{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:44:34Z","timestamp":1762609474606,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16185-0","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T05:02:56Z","timestamp":1692248576000},"page":"23563-23597","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SpMV and BiCG-Stab sparse solver on Multi-GPUs for reservoir simulation"],"prefix":"10.1007","volume":"83","author":[{"given":"Mayez","family":"Al-Mouhamed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lutfi","family":"Firdaus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-7319","authenticated-orcid":false,"given":"Ayaz H.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nazeeruddin","family":"Mohammad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"16185_CR1","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/978-3-662-48096-0_46","volume-title":"Euro-Par 2015: Parallel Processing","author":"A Abdelfattah","year":"2015","unstructured":"Abdelfattah A, Ltaief H, Keyes D (2015) High performance multi-gpu spmv for multi-component pde-based applications. In: Tr\u00e4ff JL, Hunold S, Versaci F (eds) Euro-Par 2015: Parallel Processing. Springer, Berlin, Heidelberg, pp 601\u2013612"},{"key":"16185_CR2","doi-asserted-by":"publisher","unstructured":"Abu-Sufah W, Karim AA (2012) An Effective Approach for Implementing Sparse Matrix-Vector Multiplication on Graphics Processing Units. 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, 453\u2013460. https:\/\/doi.org\/10.1109\/HPCC.2012.68","DOI":"10.1109\/HPCC.2012.68"},{"key":"16185_CR3","doi-asserted-by":"publisher","unstructured":"Acosta A, Blanco V, Almeida F (2012) Towards the Dynamic Load Balancing on Heterogeneous Multi-GPU Systems. 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, 646\u2013653. https:\/\/doi.org\/10.1109\/ISPA.2012.96","DOI":"10.1109\/ISPA.2012.96"},{"key":"16185_CR4","doi-asserted-by":"publisher","unstructured":"Ahamed A-KC, Magoules F (2012) Iterative Methods for Sparse Linear Systems on Graphics Processing Unit. 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, 836\u2013842. https:\/\/doi.org\/10.1109\/HPCC.2012.118","DOI":"10.1109\/HPCC.2012.118"},{"issue":"11","key":"16185_CR5","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.1109\/TPDS.2021.3074501","volume":"32","author":"N Ahmad","year":"2021","unstructured":"Ahmad N, Yilmaz B, Unat D (2021) A split execution model for sptrsv. IEEE Trans Parallel Distrib Syst 32(11):2809\u20132822. https:\/\/doi.org\/10.1109\/TPDS.2021.3074501","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"16185_CR6","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.parco.2019.02.005","volume":"85","author":"JI Aliaga","year":"2019","unstructured":"Aliaga JI, Dufrechou E, Ezzatti P, Quintana-Ort\u00ed ES (2019) Accelerating the task\/data-parallel version of ilupack\u2019s bicg in multi-cpu\/gpu configurations. Parallel Comput 85:79\u201387. https:\/\/doi.org\/10.1016\/j.parco.2019.02.005","journal-title":"Parallel Comput"},{"key":"16185_CR7","doi-asserted-by":"publisher","unstructured":"Aliaga J\u00cd, P\u00e9rez J, Qu\u00edntana-Orti ES, Anzt H (2013) Reformulated conjugate gradient for the energy-Aware solution of linear systems on GPUs. Proceedings of the International Conference on Parallel Processing, 320\u2013329. https:\/\/doi.org\/10.1109\/ICPP.2013.41","DOI":"10.1109\/ICPP.2013.41"},{"issue":"9","key":"16185_CR8","doi-asserted-by":"publisher","first-page":"3761","DOI":"10.1007\/s11227-017-1972-3","volume":"73","author":"Mayez Al-Mouhamed","year":"2017","unstructured":"Al-Mouhamed Mayez, Khan Ayaz H (2017) SpMV and BiCG-Stab optimization for a class of hepta-diagonal-sparse matrices on GPU. J Supercomput 