Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Фев. 27, 2025

Язык: Английский

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A Deep Learning Approach for Cost-Effective and Environmentally Sustainable Waste Transportation Systems in Developing Countries

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 145314 - 145314

Опубликована: Март 1, 2025

Язык: Английский

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Adaptive Anomaly Detection for Secure Federated Learning in IIoT Environments

Procedia Computer Science, Год журнала: 2025, Номер 258, С. 536 - 551

Опубликована: Янв. 1, 2025

Язык: Английский

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Towards integrative multi‐stakeholder responsibility for net zero in e‐waste: A systematic literature review

Business Strategy and the Environment, Год журнала: 2024, Номер unknown

Опубликована: Сен. 14, 2024

Abstract Despite extensive research on e‐waste management, the integrative responsibilities of multi‐stakeholders and dependency technology remain underexplored. This study aims to develop an multi‐stakeholder responsibility model advance net‐zero goal in management. Following PRISMA protocol, we conducted a systematic review 99 articles. The revealed three themes stakeholder responsibility, four performance measure categories, solutions, roles smart technologies. These insights informed development conceptual enhance environmental e‐waste. proposed emphasises critical attributes relationships, including partnership, shared inclusiveness, transparency; it offers practical guidance for prioritising efficient mature technologies be adopted diffused, enabling effective Our extends theory from organisation‐centric problem‐based perspective, highlighting its implications management suggesting future directions sustainability.

Язык: Английский

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E-Waste Collection Under Recycling Hub Demand and Partial Information: The Benchmark Solution in a Pilot Case

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 219 - 237

Опубликована: Янв. 1, 2025

Язык: Английский

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EADCN-BCSR: A novel framework for accurate and real-time waste detection and classification

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Апрель 17, 2025

Язык: Английский

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E-waste prediction and optimal route selection using adaptive deep Markov random field and block chain

Kybernetes, Год журнала: 2024, Номер unknown

Опубликована: Май 29, 2024

Purpose An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin using Internet of things (IoT) sensors generated. The detect the level waste in dustbin. data collected IoT sensor stored blockchain. an adaptive Markov random field (ADMRF) method implemented to determine weight wastes. performance ADMRF boosted optimizing its parameters with help improved corona virus herd immunity optimization algorithm (ICVHIOA). main objective developed ADMRF-based prediction minimize root mean square error (RMSE) and absolute (MAE) rate at time testing. If bins more than 80%, then alert message will be sent collector directly. Optimal route selection carried out ICVHIOA for collection wastes from bin. objectives optimal are reduce distance operational cost environmental impacts. considered recycling. blockchain-based dustbin evaluated comparing it other existing dustbins management. Design/methodology/approach used collect avoid certain diseases caused dumped waste. Disposal recycling necessary decrease pollution manufacture new products Findings RMSE framework was 33.65% better convolutional neural network (CNN), 27.12% increased recurrent (RNN), 22.27% advanced Resnet 9.99% superior long short-term memory (LSTM). Originality/value proposed E-waste has given enhanced also when compared conventional methods.

Язык: Английский

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Turning trash into treasure: Exploring the potential of AI in municipal waste management - An in-depth review and future prospects

Journal of Environmental Management, Год журнала: 2024, Номер 373, С. 123658 - 123658

Опубликована: Дек. 9, 2024

Язык: Английский

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AI-Enhanced Image Translation for Seamless Communication in Global Tourism

Опубликована: Апрель 18, 2024

An inventive program called Jargon Lens was created to meet the various language requirements of international visitors. AI-enhanced image translation technology, jargon lens empowers travelers effortlessly overcome barriers by seamlessly translating text in images their preferred language. The core this project is create a user-friendly mobile application that can recognize and translate within accurately real-time. This will enable users easily understand communicate different languages simply using Smartphone camera, scan or upload real-time, ensuring swift accurate understanding surroundings. Beyond personal advantages, supports constructive encounters promote cultural interchange between visitors local populations. As technological solution, aligns with evolving landscape global tourism, where diversity inclusivity are paramount. technology combine's computer vision such as optical character recognition (OCR). OCR algorithms analyze extract textual content natural processing (NLP) perform translation. In conclusion, emerges transformative tool for tourists, offering sophisticated yet easy-to-use solution navigating barriers. With its focus on real-time multilingual translation, stands key enabler enriched hassle-free travel experiences, bridging communication gaps rapidly tourism ecosystem.

Язык: Английский

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Multi-target detection of waste composition in complex environments based on an improved YOLOX-S model

Waste Management, Год журнала: 2024, Номер 190, С. 398 - 408

Опубликована: Окт. 14, 2024

Язык: Английский

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