{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:01:56Z","timestamp":1780491716365,"version":"3.54.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100015809","name":"Ministry of Natural Resources of the People's Republic of China","doi-asserted-by":"publisher","award":["2024ZD1003802"],"award-info":[{"award-number":["2024ZD1003802"]}],"id":[{"id":"10.13039\/100015809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271296"],"award-info":[{"award-number":["42271296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018806","name":"Department of Science and Technology of Hubei Province","doi-asserted-by":"publisher","award":["2023BEB040"],"award-info":[{"award-number":["2023BEB040"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10489-026-07317-8","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:54:13Z","timestamp":1780484053000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid CNN-BiLSTM model with squeeze-and-excitation attention and GSABO optimization for rock drilling time prediction in underground mines"],"prefix":"10.1007","volume":"56","author":[{"given":"Ding","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8855-9527","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qizhou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"issue":"5","key":"7317_CR1","first-page":"1060","volume":"14","author":"XS Ge","year":"2018","unstructured":"Ge XS, Yu H, Chen G, Lu X (2018) Smart mine construction based on knowledge engineering and Internet of Things. Int J Performability Eng 14(5):1060\u20131068","journal-title":"Int J Performability Eng"},{"issue":"4","key":"7317_CR2","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/J.ENG.2017.04.024","volume":"3","author":"PG Ranjith","year":"2017","unstructured":"Ranjith PG, Zhao J, Ju M, De Silva RVS, Rathnaweera TD, Bandara AKMS (2017) Opportunities and Challenges in Deep Mining: A Brief Review. Engineering 3(4):546\u2013551","journal-title":"Engineering"},{"issue":"1","key":"7317_CR3","doi-asserted-by":"publisher","first-page":"012106","DOI":"10.1088\/1742-6596\/1574\/1\/012106","volume":"1574","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Sun G (2020) Informationization and Big Data Technology in Management and Maintenance of Mining Equipment in Large Open Pit Mines. J Phys Conf Ser 1574(1):012106","journal-title":"J Phys Conf Ser"},{"key":"7317_CR4","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.4028\/www.scientific.net\/KEM.480-481.1607","volume":"480\u2013481","author":"XS Ge","year":"2011","unstructured":"Ge XS, Zhang YM (2011) From digital mine to smart mine. Key Eng. Mater. 480\u2013481:1607\u20131612","journal-title":"Key Eng. Mater."},{"issue":"01","key":"7317_CR5","first-page":"564","volume":"47","author":"E Ding","year":"2022","unstructured":"Ding E, Yu X, Xia B, Zhao X, Zhang D, Liu Y, Wang W et al (2022) Development of mine informatization and key technologies of intelligent mines. J China Coal Soc 47(01):564\u2013578","journal-title":"J China Coal Soc"},{"issue":"2","key":"7317_CR6","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s10796-014-9489-2","volume":"17","author":"A Whitmore","year":"2015","unstructured":"Whitmore A, Agarwal A, Da Xu L (2015) The Internet of Things-a survey of topics and trends. Inf. Syst. Front. 17(2):261\u2013274","journal-title":"Inf. Syst. Front."},{"issue":"2","key":"7317_CR7","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s12525-021-00464-5","volume":"32","author":"Z Van Veldhoven","year":"2022","unstructured":"Van Veldhoven Z, Vanthienen J (2022) Digital transformation as an interaction-driven perspective between business, society, and technology. Electronic Markets 32(2):629\u2013644","journal-title":"Electronic Markets"},{"issue":"4","key":"7317_CR8","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s42461-019-00103-w","volume":"36","author":"A Young","year":"2019","unstructured":"Young A, Rogers P (2019) A review of digital transformation in mining. Mining, Metallurgy & Exploration 36(4):683\u2013699","journal-title":"Mining, Metallurgy & Exploration"},{"key":"7317_CR9","doi-asserted-by":"crossref","unstructured":"Akbay D, Altindag R (2014) An Investigation of Different Drill Bits and Drilling Angles in Blast Hole Drilling. In: ISRM Regional Symposium - EUROCK 2014, pp 1033\u20131038","DOI":"10.1201\/b16955-178"},{"key":"7317_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108064","volume":"133","author":"N Li","year":"2024","unstructured":"Li N, Liu D, Wang L, Ye H, Wang Q, Yan D, Zhao S (2024) Combination prediction of underground mine rock drilling time based on seasonal and trend decomposition using Loess. Eng Appl Artif Intell 133:108064","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"7317_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-59753-6","volume":"14","author":"S