73(9):3761\u20133795. https:\/\/doi.org\/10.1007\/s11227-017-1972-3","journal-title":"J Supercomput"},{"key":"16185_CR9","doi-asserted-by":"publisher","unstructured":"Ament M, Knittel G, Weiskopf D, Stra\u00dfer W (2010) A parallel preconditioned conjugate gradient solver for the poisson problem on a multi-GPU platform. Proceedings of the 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing, PDP 2010, 583\u2013592. https:\/\/doi.org\/10.1109\/PDP.2010.51","DOI":"10.1109\/PDP.2010.51"},{"key":"16185_CR10","unstructured":"Anzt H, Tomov S, Dongarra J (2014) Implementing a sparse matrix vector product for the sell-c \/ sell-c- \u03c3 formats on nvidia gpus. Technical Report ut-eecs-14\u2013727, University of Tennessee, url: https:\/\/icl.utk.edu\/publications\/implementing-sparse-matrix-vector-product-sell-csell-c-%CF%83-formats-nvidia-gpus"},{"key":"16185_CR11","doi-asserted-by":"publisher","unstructured":"Bastem B, Unat D (2020) Tiling-based programming model for structured grids on gpu clusters. In: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region. HPCAsia2020, pp. 43\u201351. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3368474.3368485","DOI":"10.1145\/3368474.3368485"},{"key":"16185_CR12","doi-asserted-by":"publisher","unstructured":"Ben-Nun T, Sutton M, Pai S, Pingali K (2020) Groute: Asynchronous multi-gpu programming model with applications to large-scale graph processing. ACM Trans. Parallel Comput. 7(3). https:\/\/doi.org\/10.1145\/3399730","DOI":"10.1145\/3399730"},{"key":"16185_CR13","doi-asserted-by":"publisher","first-page":"109189","DOI":"10.1016\/j.jcp.2019.109189","volume":"406","author":"AN Bocharov","year":"2020","unstructured":"Bocharov AN, Evstigneev NM, Petrovskiy VP, Ryabkov OI, Teplyakov IO (2020) Implicit method for the solution of supersonic and hypersonic 3d flow problems with lower-upper symmetric-gauss-seidel preconditioner on multiple graphics processing units. J Comput Phys 406:109189. https:\/\/doi.org\/10.1016\/j.jcp.2019.109189","journal-title":"J Comput Phys"},{"issue":"1\u20132","key":"16185_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s00450-010-0112-6","volume":"25","author":"A Cevahir","year":"2010","unstructured":"Cevahir A, Nukada A, Matsuoka S (2010) High performance conjugate gradient solver on multi-GPU clusters using hypergraph partitioning. Comput Sci Res Dev 25(1\u20132):83\u201391. https:\/\/doi.org\/10.1007\/s00450-010-0112-6","journal-title":"Comput Sci Res Dev"},{"key":"16185_CR15","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhao Y, Zhao W, Zhao L (2013) A Comparative Study of Preconditioners for GPU-Accelerated Conjugate Gradient Solver. 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, 628\u2013635. https:\/\/doi.org\/10.1109\/HPCC.and.EUC.2013.94","DOI":"10.1109\/HPCC.and.EUC.2013.94"},{"key":"16185_CR16","unstructured":"Cooperative Groups. https:\/\/developer.nvidia.com\/blog\/cooperative-groups\/. Accessed 15 Aug 2023"},{"key":"16185_CR17","unstructured":"Developing a Linux Kernel Module using GPUDirect RDMA. https:\/\/docs.nvidia.com\/cuda\/gpudirect-rdma\/. Accessed 15 Aug 2023"},{"issue":"2","key":"16185_CR18","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1016\/j.jpdc.2013.10.002","volume":"74","author":"J Gao","year":"2014","unstructured":"Gao J, Liang R, Wang J (2014) Research on the conjugate gradient algorithm with a modified incomplete Cholesky preconditioner on GPU. J Parallel Distrib Comput 74(2):2088\u20132098. https:\/\/doi.org\/10.1016\/j.jpdc.2013.10.002","journal-title":"J Parallel Distrib Comput"},{"key":"16185_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.parco.2017.04.003","volume":"63","author":"J Gao","year":"2017","unstructured":"Gao J, Zhou