{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:04:25Z","timestamp":1775667865225,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.<\/jats:p>","DOI":"10.3390\/s21144916","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T21:39:15Z","timestamp":1626730755000},"page":"4916","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron"],"prefix":"10.3390","volume":"21","author":[{"given":"Ali Usman","family":"Gondal","sequence":"first","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"given":"Muhammad Imran","family":"Sadiq","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"given":"Tariq","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-6875","authenticated-orcid":false,"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0633-5587","authenticated-orcid":false,"given":"Ahmad","family":"Shaf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0580-3921","authenticated-orcid":false,"given":"Muhammad","family":"Aamir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"given":"Muhammad","family":"Shoaib","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0546-7083","authenticated-orcid":false,"given":"Adam","family":"Glowacz","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krak\u00f3w, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9675-5819","authenticated-orcid":false,"given":"Ryszard","family":"Tadeusiewicz","sequence":"additional","affiliation":[{"name":"Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krak\u00f3w, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-6177","authenticated-orcid":false,"given":"Eliasz","family":"Kantoch","sequence":"additional","affiliation":[{"name":"Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. 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