{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T06:59:56Z","timestamp":1781333996487,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2018,3,21]],"date-time":"2018-03-21T00:00:00Z","timestamp":1521590400000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s13042-018-0811-z","type":"journal-article","created":{"date-parts":[[2018,3,21]],"date-time":"2018-03-21T10:59:33Z","timestamp":1521629973000},"page":"1301-1311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Towards improving the convolutional neural networks for deep learning using the distributed artificial bee colony method"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2004-4070","authenticated-orcid":false,"given":"Anan","family":"Banharnsakun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,3,21]]},"reference":[{"key":"811_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"811_CR2","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J ((2008)) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"key":"811_CR3","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/MSP.2010.939038","volume":"28","author":"D Yu","year":"2011","unstructured":"Yu D, Deng L (2011) Deep learning and its applications to signal and information processing. IEEE Signal Process Mag 28:145\u2013154","journal-title":"IEEE Signal Process Mag"},{"key":"811_CR4","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"811_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/ATSIP.2013.8","volume":"3","author":"L Deng","year":"2014","unstructured":"Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process 3:1\u201329","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"811_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2017.2701419","author":"XZ Wang","year":"2017","unstructured":"Wang XZ, Zhang T, Wang R (2017) Noniterative deep learning: incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst. \n                    https:\/\/doi.org\/10.1109\/tsmc.2017.2701419","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"811_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.08.013","author":"Z Wang","year":"2017","unstructured":"Wang Z, Wang XZ (2017) A deep stochastic weight assignment network and its application to chess playing. J Parallel Distrib Comput. \n                    https:\/\/doi.org\/10.1016\/j.jpdc.2017.08.013","journal-title":"J Parallel Distrib Comput"},{"key":"811_CR8","unstructured":"Jordan A (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in Neural Information Processing Systems, pp\u00a0841\u2013848"},{"key":"811_CR9","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.patcog.2013.05.025","volume":"47","author":"A Fischer","year":"2014","unstructured":"Fischer A, Igel C (2014) Training restricted Boltzmann machines: an introduction. Pattern Recogn 47:25\u201339","journal-title":"Pattern Recogn"},{"key":"811_CR10","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527\u20131554","journal-title":"Neural Comput"},{"key":"811_CR11","unstructured":"Salakhutdinov R, Hinton GE (2009) Deep Boltzmann Machines. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pp\u00a0448\u2013455"},{"key":"811_CR12","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, pp\u00a01097\u20131105"},{"key":"811_CR13","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"GE Hinton","year":"2012","unstructured":"Hinton GE, Deng L, Yu D, Dahl G, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82\u201397","journal-title":"IEEE Signal Process Mag"},{"key":"811_CR14","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed AR, Hinton GE (2013) Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp\u00a06645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"811_CR15","doi-asserted-by":"crossref","unstructured":"Radzi SA, Khalil-Hani M (2011) Character recognition of license plate number using convolutional neural network. In: Proceedings of the International Visual Informatics Conference, pp\u00a045\u201355","DOI":"10.1007\/978-3-642-25191-7_6"},{"key":"811_CR16","unstructured":"Hu B, Lu Z, Li H, Chen Q (2014) Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp\u00a02042\u20132050"},{"key":"811_CR17","doi-asserted-by":"crossref","unstructured":"Yalcin H, Razavi S (2016) Plant classification using convolutional neural networks. In: IEEE proceedings of the Fifth International Conference on Agro-Geoinformatics, pp\u00a01\u20135","DOI":"10.1109\/Agro-Geoinformatics.2016.7577698"},{"key":"811_CR18","doi-asserted-by":"crossref","unstructured":"Ramadhan I, Purnama B, Al Faraby S (2016) Convolutional neural networks applied to handwritten mathematical symbols classification. In: IEEE Proceedings of the 4th International Conference on Information and Communication Technology, pp\u00a01\u20134","DOI":"10.1109\/ICoICT.2016.7571941"},{"key":"811_CR19","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/s12668-016-0215-z","volume":"6","author":"D Bashkirova","year":"2016","unstructured":"Bashkirova D (2016) Convolutional neural networks for image steganalysis. BioNanoScience 6:246\u2013248","journal-title":"BioNanoScience"},{"key":"811_CR20","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s00500-011-0754-8","volume":"16","author":"JA Parejo","year":"2012","unstructured":"Parejo JA, Ruiz-Cort\u00e9s A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16:527\u2013561","journal-title":"Soft Comput"},{"key":"811_CR21","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.ins.2016.07.037","volume":"369","author":"YC He","year":"2016","unstructured":"He YC, Wang XZ, He YL, Zhao SL, Li WB (2016) Exact and approximate algorithms for discounted {0\u20131} knapsack problem. Inf Sci 369:634\u2013647","journal-title":"Inf Sci"},{"key":"811_CR22","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1504\/IJBIC.2017.087924","volume":"10","author":"H