{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:15:51Z","timestamp":1761401751141},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,2,27]],"date-time":"2015-02-27T00:00:00Z","timestamp":1424995200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2015,11]]},"DOI":"10.1007\/s00521-015-1847-6","type":"journal-article","created":{"date-parts":[[2015,2,26]],"date-time":"2015-02-26T06:53:02Z","timestamp":1424933582000},"page":"1919-1928","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Designing evolutionary feedforward neural networks using social spider optimization algorithm"],"prefix":"10.1007","volume":"26","author":[{"given":"Seyedeh Zahra","family":"Mirjalili","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahrzad","family":"Saremi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Mohammad","family":"Mirjalili","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,2,27]]},"reference":[{"key":"1847_CR1","volume-title":"Neural smithing: supervised learning in feedforward artificial neural networks","author":"RD Reed","year":"1998","unstructured":"Reed RD, Marks RJ (1998) Neural smithing: supervised learning in feedforward artificial neural networks. Mit Press, Cambridge"},{"key":"1847_CR2","doi-asserted-by":"crossref","unstructured":"Caruana R, Niculescu-Mizil (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on Machine learning, pp 161\u2013168","DOI":"10.1145\/1143844.1143865"},{"key":"1847_CR3","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7011.001.0001","volume-title":"Unsupervised learning: foundations of neural computation","author":"GE Hinton","year":"1999","unstructured":"Hinton GE, Sejnowski TJ (1999) Unsupervised learning: foundations of neural computation. MIT press, Cambridge"},{"key":"1847_CR4","first-page":"101","volume":"22","author":"D Wang","year":"2001","unstructured":"Wang D (2001) Unsupervised learning: foundations of neural computation. AI Mag 22:101","journal-title":"AI Mag"},{"key":"1847_CR5","doi-asserted-by":"crossref","unstructured":"Barto A (1997) Reinforcement learning. Neural systems for control, pp 7\u201329","DOI":"10.1016\/B978-012526430-3\/50003-9"},{"key":"1847_CR6","volume-title":"Reinforcement learning: an introduction","author":"AG Barto","year":"1998","unstructured":"Barto AG (1998) Reinforcement learning: an introduction. MIT press, Cambridge"},{"key":"1847_CR7","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/45.329294","volume":"13","author":"G Bebis","year":"1994","unstructured":"Bebis G, Georgiopoulos M (1994) Feed-forward neural networks. Potentials, IEEE 13:27\u201331","journal-title":"Potentials, IEEE"},{"key":"1847_CR8","volume-title":"Introduction to the theory of neural computation","author":"J Hertz","year":"1991","unstructured":"Hertz J (1991) Introduction to the theory of neural computation, vol 1. Basic Books, New York"},{"key":"1847_CR9","unstructured":"Branke J (1995) Evolutionary algorithms for neural network design and training. In: Proceedings of the first nordic workshop on genetic algorithms and its applications"},{"key":"1847_CR10","doi-asserted-by":"crossref","first-page":"655","DOI":"10.7763\/IJCCE.2013.V2.268","volume":"2","author":"S Saremi","year":"2013","unstructured":"Saremi S, Mirjalili S (2013) Integrating chaos to biogeography-based optimization algorithm. Int J Comput Commun Eng 2:655\u2013658","journal-title":"Int J Comput Commun Eng"},{"key":"1847_CR11","doi-asserted-by":"crossref","unstructured":"Saremi S, Mirjalili S, Lewis A (2014) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl. doi: 10.1007\/s00521-014-1743-5","DOI":"10.1007\/s00521-014-1743-5"},{"issue":"5","key":"1847_CR12","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s00521-014-1597-x","volume":"25","author":"S Saremi","year":"2014","unstructured":"Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077\u20131097","journal-title":"Neural Comput Appl"},{"key":"1847_CR13","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.protcy.2013.12.473","volume":"12","author":"S Saremi","year":"2014","unstructured":"Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Procedia Technol 12:180\u2013185","journal-title":"Procedia Technol"},{"key":"1847_CR14","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.protcy.2013.12.472","volume":"12","author":"S Saremi","year":"2014","unstructured":"Saremi S, Mirjalili SM, Mirjalili S (2014) Unit cell topology optimization of line defect photonic crystal waveguide. Procedia Technol 12:174\u2013179","journal-title":"Procedia Technol"},{"key":"1847_CR15","unstructured":"Mirjalili S, Hashim SM (2011) BMOA: binary magnetic optimization algorithm. 