Heydari","year":"2024","unstructured":"Heydari S, Hoseinie SH, Bagherpour R (2024) Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models. Sci Rep 14(1):8928","journal-title":"Sci Rep"},{"key":"7317_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106332","volume":"183","author":"LFFM Barbosa","year":"2019","unstructured":"Barbosa LFFM, Nascimento A, Mathias MH, de Carvalho JA (2019) Machine learning methods applied to drilling rate of penetration prediction and optimization - A review. J Pet Sci Eng 183:106332","journal-title":"J Pet Sci Eng"},{"issue":"11","key":"7317_CR13","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.2118\/408-PA","volume":"14","author":"WC Maurer","year":"1962","unstructured":"Maurer WC (1962) The Perfect - Cleaning Theory of Rotary Drilling. J Pet Technol 14(11):1270\u20131274","journal-title":"J Pet Technol"},{"key":"7317_CR14","unstructured":"Bingham MG (1965) A new approach to interpreting\u2013rock drillability. (No Title)"},{"issue":"04","key":"7317_CR15","doi-asserted-by":"publisher","first-page":"541","DOI":"10.2118\/1520-PA","volume":"19","author":"JR Eckel","year":"1967","unstructured":"Eckel JR (1967) Microbit Studies of the Effect of Fluid Properties and Hydraulics on Drilling Rate. J Pet Technol 19(04):541\u2013546","journal-title":"J Pet Technol"},{"issue":"04","key":"7317_CR16","doi-asserted-by":"publisher","first-page":"371","DOI":"10.2118\/4238-PA","volume":"14","author":"AT Bourgoyne Jr.","year":"1974","unstructured":"Bourgoyne AT Jr., Young FS Jr. (1974) A multiple regression approach to optimal drilling and abnormal pressure detection. Soc Pet Eng J 14(04):371\u2013384","journal-title":"Soc Pet Eng J"},{"key":"7317_CR17","doi-asserted-by":"crossref","unstructured":"Hareland G, Rampersad PR (1994) Drag - Bit Model Including Wear. In: SPE Latin America\/Caribbean Petroleum Engineering Conference","DOI":"10.2118\/26957-MS"},{"key":"7317_CR18","doi-asserted-by":"crossref","unstructured":"Motahhari HR, Hareland G, James JA, Bartlomowicz M (2008) Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters. In: Canadian International Petroleum Conference, pp PETSOC-2008-2132","DOI":"10.2118\/2008-132"},{"issue":"01","key":"7317_CR19","doi-asserted-by":"publisher","first-page":"83","DOI":"10.2118\/13694-PA","volume":"3","author":"EA Al-Betairi","year":"1988","unstructured":"Al-Betairi EA, Moussa MM, Al-Otaibi S (1988) Multiple Regression Approach To Optimize Drilling Operations in the Arabian Gulf Area. SPE Drill Eng 3(01):83\u201388","journal-title":"SPE Drill Eng"},{"issue":"1","key":"7317_CR20","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1061\/(ASCE)1532-3641(2008)8:1(68)","volume":"8","author":"S Akin","year":"2008","unstructured":"Akin S, Karpuz C (2008) Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks. Int J Geomech 8(1):68\u201373","journal-title":"Int J Geomech"},{"issue":"2","key":"7317_CR21","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s13146-016-0291-8","volume":"32","author":"HR Ansari","year":"2017","unstructured":"Ansari HR, Sarbaz Hosseini MJ, Amirpour M (2017) Drilling rate of penetration prediction through committee support vector regression based on imperialist competitive algorithm. Carbonates Evaporites 32(2):205\u2013213","journal-title":"Carbonates Evaporites"},{"issue":"3","key":"7317_CR22","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s10064-017-1192-3","volume":"78","author":"M Darbor","year":"2019","unstructured":"Darbor M, Faramarzi L, Sharifzadeh M (2019) Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network. Bull Eng Geol Environ 78(3):1501\u20131513","journal-title":"Bull Eng Geol Environ"},{"issue":"2","key":"7317_CR23","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12517-018-4185-z","volume":"12","author":"S Elkatatny","year":"2019","unstructured":"Elkatatny S (2019) Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network. Arab J Geosci 12(2):19","journal-title":"Arab J Geosci"},{"key":"7317_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jngse.2021.104104","volume":"95","author":"D Etesami","year":"2021","unstructured":"Etesami D, Zhang WJ, Hadian M (2021) A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks. J Nat Gas Sci Eng 95:104104","journal-title":"J Nat Gas Sci Eng"},{"key":"7317_CR25","doi-asserted-by":"crossref","unstructured":"Gan C, Cao W-H, Wu M, Chen X, Hu Y-L, Liu K-Z, Zhang S-B et al (2019) Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: a case study on the Shennongjia Area, Central China. J Pet Sci Eng 181:106200","DOI":"10.1016\/j.petrol.2019.106200"},{"issue":"1","key":"7317_CR26","volume":"2016","author":"X Shi","year":"2016","unstructured":"Shi X, Liu