{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:17:16Z","timestamp":1743063436419,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030963040"},{"type":"electronic","value":"9783030963057"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96305-7_22","type":"book-chapter","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T11:06:50Z","timestamp":1646305610000},"page":"233-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Convolutional Neural Network Design Using a Particle Swarm Optimization for Face Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5798-1426","authenticated-orcid":false,"given":"Patricia","family":"Melin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9802-1907","authenticated-orcid":false,"given":"Daniela","family":"S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-5302","authenticated-orcid":false,"given":"Martha","family":"Pulido","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5689","authenticated-orcid":false,"given":"Oscar","family":"Castillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94463-0","volume-title":"Neural Networks and Deep Learning: A Textbook","author":"C Aggarwal","year":"2018","unstructured":"Aggarwal, C.: Neural Networks and Deep Learning: A Textbook. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-94463-0"},{"doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T., Al-Zawi, S.: Understanding of a convolutional neural network, In: International Conference on Engineering and Technology (ICET), pp. 1\u20136 (2017)","key":"22_CR2","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"doi-asserted-by":"crossref","unstructured":"Bin, L.Y., Huann, G.Y., Yund, L.K.: Study of convolutional neural network in recognizing static American sign language. In: 2019 IEEE international conference on signal and image processing applications (ICSIPA), pp. 41\u201345 (2019)","key":"22_CR3","DOI":"10.1109\/ICSIPA45851.2019.8977767"},{"key":"22_CR4","first-page":"22","volume":"92","author":"O Deperlioglu","year":"2018","unstructured":"Deperlioglu, O.: Classification of phonocardiograms with convolutional neural networks. Brain 92, 22\u201333 (2018)","journal-title":"Brain"},{"unstructured":"Eberhart, R., Kennedy, J.: A new optimizer using swarm theory. In: 6th International Symposium on Micro Machine and Human Science (MHS), pp. 39\u201343 (1995)","key":"22_CR5"},{"doi-asserted-by":"crossref","unstructured":"Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 84\u201388 (2000)","key":"22_CR6","DOI":"10.1109\/CEC.2000.870279"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.rinp.2021.104274","volume":"25","author":"AA Elhag","year":"2021","unstructured":"Elhag, A.A., Aloafi, T.A., Jawa, T.M., Sayed-Ahmed, N., Bayones, F.S.: Artificial neural networks and statistical models for optimization studying COVID-19. Results Phys. 25, 1\u20134 (2021)","journal-title":"Results Phys."},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.swevo.2019.05.010","volume":"49","author":"FE Fernandes","year":"2019","unstructured":"Fernandes, F.E., Yen, G.G.: Particle swarm optimization of deep neural networks architectures for image classification. Swarm Evol. Comput. 49, 62\u201374 (2019)","journal-title":"Swarm Evol. Comput."},{"doi-asserted-by":"crossref","unstructured":"Fregoso, J., Gonz\u00e1lez, C.I., Martinez, G.E.: Parameter optimization of a convolutional neural network using particle swarm optimization. In: Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms, pp. 149\u2013169 (2021)","key":"22_CR9","DOI":"10.1007\/978-3-030-68776-2_9"},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2021.102954","volume":"70","author":"EM Houby","year":"2021","unstructured":"Houby, E.M., Yassin, N.I.: Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomed. Signal Process. Control 70, 1\u201310 (2021)","journal-title":"Biomed. Signal Process. Control"},{"doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks (ICNN), vol. 4, pp. 1942\u20131948 (1995)","key":"22_CR11","DOI":"10.1109\/ICNN.1995.488968"},{"unstructured":"LeCun, Y., Bengio, Y.: Convolutional networks f or images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, pp. 255\u2013258 (1998)","key":"22_CR12"},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.autcon.2021.103765","volume":"128","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Yeoh, J.K.: Automated crack pattern recognition from images for condition assessment of concrete structures. Autom. Constr. 128, 1\u201314 (2021)","journal-title":"Autom. Constr."},{"doi-asserted-by":"publisher","unstructured":"Lu, L., Zheng, L., Carneiro, G., Yang, L.: Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets, pp. 3\u201310. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-030-13969-8","key":"22_CR14","DOI":"10.1007\/978-3-030-13969-8"},{"issue":"7","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JSEN.2019.2897416","volume":"19","author":"AK Mobarakeh","year":"2019","unstructured":"Mobarakeh, A.K., Cabrera-Carrillo, J.A., Castillo-Aguilar, J.J.: Robust face recognition based on a new supervised kernel subspace learning method. Sensors 19(7), 1\u201329 (2019)","journal-title":"Sensors"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2021.107517","volume":"109","author":"R Nand","year":"2021","unstructured":"Nand, R., Sharma, B.N., Chaudhary, K.: Stepping ahead firefly algorithm and hybridization with evolution strategy for global optimization problems. Appl. Soft Comput. 109, 1\u201317 (2021)","journal-title":"Appl. Soft Comput."},{"unstructured":"ORL Face Database. AT&T Laboratories Cambridge. https:\/\/cam-orl.co.uk\/facedatabase.html, Accessed 14 Nov 2021","key":"22_CR17"},{"issue":"1","key":"22_CR18","first-page":"109","volume":"14","author":"Y Poma","year":"2020","unstructured":"Poma, Y., Melin, P., Gonzalez, C.I., Martinez, G.E.: Optimization of convolutional neural networks using the fuzzy gravitational search algorithm. J. Autom. Mobile Rob. Intell. Syst. 14(1), 109\u2013120 (2020)","journal-title":"J. Autom. Mobile Rob. Intell. Syst."