{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:31:35Z","timestamp":1767141095403,"version":"build-2238731810"},"update-to":[{"DOI":"10.1007\/s00521-012-0983-5","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T00:00:00Z","timestamp":1375315200000},"record-id":"18695"}],"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2012,6,12]],"date-time":"2012-06-12T00:00:00Z","timestamp":1339459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2013,8]]},"DOI":"10.1007\/s00521-012-0983-5","type":"journal-article","created":{"date-parts":[[2012,6,11]],"date-time":"2012-06-11T00:22:54Z","timestamp":1339374174000},"page":"567-567","source":"Crossref","is-referenced-by-count":4,"title":["RETRACTED ARTICLE: Evolving artificial neural network and imperialist competitive algorithm for prediction permeability of the reservoir"],"prefix":"10.1007","volume":"23","author":[{"given":"Mohammad Ali","family":"Ahmadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Reza","family":"Ahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Reza","family":"Shadizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,6,12]]},"reference":[{"key":"983_CR1","doi-asserted-by":"crossref","unstructured":"Weldu T, Ghedan S, Al-Farisi O (2010) Hybrid AI and conventional empirical model for improved prediction of Log-derived permeability of heterogeneous Carbonate reservoir. SPE production and operation conference, Tunis, 8\u201310 June, SPE No.136127","DOI":"10.2118\/136127-MS"},{"key":"983_CR2","unstructured":"Mohaghegh S, Arefi R, Ameri S, Rose D (1994) Design and development of an artificial neural network for estimation of formation permeability. In SPE 28237, petroleum computer conference, July 31\u2013August 3, Dallas"},{"key":"983_CR3","doi-asserted-by":"crossref","unstructured":"Balan B, Mohaghegh S, Ameri S (1995) State-of-the-art in permeability determination from well log data: part I-a comparative study, model development. In: SPE Eastern regional conference and exhibition, West Virginia, pp 17\u201321","DOI":"10.2118\/30978-MS"},{"issue":"5","key":"983_CR4","first-page":"18","volume":"67","author":"J Wiener","year":"1995","unstructured":"Wiener J (1995) Predict permeability from wireline logs using neural networks. Petroleum Eng Int 67(5):18\u201324","journal-title":"Petroleum Eng Int"},{"key":"983_CR5","doi-asserted-by":"crossref","unstructured":"Aminian K, Bilgesu HI, Ameri S, Gil E (2000) Improving the simulation of waterflood performance with the use of neural networks. In SPE 65630, proceeding of SPE Eastern regional conference, October","DOI":"10.2118\/65630-MS"},{"key":"983_CR6","unstructured":"Aminian K, Thomas B, Bilgesu HI, Ameri S, Oyerokun A (2001) Permeability distribution prediction. In SPE paper, proceeding of SPE Eastern regional conference, October"},{"issue":"8","key":"983_CR7","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1016\/S0098-3004(00)00026-1","volume":"26","author":"PM Wong","year":"2000","unstructured":"Wong PM, Jang M, Cho S, Gedeon TD (2000) Multiple permeability predictions using an observational learning algorithm. Comput Geosci 26(8):907\u2013913","journal-title":"Comput Geosci"},{"key":"983_CR8","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1016\/S0098-1354(02)00148-5","volume":"26","author":"R Rallo","year":"2002","unstructured":"Rallo R, Ferre-Gin J, Arenas A, Giralt F (2002) Neural virtual sensor for the inferential prediction of product quality from process variables. Comput Chem Eng 26:1735\u20131754","journal-title":"Comput Chem Eng"},{"issue":"3","key":"983_CR9","first-page":"286","volume":"33","author":"JH Doveton","year":"1992","unstructured":"Doveton JH, Prensky SE (1992) Geological applications of wireline logs: a synopsis of developments and trends. Log Anal 33(3):286\u2013303","journal-title":"Log Anal"},{"key":"983_CR10","doi-asserted-by":"crossref","unstructured":"Qu X, Feng J, Sun W (2008) Parallel genetic algorithm model based on AHP and neural networks for enterprise comprehensive business. Intelligent Information Hiding and Multimedia Signal Processing, Harbin, 15\u201317 August 2008","DOI":"10.1109\/IIH-MSP.2008.334"},{"key":"983_CR11","doi-asserted-by":"crossref","unstructured":"Ahmadi MA (2012) Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilib 314:46\u201351","DOI":"10.1016\/j.fluid.2011.10.016"},{"key":"983_CR12","doi-asserted-by":"crossref","unstructured":"Ahmadi MA, Shadizadeh SR, Goudarzi A (2012) Combining artificial neural network and unified particle swarm optimization for oil flow rate prediction: case study. Neural Comput & Applic. doi: 10.1007\/s00521-012-0955-9","DOI":"10.1007\/s00521-012-0955-9"},{"key":"983_CR13","doi-asserted-by":"crossref","unstructured":"Ahmadi MA, Shadizadeh SR (2012) Neural-network-based unified particle swarm optimization for the prediction of asphaltene precipitation due to natural depletion. Neural Comput & Applic. doi: 10.1007\/s00521-012-0921-6","DOI":"10.1007\/s00521-012-0921-6"},{"key":"983_CR14","unstructured":"Ahmadi MA, Shokrollah-Zadeh Behbahani H. New approach for permeability prediction from well log data by using evolutionary algorithm, part A: recovery, utilization, and environmental effects. J Energy Sources. doi: 10.1080\/15567036.2011.605427"},{"key":"983_CR15","unstructured":"Ahmadi MA. Prediction of asphaltene precipitation by using pso-neural network, part A: recovery, utilization, and environmental effects. J Energy Sources. doi: 10.1080\/15567036.2011.598902"},{"key":"983_CR16","doi-asserted-by":"crossref","unstructured":"Ahmadi MA, Shadizadeh SR. