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

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS
Kybernetes Pub Date : 2024-05-30 DOI:10.1108/k-01-2024-0199
P. Santhuja, V. Anbarasu
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引用次数: 0

Abstract

Purpose

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

Design/methodology/approach

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

Findings

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

Originality/value

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

利用自适应深度马尔可夫随机场和区块链进行电子废物预测和最佳路线选择
目的 在深度学习技术的帮助下,开发了一种高效的电子废物管理系统。在这里,使用物联网(IoT)传感器生成了一个智能垃圾箱系统。传感器检测垃圾箱中的垃圾量。物联网传感器收集的数据存储在区块链中。在此,采用自适应深度马尔可夫随机场(ADMRF)方法来确定垃圾的重量。在改进的电晕病毒群免疫优化算法(ICVHIOA)的帮助下,通过优化其参数,提高了 ADMRF 的性能。在此,所开发的基于 ADMRF 的废物重量预测的主要目标是在测试时尽量减少均方根误差 (RMSE) 和平均绝对误差 (MAE)。如果垃圾桶重量超过 80%,则会直接向垃圾收集者发送警报信息。使用所开发的 ICVHIOA 进行最优路线选择,以高效收集智能垃圾桶中的垃圾。优化路线选择的主要目标是缩短距离和时间,从而最大限度地降低运营成本和对环境的影响。收集到的垃圾将被考虑回收利用。通过与其他现有的电子废物管理智能垃圾箱进行比较,对基于物联网和区块链的智能垃圾箱的性能进行了评估。研究结果实施框架的均方根误差比卷积神经网络(CNN)高 33.65%,比递归神经网络(RNN)高 27.12%,比 Resnet 高 22.27%,比长短时记忆(LSTM)高 9.99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Kybernetes
Kybernetes 工程技术-计算机:控制论
CiteScore
4.90
自引率
16.00%
发文量
237
审稿时长
4.3 months
期刊介绍: Kybernetes is the official journal of the UNESCO recognized World Organisation of Systems and Cybernetics (WOSC), and The Cybernetics Society. The journal is an important forum for the exchange of knowledge and information among all those who are interested in cybernetics and systems thinking. It is devoted to improvement in the understanding of human, social, organizational, technological and sustainable aspects of society and their interdependencies. It encourages consideration of a range of theories, methodologies and approaches, and their transdisciplinary links. The spirit of the journal comes from Norbert Wiener''s understanding of cybernetics as "The Human Use of Human Beings." Hence, Kybernetes strives for examination and analysis, based on a systemic frame of reference, of burning issues of ecosystems, society, organizations, businesses and human behavior.
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