{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:53:37Z","timestamp":1775145217645,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11277-022-09718-7","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T08:02:50Z","timestamp":1656403370000},"page":"3425-3441","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account"],"prefix":"10.1007","volume":"125","author":[{"given":"Pouya","family":"Demokri Dizji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saba","family":"Joudaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoshang","family":"Kolivand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"9718_CR1","doi-asserted-by":"crossref","unstructured":"Novak, B., Ilic, V., & Pavkovic, B. (2020). YOLOv3 Algorithm with additional convolutional neural network trained for traffic sign recognition.","DOI":"10.1109\/ZINC50678.2020.9161446"},{"key":"9718_CR2","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","volume":"32","author":"J Stallkamp","year":"2012","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, 32, 323\u2013332.","journal-title":"Neural Networks"},{"key":"9718_CR3","unstructured":"Yuheng, S., & Hao, Y. (2017). Image segmentation algorithms overview. CoRR, vol. abs\/1707.02051."},{"key":"9718_CR4","doi-asserted-by":"crossref","unstructured":"Sermanet, P., & LeCun, Y. (2011). Traffic sign recognition with multi-scale convolutional networks. In The 2011 international joint conference on neural networks.","DOI":"10.1109\/IJCNN.2011.6033589"},{"key":"9718_CR5","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). OverFeat: Integrated recognition, localization and detection using convolutional networks. CoRR."},{"key":"9718_CR6","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011) The German traffic sign recognition benchmark: A multi-class classification competition. In The 2011 international joint conference on neural networks.","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"9718_CR7","doi-asserted-by":"crossref","unstructured":"Finlayson, G. D., Schiele, B., & Crowley, J. L. (1998). Comprehensive colour image normalization. In Computer vision\u2014ECCV'98. Berlin.","DOI":"10.1007\/BFb0055685"},{"issue":"1","key":"9718_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJVS.2021.115888","volume":"12","author":"A Zaibi","year":"2021","unstructured":"Zaibi, A., Ladgham, A., & Sakly, A. (2021). Road sign detection using edited shuffled frogs leaping algorithm. International Journal of Vehicle Safety, 12(1), 1\u201314.","journal-title":"International Journal of Vehicle Safety"},{"key":"9718_CR9","doi-asserted-by":"publisher","first-page":"114481","DOI":"10.1016\/j.eswa.2020.114481","volume":"168","author":"WA Haque","year":"2021","unstructured":"Haque, W. A., Arefin, S., Shihavuddin, A. S. M., & Hasan, M. A. (2021). DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements. Expert Systems with Applications, 168, 114481.","journal-title":"Expert Systems with Applications"},{"key":"9718_CR10","doi-asserted-by":"crossref","unstructured":"Maaroof, B. B., Rashid, T. A., Abdulla, J. M., Hassan, B. A., Alsadoon, A., Mohamadi, M., & Mirjalili, S. (2022). Current studies and applications of shuffled frog leaping algorithm: A review. Archives of Computational Methods in Engineering, 1\u201316.","DOI":"10.1007\/s11831-021-09707-2"},{"key":"9718_CR11","unstructured":"Toth, \u0160. (2012). Difficulties of traffic sign recognition. In Mathematics applied to ICT."},{"key":"9718_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/03052150500384759","volume":"38","author":"M Eusuff","year":"2006","unstructured":"Eusuff, M., Lansey, K., & Pasha, F. (2006). Shuffled frog leaping algorithm: A memtic meta heuristic for discrete optimization. Engineering Optimization, 38, 129\u2013154.","journal-title":"Engineering Optimization"},{"key":"9718_CR13","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.asoc.2015.12.041","volume":"46","author":"A Ellahyani","year":"2016","unstructured":"Ellahyani, A., Ansari, M. E., & Jaafari, I. E. (2016). Traffic sign detection and recognition based on random forests. Applied Soft Computing, 46, 805\u2013815.","journal-title":"Applied Soft Computing"},{"key":"9718_CR14","unstructured":"Tungkasthan, A., & Premchaiswadi, W. (2011). Automatic region of interest detection in natural images."},{"key":"9718_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.5120\/18229-9167","volume":"104","author":"R Ebrahimzadeh","year":"2014","unstructured":"Ebrahimzadeh, R., & Jampour, M. (2014). Efficient handwritten digit recognition based on histogram of oriented gradients and SVM. International Journal of Computer Applications, 104, 10\u201313.","journal-title":"International Journal of Computer Applications"},{"key":"9718_CR16","first-page":"370","volume":"13","author":"A Tehami","year":"2017","unstructured":"Tehami, A., & Hadria, F. (2017). Unsupervised segmentation of images based on shuffled frog-leaping algorithm. JIPS, 13, 370\u2013384.","journal-title":"JIPS"},{"key":"9718_CR17","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05).","DOI":"10.1109\/CVPR.2005.177"},{"issue":"9","key":"9718_CR18","first-page":"599","volume":"5","author":"M Horng","year":"2013","unstructured":"Horng, M. (2013). Multilevel image threshold selection based on the shuffled frog-leaping algorithm. Journal of Chemical and Pharmaceutical Research, 5(9), 599\u2013605.","journal-title":"Journal of Chemical and Pharmaceutical Research"},{"key":"9718_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S. (2016). Traffic-sign detection and classification in the wild. In 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, Nevada, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"9718_CR20","first-page":"264","volume":"2","author":"S Lafuente-Arroyo","year":"2007","unstructured":"Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., & Lopez-Ferreras, F. (2007). Road-sign detection and recognition based on support vector machines. IEEE Transactions on Intelligent Transportation Systems, 2, 264\u2013278.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"9718_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05279-7","author":"H Kolivand","year":"2020","unstructured":"Kolivand, H., Joudaki, S., Sunar, M. S., & Tully, D. (2020). A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1). Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-020-05279-7","journal-title":"Neural Computing and Applications"},{"key":"9718_CR22","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 25 (pp. 1097\u20131105). Curran Associates, Inc."},{"issue":"3","key":"9718_CR23","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.1007\/s11063-019-09991-x","volume":"50","author":"A Jain","year":"2019","unstructured":"Jain, A., Mishra, A., Shukla, A., & Tiwari, R. (2019). A novel genetically optimized convolutional neural network for traffic sign recognition: A new benchmark on Belgium and Chinese traffic sign datasets. Neural Processing Letters, 50(3), 3019\u20133043.","journal-title":"Neural Processing Letters"},{"key":"9718_CR24","doi-asserted-by":"publisher","first-page":"13885","DOI":"10.1007\/s00521-021-06025-3","volume":"33","author":"H Kolivand","year":"2021","unstructured":"Kolivand, H., Joudaki, S., Sunar, M. S., & Tully, D. (2021). An implementation of sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 2). Neural Computing and Applications, 33, 13885\u201313907.","journal-title":"Neural Computing and Applications"},{"key":"9718_CR25","first-page":"282","volume":"19","author":"I Cinar","year":"2020","unstructured":"Cinar, I., Taspinar, Y. S., Saritas, M. M., & Koklu, M. (2020). Feature extraction and recognition on traffic sign images. Journal of Selcuk-Technic \u00d6zel Say\u0131 2020 (ICAT\u201920) Special, 19, 282\u2013292.","journal-title":"Journal of Selcuk-Technic \u00d6zel Say\u0131 2020 (ICAT\u201920) Special"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09718-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-022-09718-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09718-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:50:44Z","timestamp":1744156244000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-022-09718-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":25,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["9718"],"URL":"https:\/\/doi.org\/10.1007\/s11277-022-09718-7","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,28]]},"assertion":[{"value":"14 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}