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. J Fuel. doi: 10.1016\/j.fuel.2012.05.050","DOI":"10.1016\/j.fuel.2012.05.050"},{"key":"983_CR17","doi-asserted-by":"crossref","unstructured":"Zendehboudi S, Ahmadi MA, James L, Chatzis I. Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization. Energy Fuels. doi: 10.1021\/ef300443j","DOI":"10.1021\/ef300443j"},{"key":"983_CR18","doi-asserted-by":"crossref","unstructured":"Ahmadi MA (2011) Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Petrol Explor Prod Technol 1:99\u2013106. doi: 10.1007\/s13202-011-0013-7","DOI":"10.1007\/s13202-011-0013-7"},{"key":"983_CR19","doi-asserted-by":"crossref","unstructured":"Ahmadi MA, Zendehboudi S, Lohi A, Elkamel A, Chatzis I. Application of hybrid genetic algorithm with particle swarm optimization and neural network for reservoir permeability prediction. J Geophys Prospect. doi: 10.1111\/j.1365-2478.2012.01080.x","DOI":"10.1111\/j.1365-2478.2012.01080.x"},{"key":"983_CR20","doi-asserted-by":"crossref","unstructured":"Ahmadi MA, Shadizadeh SR (2012) Prediction of asphaltene precipitation by using hybrid genetic algorithm and particle swarm optimization and neural network. Neural Comput & Applic. doi: 10.1007\/s00521-012-0920-7","DOI":"10.1007\/s00521-012-0920-7"},{"key":"983_CR21","unstructured":"Ahmadi MA, Shadizadeh SR (2012) Permeability prediction of carbonate reservoir by combining neural network and shuffled frog-leaping. J Amer Sci 8(2):529\u2013533"},{"key":"983_CR22","unstructured":"Ahmadi MA, Shadizadeh SR, Hasanvand M (2011) Neural Network based stochastic particle swarm optimization for prediction of minimum miscible pressure. Int J Comput Appl 34(1):15\u201319"},{"key":"983_CR23","doi-asserted-by":"crossref","unstructured":"Reed R (1993) Pruning algorithms-a survey. IEEE Trans Neural Netw 4:740\u2013747","DOI":"10.1109\/72.248452"},{"key":"983_CR24","unstructured":"Ahmadi MA, Shirdel MR, Samaee MA. Prediction of asphaltene precipitation by using artificial intelligence. Pet Sci Technol. doi: 10.1080\/10916466.2011.590834"},{"key":"983_CR25","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, IEEE congress on evolutionary computation, pp 4661\u20134667","DOI":"10.1109\/CEC.2007.4425083"},{"issue":"5","key":"983_CR26","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0893-6080(90)90005-6","volume":"3","author":"K Hornik","year":"1990","unstructured":"Hornik K, Stinchcombe M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks. Neural Netw 3(5):551\u2013560","journal-title":"Neural Netw"},{"key":"983_CR27","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1109\/TNN.2003.810618","volume":"14","author":"N Garcia-Pedrajas","year":"2003","unstructured":"Garcia-Pedrajas N, Hervas-Martinez C, Munoz-Perez J (2003) COVNET: a cooperative co evolutionary model for evolving artificial neural networks. IEEE Trans Neural Netw 14:575\u2013596","journal-title":"IEEE Trans Neural Netw"},{"key":"983_CR28","volume-title":"Neural fuzzy adaptive modeling and control","author":"M Brown","year":"1994","unstructured":"Brown M, Harris C (1994) Neural fuzzy adaptive modeling and control. Prentice-Hall, Englewood Cliffs"},{"key":"983_CR29","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornick","year":"1989","unstructured":"Hornick K, Stinchcombe M, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359\u2013366","journal-title":"Neural Netw"}],"updated-by":[{"DOI":"10.1007\/s00521-012-0983-5","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T00:00:00Z","timestamp":1375315200000},"record-id":"18695"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-0983-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-012-0983-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-0983-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T01:43:30Z","timestamp":1642383810000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-012-0983-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,6,12]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2013,8]]}},"alternative-id":["983"],"URL":"https:\/\/doi.org\/10.1007\/s00521-012-0983-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,6,12]]}}}