{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:35:50Z","timestamp":1770748550449,"version":"3.50.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2015,9,23]],"date-time":"2015-09-23T00:00:00Z","timestamp":1442966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Profoundly hearing-impaired community (PHIC) cannot moderate wisely an acoustic noise emanated from moving vehicle in outdoor environment. Due to this, they have difficulties to distinguish type and the distance of moving vehicles especially the one comes from the rear. Hence, they are at risk whenever they are outdoors. In this paper, a simple system is proposed to identify the type and distance (zone-based) of a moving vehicle using a multi-classifier system (MCS). One-third octave filter bands approach has been used for extracting the significant feature from the noise emanated by the moving vehicle. The extracted features were associated with the type and zone of the moving vehicle and the MCS based on multilayer perceptron has been developed. The developed multilayer perceptron model with the same hidden neuron and training algorithm has been proposed for MCS. This network has been tested for single classifier and MCS. The developed MCS has improved the classification accuracy compared to single classifier.<\/jats:p>","DOI":"10.3233\/ifs-151581","type":"journal-article","created":{"date-parts":[[2015,10,6]],"date-time":"2015-10-06T12:00:35Z","timestamp":1444132835000},"page":"149-157","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Homogeneous multi-classifier system for\u00a0moving vehicles noiseclassification based\u00a0on\u00a0multilayer perceptron"],"prefix":"10.1177","volume":"29","author":[{"given":"Norasmadi","family":"Abdul Rahim","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia"}]},{"given":"MP","family":"Paulraj","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia"}]},{"given":"Abdul Hamid","family":"Adom","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia"}]},{"given":"Shazmin Aniza Abdul","family":"Shukor","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia"}]},{"given":"Maz Jamilah","family":"Masnan","sequence":"additional","affiliation":[{"name":"Institute of Engineering Mathematics, Universiti Malaysia Perlis, Arau, Perlis, Malaysia"}]}],"member":"179","published-online":{"date-parts":[[2015,9,23]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"DSP based Acoustic Vehicle Classification for Multi-Sensor Real-Time Traffic Surveillance, Poznan, Poland","author":"Klausner A","year":"2007","unstructured":"KlausnerAErbSTenggARinnerB2007DSP based Acoustic Vehicle Classification for Multi-Sensor Real-Time Traffic Surveillance, Poznan, PolandPresented at the 15th European Signal Processing Conference","journal-title":"Presented at the 15th European Signal Processing Conference"},{"key":"e_1_3_2_3_2","first-page":"429","article-title":"Vehicle sound signature recognition by frequency vector principal component analysis","volume":"1","author":"Huadong W","year":"1998","unstructured":"HuadongWSiegelMKhoslaP1998Vehicle sound signature recognition by frequency vector principal component analysisin Instrumentation and Measurement Technology Conference, 1998 IMTC\/98 Conference Proceedings IEEEvol. 1429434","journal-title":"in Instrumentation and Measurement Technology Conference, 1998 IMTC\/98 Conference Proceedings IEEE"},{"key":"e_1_3_2_4_2","first-page":"5","article-title":"Neural Networks for Vehicle Recognition","author":"Maciejewski H","year":"1997","unstructured":"MaciejewskiHMazurkiewiczJSkowronKWalkowiakT1997Neural Networks for Vehicle Recognitionin Proceeding of the 6th International Conference on Microelectronics for Neural Networks, Evolutionary and Fuzzy Systems5","journal-title":"in Proceeding of the 6th International Conference on Microelectronics for Neural Networks, Evolutionary and Fuzzy Systems"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008455010040"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-008-0058-z"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008475713345"},{"key":"e_1_3_2_8_2","first-page":"1","article-title":"Bayesian Subspace Methods for Acoustic Signature Recognition of Vehicles","author":"Munich ME","year":"2004","unstructured":"MunichME2004Bayesian Subspace Methods for Acoustic Signature Recognition of Vehiclesin Proceeding of the 12th European Signal Processing Conference14","journal-title":"in Proceeding of the 12th European Signal Processing Conference"},{"key":"e_1_3_2_9_2","first-page":"1336","article-title":"Nonlinear Hebbian Learning for noise-independent vehicle sound recognition","author":"Bing L","year":"2008","unstructured":"BingLDibazarABergerTW2008Nonlinear Hebbian Learning for noise-independent vehicle sound recognitionin Neural Networks, 2008. 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