Zhu","year":"2017","unstructured":"Zhu H, He Y, Wang XZ, Tsang EC (2017) Discrete differential evolutions for the discounted {0\u20131} knapsack problem. Int J Bio-Inspired Comput 10:219\u2013238","journal-title":"Int J Bio-Inspired Comput"},{"key":"811_CR23","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.future.2017.05.044","volume":"78","author":"Y He","year":"2018","unstructured":"He Y, Xie H, Wong TL, Wang XZ (2018) A novel binary artificial bee colony algorithm for the set-union knapsack problem. Future Gener Comput Syst 78:77\u201386","journal-title":"Future Gener Comput Syst"},{"key":"811_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/919406","volume":"2014","author":"A Banharnsakun","year":"2014","unstructured":"Banharnsakun A, Tanathong S (2014) Object detection based on template matching through use of best-so-far ABC. Comput Intell Neurosci 2014:1\u20138","journal-title":"Comput Intell Neurosci"},{"key":"811_CR25","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s13042-015-0471-1","volume":"8","author":"A Banharnsakun","year":"2017","unstructured":"Banharnsakun A (2017) Hybrid ABC-ANN for pavement surface distress detection and classification. Int J Mach Learn Cybernet 8:699\u2013710","journal-title":"Int J Mach Learn Cybernet"},{"key":"811_CR26","doi-asserted-by":"publisher","first-page":"241","DOI":"10.2307\/2348448","volume":"44","author":"SP Brooks","year":"1995","unstructured":"Brooks SP, Morgan BJT (1995) Optimization using simulated annealing. The Statistician 44:241\u2013257","journal-title":"The Statistician"},{"key":"811_CR27","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1023\/A:1022602019183","volume":"3","author":"DE Goldberg","year":"1988","unstructured":"Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95\u201399","journal-title":"Mach Learn"},{"key":"811_CR28","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol\u00a04, pp\u00a01942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"811_CR29","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"DC Liu","year":"1989","unstructured":"Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45:503\u2013528","journal-title":"Math Program"},{"key":"811_CR30","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10462-012-9328-0","volume":"42","author":"D Karaboga","year":"2014","unstructured":"Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21\u201357","journal-title":"Artif Intell Rev"},{"key":"811_CR31","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.engappai.2017.09.002","volume":"67","author":"A Caliskan","year":"2018","unstructured":"Caliskan A, Yuksel ME, Badem H, Basturk A (2018) Performance improvement of deep neural network classifiers by a simple training strategy. Eng Appl Artif Intell 67:14\u201323","journal-title":"Eng Appl Artif Intell"},{"key":"811_CR32","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.neucom.2017.05.061","volume":"266","author":"H Badem","year":"2017","unstructured":"Badem H, Basturk A, Caliskan A, Yuksel ME (2017) A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms. Neurocomputing 266:506\u2013526","journal-title":"Neurocomputing"},{"key":"811_CR33","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.procs.2015.12.114","volume":"72","author":"LR Rere","year":"2015","unstructured":"Rere LR, Fanany MI, Arymurthy AM (2015) Simulated annealing algorithm for deep learning. Proc Comput Sci 72:137\u2013144","journal-title":"Proc Comput Sci"},{"key":"811_CR34","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.patcog.2016.01.012","volume":"59","author":"EP Ijjina","year":"2016","unstructured":"Ijjina EP, Chalavadi KM (2016) Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recogn 59:199\u2013212","journal-title":"Pattern Recogn"},{"key":"811_CR35","first-page":"79","volume":"6","author":"HM Albeahdili","year":"2016","unstructured":"Albeahdili HM, Han T, Islam NE (2016) Hybrid algorithm for the optimization of training convolutional neural network. Int J Adv Comput Sci Appl 6:79\u201385","journal-title":"Int J Adv Comput Sci Appl"},{"key":"811_CR36","doi-asserted-by":"crossref","unstructured":"Banharnsakun A, Achalakul T, Sirinaovakul B (2010) Artificial bee colony algorithm on distributed environments. In: Proceedings of the IEEE 2nd World Congress on Nature and Biologically Inspired Computing, pp.\u00a013\u201318","DOI":"10.1109\/NABIC.2010.5716309"},{"key":"811_CR37","volume-title":"Introduction to parallel computing","author":"A Grama","year":"2003","unstructured":"Grama A, Gupta A, Karypis G, Kumar V (2003) Introduction to parallel computing. Addison-Wesley, New York"},{"key":"811_CR38","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE 86, pp 2278\u20132324","DOI":"10.1109\/5.726791"},{"key":"811_CR39","doi-asserted-by":"crossref","unstructured":"LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp\u00a0253\u2013256","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"811_CR40","doi-asserted-by":"crossref","unstructured":"Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Proceedings of Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp\u00a065\u201374","DOI":"10.1007\/978-3-642-12538-6_6"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0811-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-018-0811-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0811-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T13:07:10Z","timestamp":1558616830000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-018-0811-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,21]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["811"],"URL":"https:\/\/doi.org\/10.1007\/s13042-018-0811-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,21]]},"assertion":[{"value":"19 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}