A Smart Handling of Bio-Medical Waste and its Segregation with Intelligant Machine Learning Model

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI:10.1109/ICDT57929.2023.10150547

Brijendra Gupta, P. Sreelatha, M. Shanmathi, John Philip Bhimavarapu, P. John Augustine, K. Sathyarajasekaran

{"title":"A Smart Handling of Bio-Medical Waste and its Segregation with Intelligant Machine Learning Model","authors":"Brijendra Gupta, P. Sreelatha, M. Shanmathi, John Philip Bhimavarapu, P. John Augustine, K. Sathyarajasekaran","doi":"10.1109/ICDT57929.2023.10150547","DOIUrl":null,"url":null,"abstract":"The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the Biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Disruptive Technologies (ICDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDT57929.2023.10150547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the Biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于智能机器学习模型的生物医疗垃圾智能处理及其分离

生物医疗废物管理系统组织医院日常医疗废物的处理。每天医院的医疗废物都通过它运送。一个单独的系统用于处理医疗用品，包括针头、塑料、玻璃器皿、医疗服、过期药物和人类废物。在此基础上，他们利用生物医学废物管理中心接收医院的日常医疗废物并进行适当处理。任何医院都不该处理医疗垃圾。这是非法的，负责的医院必须对医疗废物进行适当分类，并将其送到生物医学废物处理设施。本文提出了一种智能机器学习模型来处理不同的生物医疗浪费，并根据医疗规则对其进行分类。在医院处理的医疗废物被安全运输和焚烧。所提出的模型有助于处理这类通常具有传染性的医疗废物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2023 International Conference on Disruptive Technologies (ICDT)

自引率

0.00%

发文量

期刊最新文献

Best Ways Using AI in Impacting Success on MBA Graduates A Mechanism Used to Predict Diet Consumption and Stress Management in Humans Using IoMT ICDT 2023 Cover Page Machine Learning-Based Approach for Hand Gesture Recognition A Smart Innovation of Business Intelligence Based Analytical Model by Using POS Based Deep Learning Model