Y, He G, Xia Y (2017) A multi-gpu parallel optimization model for the preconditioned conjugate gradient algorithm. Parallel Comput 63:1\u201316. https:\/\/doi.org\/10.1016\/j.parco.2017.04.003","journal-title":"Parallel Comput"},{"issue":"7","key":"16185_CR20","doi-asserted-by":"publisher","first-page":"3607","DOI":"10.1109\/TAP.2013.2258882","volume":"61","author":"J Guan","year":"2013","unstructured":"Guan J, Yan S, Jin JM (2013) An OpenMP-CUDA implementation of multilevel fast multipole algorithm for electromagnetic simulation on multi-GPU computing systems. IEEE Trans Antennas Propag 61(7):3607\u20133616. https:\/\/doi.org\/10.1109\/TAP.2013.2258882","journal-title":"IEEE Trans Antennas Propag"},{"key":"16185_CR21","doi-asserted-by":"publisher","unstructured":"Guo P, Zhang C (2016) Performance optimization for spmv on multi-gpu systems using threads and multiple streams. In: 2016 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW), pp. 67\u201372. https:\/\/doi.org\/10.1109\/SBAC-PADW.2016.20","DOI":"10.1109\/SBAC-PADW.2016.20"},{"key":"16185_CR22","doi-asserted-by":"publisher","unstructured":"Hermann E, Raffin B, Faure F, Gautier T, Allard J (2010) Multi-GPU and multi-CPU parallelization for interactive physics simulations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6272 LNCS(PART 2), 235\u2013246. https:\/\/doi.org\/10.1007\/978-3-642-15291-7_23","DOI":"10.1007\/978-3-642-15291-7_23"},{"key":"16185_CR23","doi-asserted-by":"publisher","unstructured":"Jradi WAR, Dantas do Nascimento HA, Santos Martins W (2018) A fast and generic gpu-based parallel reduction implementation. In: 2018 Symposium on High Performance Computing Systems (WSCAD), pp. 16\u201322. https:\/\/doi.org\/10.1109\/WSCAD.2018.00013","DOI":"10.1109\/WSCAD.2018.00013"},{"key":"16185_CR24","doi-asserted-by":"publisher","DOI":"10.2118\/141265-MS","author":"H Klie","year":"2011","unstructured":"Klie H, Sudan H, Li R (2011) Exploiting Capabilities of Many Core Platforms in Reservoir Simulation. SPE Reserv Simul. https:\/\/doi.org\/10.2118\/141265-MS","journal-title":"SPE Reserv Simul"},{"key":"16185_CR25","doi-asserted-by":"publisher","unstructured":"Li A, van den Braak G-J, Corporaal H, Kumar A (2015) Fine-grained synchronizations and dataflow programming on gpus. In: Proceedings of the 29th ACM on International Conference on Supercomputing. ICS \u201915, pp. 109\u2013118. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2751205.2751232","DOI":"10.1145\/2751205.2751232"},{"issue":"2","key":"16185_CR26","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s11227-012-0825-3","volume":"63","author":"R Li","year":"2013","unstructured":"Li R, Saad Y (2013) GPU-accelerated preconditioned iterative linear solvers. J Supercomput 63(2):443\u2013466. https:\/\/doi.org\/10.1007\/s11227-012-0825-3","journal-title":"J Supercomput"},{"issue":"1","key":"16185_CR27","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/TPDS.2019.2928289","volume":"31","author":"A Li","year":"2020","unstructured":"Li A, Song SL, Chen J, Li J, Liu X, Tallent NR, Barker KJ (2020) Evaluating modern gpu interconnect: Pcie, nvlink, nv-sli, nvswitch and gpudirect. IEEE Trans Parallel Distrib Syst 31(1):94\u2013110","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"16185_CR28","unstructured":"Liu Y (2014) Faster GPU-based sparse matrix-vector multiplication. https:\/\/lightspmv.sourceforge.net\/homepage.htm#latest. Accessed 15 Aug 2023"},{"key":"16185_CR29","doi-asserted-by":"publisher","unstructured":"Liu Y, Schmidt B (2015) Lightspmv: Faster csr-based sparse matrix-vector multiplication on cuda-enabled gpus. In: 2015 IEEE 26th International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 82\u201389. https:\/\/doi.org\/10.1109\/ASAP.2015.7245713","DOI":"10.1109\/ASAP.2015.7245713"},{"key":"16185_CR30","doi-asserted-by":"publisher","unstructured":"Liu W, Vinter B (2015) Csr5: An efficient storage format for cross-platform sparse matrix-vector multiplication. In: Proceedings of the 29th ACM on International Conference on Supercomputing. ICS \u201915, pp. 339\u2013350. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2751205.2751209","DOI":"10.1145\/2751205.2751209"},{"issue":"July 2016","key":"16185_CR31","first-page":"355","volume":"3","author":"N Lopes","year":"2011","unstructured":"Lopes N, Ribeiro B (2011) GPUMLib: An Efficient Open-source GPU Machine Learning Library. J Comput Inf Syst 3(July 2016):355\u2013362","journal-title":"J Comput Inf Syst"},{"key":"16185_CR32","doi-asserted-by":"publisher","unstructured":"Mak J, Choboter P, Lupo C (2011) Numerical ocean modeling and simulation with CUDA. OCEANS\u201911 MTS\/IEEE KONA, Waikoloa, pp 1\u20136. https:\/\/doi.org\/10.23919\/OCEANS.2011.6107199","DOI":"10.23919\/OCEANS.2011.6107199"},{"issue":"1","key":"16185_CR33","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TPDS.2016.2549523","volume":"28","author":"X Mei","year":"2017","unstructured":"Mei X, Chu X (2017) Dissecting gpu memory hierarchy through microbenchmarking. IEEE Trans Parallel Distrib Syst 28(1):72\u201386. https:\/\/doi.org\/10.1109\/TPDS.2016.2549523","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"16185_CR34","unstructured":"Micikevicius P. Multi-GPU Programming. https:\/\/www.nvidia.com\/docs\/io\/116711\/sc11-multi-gpu.pdf. Accessed 15 Aug 2023"},{"key":"16185_CR35","unstructured":"Micikevicius P (2011) Multi-GPU programming. GPU Computing Webinars, NVIDIA, url: https:\/\/developer.download.nvidia.com\/CUDA\/training\/cuda_webinars_multi_gpu.pdf"},{"key":"16185_CR36","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717952","volume-title":"Numerical Computing with MATLAB","author":"CB Moler","year":"2004","unstructured":"Moler CB (2004) Numerical Computing with MATLAB. SIAM, Massachusets"},{"issue":"April","key":"16185_CR37","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1365490.1365500","volume":"6","author":"J Nickolls","year":"2008","unstructured":"Nickolls J, Buck I, Garland M, Skadron K (2008) Scalable parallel programming with CUDA. AMC Queue 6(April):40\u201353. https:\/\/doi.org\/10.1145\/1365490.1365500","journal-title":"AMC Queue"},{"key":"16185_CR38","unstructured":"NVIDIA GPUDirect. https:\/\/developer.nvidia.com\/gpudirect. Accessed 15 Aug 2023"},{"issue":"5","key":"16185_CR39","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/JPROC.2008.917757","volume":"96","author":"JD Owens","year":"2008","unstructured":"Owens JD, Houston M, Luebke D, Green S, Stone JE, Phillips JC (2008) GPU computing. Proc IEEE 96(5):879\u2013899. https:\/\/doi.org\/10.1109\/JPROC.2008.917757","journal-title":"Proc IEEE"},{"key":"16185_CR40","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718003","volume-title":"Iterative Methods for Sparse Linear Systems","author":"Y Saad","year":"2003","unstructured":"Saad Y (2003) Iterative Methods for Sparse Linear Systems. SIAM, Minnesota"},{"key":"16185_CR41","doi-asserted-by":"publisher","unstructured":"Schaetz S, Uecker M (2012) A multi-GPU programming library for real-time applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7439 LNCS(PART 1), 114\u2013128 https:\/\/arxiv.org\/abs\/1301.1215arXiv:1301.1215. https:\/\/doi.org\/10.1007\/978-3-642-33078-0_9","DOI":"10.1007\/978-3-642-33078-0_9"},{"key":"16185_CR42","doi-asserted-by":"publisher","unstructured":"Sourouri M, Gillberg T, Baden SB, Cai X (2014) Effective multi-GPU communication using multiple CUDA streams and threads. Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS 2015-April, 981\u2013986. https:\/\/doi.org\/10.1109\/PADSW.2014.7097919","DOI":"10.1109\/PADSW.2014.7097919"},{"key":"16185_CR43","doi-asserted-by":"publisher","unstructured":"Steinberger M, Derlery A, Zayer R, Seidel H-P (2016) How naive is naive spmv on the gpu? In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1\u20138. https:\/\/doi.org\/10.1109\/HPEC.2016.7761634","DOI":"10.1109\/HPEC.2016.7761634"},{"key":"16185_CR44","unstructured":"Technology P (n.d.) Product Brief: PEX 8747, PCI Express Gen 3 Switch, 48 Lanes, 5 Ports. https:\/\/docs.broadcom.com\/doc\/12351854. Accessed 15 Aug 2023"},{"issue":"2","key":"16185_CR45","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s11227-010-0468-1","volume":"59","author":"JC Thibault","year":"2012","unstructured":"Thibault JC, Senocak I (2012) Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms. J Supercomput 59(2):693\u2013719. https:\/\/doi.org\/10.1007\/s11227-010-0468-1","journal-title":"J Supercomput"},{"key":"16185_CR46","doi-asserted-by":"crossref","unstructured":"Tiwari M, Vadhiyar S (2022) Strategies for\u00c2 efficient execution of\u00c2 pipelined conjugate gradient method on\u00c2 gpu systems. In: Anzt H, Bienz A, Luszczek P, Baboulin M (eds.) High Performance Computing. ISC High Performance 2022 International Workshops, pp. 77\u201389. Springer, Cham","DOI":"10.1007\/978-3-031-23220-6_6"},{"key":"16185_CR47","doi-asserted-by":"publisher","unstructured":"Torres R, Ferrer R, Teruel X (2022) A novel set of directives for multi-device programming with openmp. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 401\u2013410. https:\/\/doi.org\/10.1109\/IPDPSW55747.2022.00075","DOI":"10.1109\/IPDPSW55747.2022.00075"},{"key":"16185_CR48","doi-asserted-by":"publisher","unstructured":"Xie C, Chen J, Firoz J, Li J, Song SL, Barker K, Raugas M, Li A (2021) Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3472456.3472478","DOI":"10.1145\/3472456.3472478"},{"key":"16185_CR49","doi-asserted-by":"publisher","unstructured":"Yang C, Bulu\u00e7 A, Owens JD (2018) Design principles for sparse matrix multiplication on the gpu. In: Euro-Par 2018: Parallel Processing: 24th International Conference on Parallel and Distributed Computing, Turin, Italy, August 27 - 31, 2018, Proceedings, pp. 672\u2013687. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-319-96983-1_48","DOI":"10.1007\/978-3-319-96983-1_48"},{"issue":"8","key":"16185_CR50","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3155284.3018755","volume":"52","author":"X Zhang","year":"2017","unstructured":"Zhang X, Tan G, Xue S, Li J, Zhou K, Chen M (2017) Understanding the gpu microarchitecture to achieve bare-metal performance tuning. SIGPLAN Not 52(8):31\u201343. https:\/\/doi.org\/10.1145\/3155284.3018755","journal-title":"SIGPLAN Not"},{"key":"16185_CR51","unstructured":"Zhang L, Wahib M, Chen P, Meng J, Wang X, Toshio E, Matsuoka S (2023) ArXiv:2204.02064v2 [CS.DC] 21 may 2022. https:\/\/arxiv.org\/pdf\/2204.02064. Accessed 15 Aug 2023"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16185-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16185-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16185-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T14:04:05Z","timestamp":1708869845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16185-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":51,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16185"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16185-0","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"assertion":[{"value":"8 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not Applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}