G, Gong X, Zhang J, Wang J, Zhang H (2016) An efficient approach for real-time prediction of rate of penetration in offshore drilling. Math Probl Eng 2016(1):3575380","journal-title":"Math Probl Eng"},{"key":"7317_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.enggeo.2014.02.006","volume":"173","author":"H Basarir","year":"2014","unstructured":"Basarir H, Tutluoglu L, Karpuz C (2014) Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Eng Geol 173:1\u20139","journal-title":"Eng Geol"},{"key":"7317_CR28","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.enggeo.2017.06.014","volume":"226","author":"AC Adoko","year":"2017","unstructured":"Adoko AC, Gokceoglu C, Yagiz S (2017) Bayesian prediction of TBM penetration rate in rock mass. Eng Geol 226:245\u2013256","journal-title":"Eng Geol"},{"issue":"1","key":"7317_CR29","volume":"2023","author":"Y Ren","year":"2023","unstructured":"Ren Y, Lu B, Zheng S, Bai K, Cheng L, Yan H, Wang G (2023) Research on the rate of penetration prediction method based on stacking ensemble learning. Geofluids 2023(1):6645604","journal-title":"Geofluids"},{"key":"7317_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109402","volume":"138","author":"Y Sun","year":"2024","unstructured":"Sun Y, Tao H, Stojanovic V (2024) Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced samples. Eng Appl Artif Intell 138:109402","journal-title":"Eng Appl Artif Intell"},{"key":"7317_CR31","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.isatra.2024.12.023","volume":"157","author":"Y Sun","year":"2025","unstructured":"Sun Y, Tao H, Stojanovic V (2025) End-to-end multi-scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples. ISA Trans 157:419\u2013433","journal-title":"ISA Trans"},{"key":"7317_CR32","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.ijrefrig.2025.08.016","volume":"179","author":"M Akbari","year":"2025","unstructured":"Akbari M, Bagherzadeh SA, Dehkordi MHR, Naghsh A, Azimy N, Azimy H (2025) Optimizing thermophysical properties of non-Newtonian nano-refrigerants for refrigeration systems using machine learning approaches. Int J Refrig 179:420\u2013431","journal-title":"Int J Refrig"},{"issue":"16","key":"7317_CR33","doi-asserted-by":"publisher","first-page":"8009","DOI":"10.1007\/s10973-022-11827-1","volume":"148","author":"H Azimy","year":"2023","unstructured":"Azimy H, Azimy N, Meghdadi Isfahani AH, Bagherzadeh SA, Farahnakian M (2023) Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting. J Therm Anal Calorim 148(16):8009\u20138023","journal-title":"J Therm Anal Calorim"},{"key":"7317_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.112554","volume":"162","author":"B Zou","year":"2025","unstructured":"Zou B, Xia K, Ma J, Long X (2025) Deep learning driven prediction of dynamic stress-strain response in limestone: insights into transient mechanical behavior under complex loadings for shield tunneling. Eng Appl Artif Intell 162:112554","journal-title":"Eng Appl Artif Intell"},{"key":"7317_CR35","doi-asserted-by":"crossref","unstructured":"Zhang G-H, Yu J-C, Han Z-Y, Li S-L, Dan L-Z, Hua D-J, Jiao Y-Y et al (2025) A rock strength prediction model utilizing real-time data from percussion\u2013rotary drilling measurements. Rock Mech Rock Eng 58(7):7783\u20137803","DOI":"10.1007\/s00603-025-04474-z"},{"issue":"10","key":"7317_CR36","doi-asserted-by":"publisher","first-page":"2414","DOI":"10.1002\/nag.3991","volume":"49","author":"S Sun","year":"2025","unstructured":"Sun S, Jiang Z, Li L, Wang J, Song S (2025) DEM Analysis on Rock-Breaking Impact Effect of Shield Disc Cutter in Typical Soft and Hard Composite Strata. Int J Numer Anal Methods Geomech 49(10):2414\u20132431","journal-title":"Int J Numer Anal Methods Geomech"},{"issue":"17","key":"7317_CR37","doi-asserted-by":"publisher","first-page":"4076","DOI":"10.1002\/nag.70059","volume":"49","author":"S-Q Sun","year":"2025","unstructured":"Sun S-Q, Pan S-G, Li L-P, Li Z-Y, Fan K-R, He S-J (2025) Prediction Model for Rock-Breaking Force and Wear of Large-Diameter Shield Disc Cutters in Hard Rock Stratum. Int J Numer Anal Methods Geomech 49(17):4076\u20134090","journal-title":"Int J Numer Anal Methods Geomech"},{"key":"7317_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2025.107189","volume":"168","author":"Z Zhou","year":"2026","unstructured":"Zhou Z, Yang J, Song J, Liu Y, Gao C, Wang M, Li Y (2026) Research on shield parameter prediction method based on multivariate variable weight combination model. Tunn Undergr Space Technol 168:107189","journal-title":"Tunn Undergr Space Technol"},{"key":"7317_CR39","doi-asserted-by":"crossref","unstructured":"Li LH, Yatskar M, Yin D, Hsieh C-J, Chang K-W (2020) What does BERT with vision look