Harnessing Artificial Intelligence for Efficient and Sustainable Garments: A Predictive Analysis of Deep Learning and Machine Learning from Selected Areas around Dhaka City

Harnessing Artificial Intelligence for Efficient and Sustainable Garments: A Predictive Analysis of Deep Learning and Machine Learning from Selected Areas around Dhaka City | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Harnessing Artificial Intelligence for Efficient and Sustainable Garments: A Predictive Analysis of Deep Learning and Machine Learning from Selected Areas around Dhaka City Kazi Redwan Rafi, Mithila Rahman, Dr Callum Russell, Nurjahan Begum, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7241903/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Garment industries produce a significant amount of materials and process waste, which has become an environmental and economical concern. Detecting or classifying those wastes at an initial period can be challenging Implementation of Artificial intelligence (AI) constitutes a crucial element in the prediction and classification of waste materials. Machine learning (ML) and deep learning (DL) can forecast waste quantities and differentiate various waste types based on multiple datasets. To forecast this problem, specific AI models have been applied. Artificial neural network (ANN), convoluted neural network (CNN) models such as, mobileNetV2, denseNet121, efficientNetB0, random forest (RF), gradient boosting (GB) were used as AI models The dataset underwent several preprocessing procedures. To evaluate the effectiveness of the AI model implementation, metrics including accuracy, F1-score, recall, precision, training and validation accuracy, training and validation loss, and MAE were employed. The result shows validation accuracies of MobileNetV2, improved MobileNetV2, DenseNet121, EfficientNetB0, improved EfficientNetB0 are 85.12%,81.83%,83.86%, 93.89%,94.67% respectively by utilizing data augmentation, hyperparameter tuning, adam optimization, softmax, ReLU activation function, transfer learning etc. Random forest gave accuracy = 81.72%, precision = 82.10% F1 score = 81.18% and gradient boosting accuracy = 81.19%, precision = 82.16% F1 score = 80.86% and R 2 of 99.78% for small dataset and 90.31% for big dataset had been shown by ANN and with improvement 95.17% has been showed by utilizing hyperparameter tuning, data scaling, adding more hidden layers etc. These suggested multi-AI models can contribute to predicting and detecting waste and provide valuable insights for the garments sector to a more sustainable and environmentally responsible industry. Environmental Engineering Artificial Intelligence Deep Learning Machine Learning Waste management Garments sustainability Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7241903","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492514772,"identity":"0f21fa3e-6caa-433f-8e13-c6c8a7c4acdc","order_by":0,"name":"Kazi Redwan 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industries produce a significant amount of materials and process waste, which has become an environmental and economical concern. Detecting or classifying those wastes at an initial period can be challenging Implementation of Artificial intelligence (AI) constitutes a crucial element in the prediction and classification of waste materials. Machine learning (ML) and deep learning (DL) can forecast waste quantities and differentiate various waste types based on multiple datasets. To forecast this problem, specific AI models have been applied. Artificial neural network (ANN), convoluted neural network (CNN) models such as, mobileNetV2, denseNet121, efficientNetB0, random forest (RF), gradient boosting (GB) were used as AI models The dataset underwent several preprocessing procedures. To evaluate the effectiveness of the AI model implementation, metrics including accuracy, F1-score, recall, precision, training and validation accuracy, training and validation loss, and MAE were employed. The result shows validation accuracies of MobileNetV2, improved MobileNetV2, DenseNet121, EfficientNetB0, improved EfficientNetB0 are 85.12%,81.83%,83.86%, 93.89%,94.67% respectively by utilizing data augmentation, hyperparameter tuning, adam optimization, softmax, ReLU activation function, transfer learning etc. Random forest gave accuracy\u0026thinsp;=\u0026thinsp;81.72%, precision\u0026thinsp;=\u0026thinsp;82.10% F1 score\u0026thinsp;=\u0026thinsp;81.18% and gradient boosting accuracy\u0026thinsp;=\u0026thinsp;81.19%, precision\u0026thinsp;=\u0026thinsp;82.16% F1 score\u0026thinsp;=\u0026thinsp;80.86% and R\u003csup\u003e2\u003c/sup\u003e of 99.78% for small dataset and 90.31% for big dataset had been shown by ANN and with improvement 95.17% has been showed by utilizing hyperparameter tuning, data scaling, adding more hidden layers etc. These suggested multi-AI models can contribute to predicting and detecting waste and provide valuable insights for the garments sector to a more sustainable and environmentally responsible industry.\u003c/p\u003e","manuscriptTitle":"Harnessing Artificial Intelligence for Efficient and Sustainable Garments: A Predictive Analysis of Deep Learning and Machine Learning from Selected Areas around Dhaka City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 04:55:16","doi":"10.21203/rs.3.rs-7241903/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d139ed1d-519c-4664-afb7-1a45f829ba3b","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52293637,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2025-07-30T04:55:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 04:55:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7241903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7241903","identity":"rs-7241903","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}