基于机器学习的智能城市垃圾自动检测与分类

IF 4.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal on Semantic Web and Information Systems Pub Date : 2023-06-09 DOI:10.4018/ijswis.324105
Meena Malik, C. Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi, B. Gupta, A. Albeshri, Ammar Almomani
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引用次数: 1

摘要

机器学习和深度学习是计算机科学中最受欢迎的领域之一,从基础教育到遗传和空间工程,它们都有巨大的应用。机器学习技术在智慧城市发展中的应用已经开始;然而,仍处于起步阶段。智慧城市发展的一个主要挑战是有效的废物管理,通过适当的规划和实施来连接不同的区域,如住宅建筑、酒店、工业和商业机构、运输部门、医疗机构、旅游景点、公共场所等。智慧城市专家在评估和制定有效的废物管理方案方面发挥着重要作用,该方案可以很容易地与整个城市的整体发展规划相结合。在这项工作中,我们提供了一个使用卷积神经网络将城市垃圾自动分类为多个类别的模型。我们用新的数据集对预训练的神经网络模型进行微调来表示正在实现的模型,用于垃圾分类。在这种模式的帮助下,软件和硬件都可以使用低成本的资源开发,并且可以大规模部署,因为这是与整个城市的健康生活提供相关的问题。开发此类模型的主要重要方面是使用预训练模型,并利用迁移学习对特定任务的预训练模型进行微调。
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Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities
Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
审稿时长
20 months
期刊介绍: The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.
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