at? In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5265\u20135275","DOI":"10.18653\/v1\/2020.acl-main.469"},{"key":"7317_CR40","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"7317_CR41","doi-asserted-by":"crossref","unstructured":"Letarte G, Paradis F, Gigu\u00e8re P, Laviolette F (2018) Importance of self-attention for sentiment analysis. In: Proceedings of the 2018 EMNLP workshop BlackboxNLP: analyzing and interpreting neural networks for NLP, pp 267\u2013275","DOI":"10.18653\/v1\/W18-5429"},{"issue":"1","key":"7317_CR42","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.32604\/cmc.2023.039274","volume":"76","author":"H Zhang","year":"2023","unstructured":"Zhang H, Yang G, Yu H, Zheng Z (2023) Kalman filter-based CNN-BiLSTM-ATT model for traffic flow prediction. Computers Mater Continua 76(1):1047","journal-title":"Computers Mater Continua"},{"key":"7317_CR43","doi-asserted-by":"publisher","first-page":"102275","DOI":"10.1016\/j.seta.2022.102275","volume":"52","author":"IM Mustaqeem","year":"2022","unstructured":"Mustaqeem IM, Kwon S (2022) A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting. Sustainable Energy Technol Assess 52:102275","journal-title":"Sustainable Energy Technol Assess"},{"issue":"5","key":"7317_CR44","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1177\/00202940231212146","volume":"57","author":"X Liu","year":"2024","unstructured":"Liu X, Chen G, Wang H, Wei X (2024) A Siamese CNN-BiLSTM-based method for unbalance few-shot fault diagnosis of rolling bearings. Meas Control 57(5):551\u2013565","journal-title":"Meas Control"},{"key":"7317_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2021.818833","volume":"12","author":"X Lu","year":"2022","unstructured":"Lu X (2022) Deep learning based emotion recognition and visualization of figural representation. Front Psychol 12:818833","journal-title":"Front Psychol"},{"key":"7317_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109228","volume":"136","author":"F Yuan","year":"2023","unstructured":"Yuan F, Zhang Z, Fang Z (2023) An effective CNN and Transformer complementary network for medical image segmentation. Pattern Recogn 136:109228","journal-title":"Pattern Recogn"},{"issue":"7","key":"7317_CR47","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"issue":"12","key":"7317_CR48","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.3390\/math8122133","volume":"8","author":"S Mustaqeem,Kwon","year":"2020","unstructured":"Mustaqeem,Kwon S (2020) CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network. Mathematics 8(12):2133","journal-title":"Mathematics"},{"key":"7317_CR49","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"7317_CR50","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/biomimetics8020149","volume":"8","author":"P Trojovsk\u00fd","year":"2023","unstructured":"Trojovsk\u00fd P, Dehghani M (2023) Subtraction-average-based optimizer: A new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics 8(2):149","journal-title":"Biomimetics"},{"key":"7317_CR51","volume-title":"A Value for N-Person Games","author":"LS Shapley","year":"1952","unstructured":"Shapley LS (1952) A Value for N-Person Games. RAND Corporation, Santa Monica, CA"},{"key":"7317_CR52","first-page":"4768","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30:4768\u20134777","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"7317_CR53","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2014","unstructured":"\u0160trumbelj E, Kononenko I (2014) Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst 41(3):647\u2013665","journal-title":"Knowl Inf Syst"},{"key":"7317_CR54","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"issue":"18","key":"7317_CR55","doi-asserted-by":"publisher","first-page":"11755","DOI":"10.1007\/s00521-025-11071-2","volume":"37","author":"TH Tran","year":"2025","unstructured":"Tran TH, Nguyen DT, Ngo MD, Doan L, Luong NH, Binh HTT (2025) Kernelshap-nas: a shapley additive explanatory approach for characterizing operation influences. Neural Comput Appl 37(18):11755\u201311771","journal-title":"Neural Comput Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07317-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07317-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07317-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:03:20Z","timestamp":1780488200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07317-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["7317"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07317-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"8 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","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 declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of Competing Interest"}}],"article-number":"288"}}