost models to predict blast-induced ground vibration. Eng. Comput. 38(5), 4145\u20134162 (2022). https:\/\/doi.org\/10.1007\/s00366-021-01393-9","DOI":"10.1007\/s00366-021-01393-9"},{"key":"31_CR27","unstructured":"Raina, A.K., Murthy, V., Soni, A.K.: Flyrock in surface mine blasting: understanding the basics to develop a predictive regime. Current Sci. 660\u2013665 (2015)"},{"issue":"4","key":"31_CR28","first-page":"607","volume":"36","author":"WP Rogers","year":"2019","unstructured":"Rogers, W.P., et al.: Automation in the mining industry: review of technology, systems, human factors, and political risk. Min. Metall. Explor. 36(4), 607\u2013631 (2019)","journal-title":"Min. Metall. Explor."},{"issue":"3","key":"31_CR29","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660\u2013674 (1991)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Sawmliana, C., Hembram, P., Singh, R.K., Banerjee, S., Singh, P., Roy, P.P.: An investigation to assess the cause of accident due to flyrock in an opencast coal mine: a case study. J. Inst. Eng. (India) Ser. D 101, 15\u201326 (2020)","DOI":"10.1007\/s40033-020-00215-4"},{"issue":"4","key":"31_CR31","first-page":"66","volume":"5","author":"T Sevelka","year":"2022","unstructured":"Sevelka, T.: Preventing the potentially deadly consequences of flyrock: mandatory minimum setbacks and separation distances required. J. Nat. Resour. 5(4), 66\u201398 (2022)","journal-title":"J. Nat. Resour."},{"key":"31_CR32","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s10706-015-9869-5","volume":"33","author":"R Trivedi","year":"2015","unstructured":"Trivedi, R., Singh, T., Gupta, N.: Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech. Geol. Eng. 33, 875\u2013891 (2015)","journal-title":"Geotech. Geol. Eng."},{"issue":"5","key":"31_CR33","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.jrmge.2014.07.003","volume":"6","author":"R Trivedi","year":"2014","unstructured":"Trivedi, R., Singh, T., Raina, A.: Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J. Rock Mech. Geotech. Eng. 6(5), 447\u2013454 (2014)","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"31_CR34","unstructured":"Vanschoren, J.: Meta-learning: a survey. arXiv preprint arXiv:1810.03548 (2018)"},{"issue":"2","key":"31_CR35","first-page":"52","volume":"1","author":"L Wang","year":"2016","unstructured":"Wang, L., Alexander, C.A.: Machine learning in big data. Int. J. Math. Eng. Manag. Sci. 1(2), 52\u201361 (2016)","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"issue":"5","key":"31_CR36","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1109\/JSAC.2022.3143259","volume":"40","author":"S Yue","year":"2022","unstructured":"Yue, S., Ren, J., Xin, J., Zhang, D., Zhang, Y., Zhuang, W.: Efficient federated meta-learning over multi-access wireless networks. IEEE J. Sel. Areas Commun. 40(5), 1556\u20131570 (2022)","journal-title":"IEEE J. Sel. Areas Commun."}],"container-title":["Lecture Notes in Computer Science","AI 2023: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8388-9_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:55:55Z","timestamp":1710356155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8388-9_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819983872","9789819983889"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8388-9_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}