{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T04:02:21Z","timestamp":1746331341879,"version":"3.40.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319076911"},{"type":"electronic","value":"9783319076928"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-07692-8_17","type":"book-chapter","created":{"date-parts":[[2014,5,29]],"date-time":"2014-05-29T16:19:57Z","timestamp":1401380397000},"page":"173-182","source":"Crossref","is-referenced-by-count":3,"title":["CSLMEN: A New Cuckoo Search Levenberg Marquardt Elman Network for Data Classification"],"prefix":"10.1007","author":[{"given":"Nazri Mohd","family":"Nawi","sequence":"first","affiliation":[]},{"given":"Abdullah","family":"Khan","sequence":"additional","affiliation":[]},{"given":"M. Z.","family":"Rehman","sequence":"additional","affiliation":[]},{"given":"Tutut","family":"Herawan","sequence":"additional","affiliation":[]},{"given":"Mustafa Mat","family":"Deris","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"17_CR1","unstructured":"Firdaus, A.A.M., Mariyam, S.H.S., Razana, A.: Enhancement of Particle Swarm Optimization in Elman Recurrent Network with bounded Vmax Function. In: Third Asia International Conference on Modelling & Simulation (2009)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Peng, X.G., Venayagamoorthy, K., Corzin, K.A.: Combined Training of Recurrent Neural Networks with Particle Swarm Optimization and Back propagation Algorithms for Impedance Identification. In: IEEE Swarm Intelligence Symposium, pp. 9\u201315 (2007)","DOI":"10.1109\/SIS.2007.368020"},{"key":"17_CR3","unstructured":"Haykin, S.: Neural Networks:A Comprehensive Foundation, 2nd edn., pp. 84\u201389 (1999) ISBN 0-13-273350"},{"issue":"4","key":"17_CR4","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1016\/j.eswa.2007.04.002","volume":"34","author":"E.D. Ubeyli","year":"2008","unstructured":"Ubeyli, E.D.: Recurrent neural networks employing lyapunov exponents for analysis of Doppler ultrasound signals. J. Expert Systems with Applications\u00a034(4), 2538\u20132544 (2008)","journal-title":"J. Expert Systems with Applications"},{"issue":"3","key":"17_CR5","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.compbiomed.2008.01.002","volume":"38","author":"E.D. Ubeyli","year":"2008","unstructured":"Ubeyli, E.D.: Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients. J. Computers in Biology and Medicine\u00a038(3), 401\u2013410 (2008)","journal-title":"J. Computers in Biology and Medicine"},{"issue":"6","key":"17_CR6","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/72.728395","volume":"9","author":"E.W. Saad","year":"1998","unstructured":"Saad, E.W., Prokhorov, D.V., WunschII, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. J. IEEE Transactions on Neural Networks\u00a09(6), 1456\u20131470 (1998)","journal-title":"J. IEEE Transactions on Neural Networks"},{"issue":"12","key":"17_CR7","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1016\/S0031-3203(99)00187-9","volume":"33","author":"L. Gupta","year":"2000","unstructured":"Gupta, L., McAvoy, M.: Investigating the prediction capabilities of the simple recurrent neural network on real temporal sequences. J. Pattern Recognition\u00a033(12), 2075\u20132081 (2000)","journal-title":"J. Pattern Recognition"},{"issue":"10","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/S0031-3203(99)00149-1","volume":"33","author":"L. Gupta","year":"2000","unstructured":"Gupta, L., McAvoy, M., Phegley, J.: Classification of temporal sequences via prediction using the simple recurrent neural network. J. Pattern Recognition\u00a033(10), 1759\u20131770 (2000)","journal-title":"J. Pattern Recognition"},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.eswa.2005.04.011","volume":"29","author":"G.F. Nihal","year":"2005","unstructured":"Nihal, G.F., Elif, U.D., Inan, G.: Recurrent neural networks employing lyapunov exponents for EEG signals classification. J. Expert Systems with Applications\u00a029, 506\u2013514 (2005)","journal-title":"J. Expert Systems with Applications"},{"issue":"2","key":"17_CR10","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"J.L. Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. J. Cognitive Science\u00a014(2), 179\u2013211 (1990)","journal-title":"J. Cognitive Science"},{"issue":"4","key":"17_CR11","first-page":"838","volume":"1","author":"M.Z. Rehman","year":"2012","unstructured":"Rehman, M.Z., Nawi, N.M.: Improving the Accuracy of Gradient Descent Back Propagation Algorithm(GDAM) on Classification Problems. Int. J. of New Computer Architectures and their Applications (IJNCAA)\u00a01(4), 838\u2013847 (2012)","journal-title":"Int. J. of New Computer Architectures and their Applications (IJNCAA)"},{"key":"17_CR12","unstructured":"Wam, A., Esm, S., Esa, A.: Modified Back Propagation Algorithm for Learning Artificial Neural Networks. In: The 18th National Radio Science Conference, pp. 345\u2013352 (2001)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Wen, J., Zhao, J.L., Luo, S.W., Han, Z.: The Improvements of BP Neural Network Learning Algorithm. In: 5th Int. Conf. on Signal Processing WCCC-ICSP, pp. 1647\u20131649 (2000)","DOI":"10.1109\/ICOSP.2000.893417"},{"issue":"2","key":"17_CR14","first-page":"257","volume":"9","author":"Y. Tanoto","year":"2011","unstructured":"Tanoto, Y., Ongsakul, W., Charles, O., Marpaung, P.: Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting. J. Telkomnika\u00a09(2), 257\u2013266 (2011)","journal-title":"J. Telkomnika"},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s00521-010-0493-2","volume":"20","author":"C. Peng","year":"2011","unstructured":"Peng, C., Magoulas, G.D.: NonmonotoneLevenberg\u2013Marquardt training of recurrent neural architectures for processing symbolic sequences. J. of Neural Comput& Application\u00a020, 897\u2013908 (2011)","journal-title":"J. of Neural Comput& Application"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Yang, X.S., Deb, S.: Cuckoo search via L\u00e9vy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing, India, pp. 210\u2013214 (2009)","DOI":"10.1109\/NABIC.2009.5393690"},{"issue":"4","key":"17_CR17","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1504\/IJMMNO.2010.035430","volume":"1","author":"X.S. Yang","year":"2010","unstructured":"Yang, X.S., Deb, S.: Engineering Optimisation by Cuckoo Search. J. International Journal of Mathematical Modelling and Numerical Optimisation\u00a01(4), 330\u2013343 (2010)","journal-title":"J. International Journal of Mathematical Modelling and Numerical Optimisation"},{"key":"17_CR18","unstructured":"Tuba, M., Subotic, M., Stanarevic, N.: Modified cuckoo search algorithm for unconstrained optimization problems. In: Proceedings of the European Computing Conference (ECC 2011), pp. 263\u2013268 (2011)"},{"issue":"2","key":"17_CR19","first-page":"62","volume":"11","author":"M. Tuba","year":"2012","unstructured":"Tuba, M., Subotic, M., Stanarevic, N.: Performance of a Modified Cuckoo Search Algorithm for Unconstrained Optimization Problems. J. Faculty of Computer Science\u00a011(2), 62\u201374 (2012)","journal-title":"J. Faculty of Computer Science"},{"issue":"4","key":"17_CR20","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1504\/IJMMNO.2010.035430","volume":"1","author":"X.S. Yang","year":"2010","unstructured":"Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. J. Int. J. Mathematical Modelling and Numerical Optimisation\u00a01(4), 330\u2013343 (2010)","journal-title":"J. Int. J. Mathematical Modelling and Numerical Optimisation"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Chaowanawate, K., Heednacram, A.: Implementation of Cuckoo Search in RBF Neural Network for Flood Forecasting. In: 4th International Conference on Computational Intelligence, Communication Systems and Networks, pp. 22\u201326 (2012)","DOI":"10.1109\/CICSyN.2012.15"},{"issue":"6","key":"17_CR22","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/72.329697","volume":"5","author":"M.T. Hagan","year":"1999","unstructured":"Hagan, M.T., Menhaj, M.B.: Training Feedforward Networks with the Marquardt Algorithm. J. IEEE Transactions on Neural Networks\u00a05(6), 989\u2013993 (1999)","journal-title":"J. IEEE Transactions on Neural Networks"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Nourani, E., Rahmani, A.M., Navin, A.H.: Forecasting Stock Prices using a hybrid Artificial Bee Colony based Neural Network. In: ICIMTR 2012, Malacca, Malaysia, pp. 21\u201322 (2012)","DOI":"10.1109\/ICIMTR.2012.6236444"},{"key":"17_CR24","first-page":"432","volume":"9","author":"M.Z. Rehman","year":"2012","unstructured":"Rehman, M.Z., Nawi, N.M.: Studying the Effect of adaptive momentum in improving the accuracy of gradient descent back propagation algorithm on classification problems. J. Int. Journal of Modern Physics: Conference Series\u00a09, 432\u2013439 (2012)","journal-title":"J. Int. Journal of Modern Physics: Conference Series"}],"container-title":["Advances in Intelligent Systems and Computing","Recent Advances on Soft Computing and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-07692-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T04:57:18Z","timestamp":1746248238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-07692-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319076911","9783319076928"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-07692-8_17","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2014]]}}}