3rd international conference on machine learning and computing (ICMLC 2011), Singapore, pp 201\u2013206"},{"issue":"7\u20138","key":"1847_CR16","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1007\/s00521-014-1640-y","volume":"25","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7\u20138):1569\u20131584","journal-title":"Neural Comput Appl"},{"issue":"6","key":"1847_CR17","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1007\/s13369-014-1156-x","volume":"39","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Lewis A, Sadiq AS (2014) Autonomous particles groups for particle swarm optimization. Arab J Sci Eng 39(6):4683\u20134697","journal-title":"Arab J Sci Eng"},{"key":"1847_CR18","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"issue":"3\u20134","key":"1847_CR19","first-page":"663","volume":"25","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili S, Mirjalili SM, Yang XS (2013) Binary bat algorithm. Neural Comput Appl 25(3\u20134):663\u2013681","journal-title":"Binary bat algorithm. Neural Comput Appl"},{"key":"1847_CR20","doi-asserted-by":"crossref","first-page":"C345","DOI":"10.21914\/anziamj.v54i0.6154","volume":"54","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili S, Rawlins T, Hettenhausen J, Lewis A (2013) A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem. ANZIAM J 54:C345\u2013C360","journal-title":"ANZIAM J"},{"issue":"6","key":"1847_CR21","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s00521-014-1629-6","volume":"25","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Wang GG, Coelho LDS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423\u20131435","journal-title":"Neural Comput Appl"},{"key":"1847_CR22","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw. doi: 10.1016\/j.advengsoft.2015.01.010","journal-title":"Adv Eng Softw"},{"key":"1847_CR23","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267:66\u201372","journal-title":"Sci Am"},{"key":"1847_CR24","doi-asserted-by":"crossref","unstructured":"Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"1847_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","volume":"9","author":"S Mirjalili","year":"2012","unstructured":"Mirjalili S, Lewis A (2012) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1\u201314. doi: 10.1016\/j.swevo.2012.09.002","journal-title":"Swarm Evolut Comput"},{"key":"1847_CR26","first-page":"36","volume-title":"Encyclopedia of machine learning","author":"M Dorigo","year":"2010","unstructured":"Dorigo M, Birattari M (2010) Ant colony optimization. In: Encyclopedia of machine learning. Springer, New York, pp 36\u201339"},{"key":"1847_CR27","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341\u2013359","journal-title":"J Global Optim"},{"key":"1847_CR28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1015059928466","volume":"1","author":"H-G Beyer","year":"2002","unstructured":"Beyer H-G, Schwefel H-P (2002) Evolution strategies\u2014a comprehensive introduction. Nat Comput 1:3\u201352","journal-title":"Nat Comput"},{"key":"1847_CR29","doi-asserted-by":"crossref","unstructured":"Mirjalili S, Mohd Hashim SZ (2010) A new hybrid PSOGSA algorithm for function optimization. In: IEEE international conference on computer and information application (ICCIA 2010), China, pp 374\u2013377","DOI":"10.1109\/ICCIA.2010.6141614"},{"key":"1847_CR30","unstructured":"Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: IJCAI, vol 89, pp 762\u2013767"},{"key":"1847_CR31","unstructured":"Belew RK, McInerney J, Schraudolph NN (1990) Evolving networks: using the genetic algorithm with connectionist learning"},{"key":"1847_CR32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/TNN.2002.804317","volume":"14","author":"FH-F Leung","year":"2003","unstructured":"Leung FH-F, Lam H-K, Ling S-H, Tam PK-S (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. Neural Netw IEEE Trans 14:79\u201388","journal-title":"Neural Netw IEEE Trans"},{"key":"1847_CR33","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/0167-2789(94)90285-2","volume":"75","author":"H Kitano","year":"1994","unstructured":"Kitano H (1994) Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms. Phys D 75:225\u2013238","journal-title":"Phys D"},{"key":"1847_CR34","unstructured":"Chen M-S, Liao FH (1998) Neural networks training using genetic algorithms. In: IEEE international conference on systems, man, and cybernetics, 1998, vol 3. IEEE, pp 2436\u20132441"},{"key":"1847_CR35","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/0167-8191(90)90086-O","volume":"14","author":"D Whitley","year":"1990","unstructured":"Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14:347\u2013361","journal-title":"Parallel Comput"},{"key":"1847_CR36","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/1471-2105-7-125","volume":"7","author":"M Meissner","year":"2006","unstructured":"Meissner M, Schmuker M, Schneider G (2006) Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinform 7:125","journal-title":"BMC Bioinform"},{"key":"1847_CR37","doi-asserted-by":"crossref","unstructured":"Gudise VG, Venayagamoorthy GK (2003) Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In: Swarm intelligence symposium, 2003. SIS\u201903. Proceedings of the 2003 IEEE, pp 110\u2013117","DOI":"10.1109\/SIS.2003.1202255"},{"key":"1847_CR38","doi-asserted-by":"crossref","unstructured":"Al-kazemi B, Mohan CK (2002). Training feedforward neural networks using multi-phase particle swarm optimization. In: Neural information processing, 2002. ICONIP\u201902. Proceedings of the 9th international conference on, 2002, pp 2615\u20132619","DOI":"10.1109\/ICONIP.2002.1201969"},{"key":"1847_CR39","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.amc.2006.07.025","volume":"185","author":"J-R Zhang","year":"2007","unstructured":"Zhang J-R, Zhang J, Lok T-M, Lyu MR (2007) A hybrid particle swarm optimization\u2013back-propagation algorithm for feedforward neural network training. Appl Math Comput 185:1026\u20131037","journal-title":"Appl Math Comput"},{"key":"1847_CR40","unstructured":"Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. Learning 6(1)"},{"key":"1847_CR41","unstructured":"Ismail A, Engelbrecht AP (1999) Training product units in feedforward neural networks using particle swarm optimization. In: Proceedings of the international conference on artificial intelligence, pp 36\u201340"},{"key":"1847_CR42","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00521-007-0084-z","volume":"16","author":"K Socha","year":"2007","unstructured":"Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16:235\u2013247","journal-title":"Neural Comput Appl"},{"key":"1847_CR43","doi-asserted-by":"crossref","unstructured":"Blum C, Socha K (2005) Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In: Hybrid Intelligent Systems, 2005. HIS\u201905. Fifth international conference on, p. 6","DOI":"10.1109\/ICHIS.2005.104"},{"key":"1847_CR44","first-page":"016","volume":"7","author":"B-R Hong","year":"2003","unstructured":"Hong B-R, Jin F-H, Gao Q-J (2003) Multi-layer feedforward neural network based on ant colony system. J Harbin Inst Technol 7:016","journal-title":"J Harbin Inst Technol"},{"key":"1847_CR45","doi-asserted-by":"crossref","unstructured":"Liu YP, Wu MG, Qian JX (2006) Evolving neural networks using the hybrid of ant colony optimization and BP algorithms. In: Advances in neural networks-ISNN 2006, ed: Springer, pp 714\u2013722","DOI":"10.1007\/11759966_105"},{"key":"1847_CR46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1023\/A:1022995128597","volume":"17","author":"J Ilonen","year":"2003","unstructured":"Ilonen J, Kamarainen J-K, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17:93\u2013105","journal-title":"Neural Process Lett"},{"key":"1847_CR47","doi-asserted-by":"crossref","unstructured":"Slowik A, Bialko M (2008) Training of artificial neural networks using differential evolution algorithm. In: Human system interactions. Conference on, pp 60\u201365","DOI":"10.1109\/HSI.2008.4581409"},{"key":"1847_CR48","doi-asserted-by":"crossref","unstructured":"Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In Modeling decisions for artificial intelligence, ed: Springer, 2007, pp 318\u2013329","DOI":"10.1007\/978-3-540-73729-2_30"},{"key":"1847_CR49","doi-asserted-by":"crossref","unstructured":"Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: IEEE congress on evolutionary computation (CEC), 2011. IEEE, New Orleans, pp 84\u201388","DOI":"10.1109\/CEC.2011.5949602"},{"key":"1847_CR50","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1016\/j.amc.2012.04.069","volume":"218","author":"S Mirjalili","year":"2012","unstructured":"Mirjalili S, Mohd SZ (2012) Hashim, and H. Moradian Sardroudi, \u201cTraining feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm,\u201d. Appl Math Comput 218:11125\u201311137","journal-title":"Appl Math Comput"},{"key":"1847_CR51","first-page":"803","volume":"73","author":"M Ghalambaz","year":"2011","unstructured":"Ghalambaz M, Noghrehabadi A, Behrang M, Assareh E, Ghanbarzadeh A, Hedayat N (2011) A hybrid neural network and gravitational search algorithm (HNNGSA) method to solve well known Wessinger\u2019s equation. World Acad Sci Eng Technol 73:803\u2013807","journal-title":"World Acad Sci Eng Technol"},{"key":"1847_CR52","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/72.410361","volume":"6","author":"R Battiti","year":"1995","unstructured":"Battiti R, Tecchiolli G (1995) Training neural nets with the reactive tabu search. Neural Netw IEEE Trans 6:1185\u20131200","journal-title":"Neural Netw IEEE Trans"},{"key":"1847_CR53","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.engappai.2004.04.003","volume":"17","author":"A Kalinli","year":"2004","unstructured":"Kalinli A, Karaboga D (2004) Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation. Eng Appl Artif Intell 17:529\u2013542","journal-title":"Eng Appl Artif Intell"},{"key":"1847_CR54","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ins.2014.01.038","volume":"269","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188\u2013209","journal-title":"Inf Sci"},{"key":"1847_CR55","doi-asserted-by":"crossref","unstructured":"Wienholt W (1993) Minimizing the system error in feedforward neural networks with evolution strategy. In: ICANN\u201993. Springer, London, pp. 490\u2013493","DOI":"10.1007\/978-1-4471-2063-6_125"},{"key":"1847_CR56","doi-asserted-by":"crossref","unstructured":"Mirjalili S, Sadiq AS (2011) Magnetic optimization algorithm for training multi layer perceptron. In: Communication software and networks (ICCSN), 2011 IEEE 3rd international conference on, 2011, pp 42\u201346","DOI":"10.1109\/ICCSN.2011.6014845"},{"key":"1847_CR57","doi-asserted-by":"crossref","unstructured":"Mirjalili S (2015) How effective is a Grey wolf optimizer in training multi-layer perceptrons. Appl Intell. doi: 10.1007\/s10489-014-0645-7","DOI":"10.1007\/s10489-014-0645-7"},{"key":"1847_CR58","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Evolutionary Comput IEEE Trans 1:67\u201382","journal-title":"Evolutionary Comput IEEE Trans"},{"key":"1847_CR59","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.eswa.2013.07.067","volume":"41","author":"E Cuevas","year":"2014","unstructured":"Cuevas E, Cienfuegos M (2014) A new algorithm inspired in the behavior of the social-spider for constrained optimization. Exp Syst Appl: Int J. 41:412\u2013425","journal-title":"Exp Syst Appl: Int J"},{"key":"1847_CR60","doi-asserted-by":"crossref","first-page":"6374","DOI":"10.1016\/j.eswa.2013.05.041","volume":"40","author":"E Cuevas","year":"2013","unstructured":"Cuevas E, Cienfuegos M, Zald\u00edvar D, P\u00e9rez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40:6374\u20136384","journal-title":"Expert Syst Appl"},{"key":"1847_CR61","doi-asserted-by":"crossref","unstructured":"Pereira L, Rodrigues D, Ribeiro P, Papa J, Weber SA (2014) Social-spider optimization-based artificial neural networks training and its applications for parkinson\u2019s disease identification. In: Computer-based medical systems (CBMS), 2014 IEEE 27th International symposium on, 2014, pp 14\u201317","DOI":"10.1109\/CBMS.2014.25"},{"key":"1847_CR62","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359\u2013366","journal-title":"Neural Netw"},{"key":"1847_CR63","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository"},{"key":"1847_CR64","unstructured":"Wdaa I, Sttar A (2008) Differential evolution for neural networks learning enhancement. Universiti Teknologi Malaysia, Faculty of Computer Science and Information System"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1847-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-1847-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1847-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T08:29:59Z","timestamp":1566376199000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-1847-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,27]]},"references-count":64,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2015,11]]}},"alternative-id":["1847"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-1847-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,2,27]]}}}