},{"key":"22_CR19","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-030-34135-0_6","volume-title":"Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine","author":"Y Poma","year":"2020","unstructured":"Poma, Y., Melin, P., Gonz\u00e1lez, C.I., Martinez, G.E.: Optimal recognition model based on convolutional neural networks and fuzzy gravitational search algorithm method. In: Castillo, O., Melin, P. (eds.) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. SCI, vol. 827, pp. 71\u201381. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-34135-0_6"},{"doi-asserted-by":"crossref","unstructured":"Poma, Y., Melin, P., Gonz\u00e1lez, C.I., Martinez, G.E.: Filter size optimization on a convolutional neural network using FGSA. In: Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms, pp. 391\u2013403 (2020)","key":"22_CR20","DOI":"10.1007\/978-3-030-35445-9_29"},{"issue":"2","key":"22_CR21","first-page":"51","volume":"2","author":"RM Ramadan","year":"2009","unstructured":"Ramadan, R.M., Abdel-Kader, R.F.: Face recognition using particle swarm optimization-based selected features. Int. J. Signal Process. Image Process. Pattern Recogn. 2(2), 51\u201356 (2009)","journal-title":"Int. J. Signal Process. Image Process. Pattern Recogn."},{"key":"22_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jas.2021.105413","volume":"132","author":"KM Reese","year":"2021","unstructured":"Reese, K.M.: Deep learning artificial neural networks for non-destructive archaeological site dating. J. Archaeol. Sci. 132, 1\u201314 (2021)","journal-title":"J. Archaeol. Sci."},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-3-030-68776-2_8","volume":"2021","author":"R Rodriguez","year":"2021","unstructured":"Rodriguez, R., Gonz\u00e1lez, C.I., Martinez, G.E., Melin, P.: An improved convolutional neural network based on a parameter modification of the convolution layer\u201d, fuzzy logic hybrid extensions of neural and optimization algorithms: theory and applications. Stud. Comput. Intell. 2021, 125\u2013147 (2021)","journal-title":"Stud. Comput. Intell."},{"issue":"3","key":"22_CR24","doi-asserted-by":"publisher","first-page":"3229","DOI":"10.3233\/JIFS-191198","volume":"38","author":"D S\u00e1nchez","year":"2020","unstructured":"S\u00e1nchez, D., Melin, P., Castillo, O.: Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition. J. Intell. Fuzzy Syst. 38(3), 3229\u20133252 (2020)","journal-title":"J. Intell. Fuzzy Syst."},{"doi-asserted-by":"crossref","unstructured":"Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. In: International Conference on Evolutionary Programming, pp. 591\u2013600 (1998)","key":"22_CR25","DOI":"10.1007\/BFb0040810"},{"key":"22_CR26","first-page":"1","volume":"38","author":"\u00c7K \u015eim\u015fek","year":"2021","unstructured":"\u015eim\u015fek, \u00c7.K., Arabac\u0131, D.: Simulation of the climatic changes around the coastal land reclamation areas using artificial neural networks. Urban Clim. 38, 1\u201318 (2021)","journal-title":"Urban Clim."},{"doi-asserted-by":"crossref","unstructured":"Szegedy C., et al.: Going deeper with convolutions, In: The IEEE conference on computer vision and pattern recognition (CVPR), pp. 1\u20139 (2015)","key":"22_CR27","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ces.2021.116949","volume":"246","author":"J Szoplik","year":"2021","unstructured":"Szoplik, J., Ciuksza, M.: Mixing time prediction with artificial neural network model. Chem. Eng. Sci. 246, 1\u20138 (2021)","journal-title":"Chem. Eng. Sci."},{"key":"22_CR29","doi-asserted-by":"publisher","DOI":"10.4324\/9781315154282","volume-title":"Convolutional Neural Networks in Visual Computing, Concise Guide","author":"R Venkatesan","year":"2017","unstructured":"Venkatesan, R., Baoxin, L.: Convolutional Neural Networks in Visual Computing, Concise Guide, 1st edn. CRC Press, Boca Raton (2017)","edition":"1"},{"doi-asserted-by":"crossref","unstructured":"Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511\u2013518 (2001)","key":"22_CR30","DOI":"10.1109\/CVPR.2001.990517"},{"issue":"2","key":"22_CR31","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"PA Viola","year":"2004","unstructured":"Viola, P.A., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vision"},{"issue":"4","key":"22_CR32","doi-asserted-by":"publisher","first-page":"4390","DOI":"10.1016\/j.eswa.2010.09.108","volume":"38","author":"J Wei","year":"2011","unstructured":"Wei, J., Jian-qi, Z., Xiang, Z.: Face recognition method based on support vector machine and particle swarm optimization. Expert Syst. Appl. 38(4), 4390\u20134393 (2011)","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Xu, X., Ge H., Li S.: An improvement on recurrent neural network by combining convolution neural network and a simple initialization of the weights. In: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, pp. 150\u2013154 (2016)","key":"22_CR33","DOI":"10.1109\/ICOACS.2016.7563068"},{"unstructured":"Yale Face Database. UCSD. http:\/\/vision.ucsd.edu\/content\/yale-face-database, Accessed 14 Nov 2021","key":"22_CR34"},{"key":"22_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.micpro.2021.103865","volume":"82","author":"L Zou","year":"2021","unstructured":"Zou, L.: Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm. Microprocess. Microsyst. 82, 1\u20138 (2021)","journal-title":"Microprocess. Microsyst."}],"container-title":["Lecture Notes in Networks and Systems","Hybrid Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96305-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T18:26:30Z","timestamp":1726770390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96305-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030963040","9783030963057"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96305-7_22","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"his2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/his21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}