Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-08-12 DOI:10.1007/s00521-021-06376-x
Supriya Raheja, Shreya Kasturia, Xiaochun Cheng, Manoj Kumar
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引用次数: 0

Abstract

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

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基于机器学习的冠状病毒肺炎疫情扩散预测模型。
冠状病毒大流行在全球范围内影响着人们的健康和繁荣。阳性病例数量的持续增加加剧了全球各国政府的压力。需要一种对疫情进行更准确预测的方法。本文提出了一种新的方法,称为扩散预测模型,用于预测四个国家的冠状病毒病例数:印度、法国、中国和尼泊尔。扩散预测模型主要研究人体接触的扩散过程。该模型考虑了两种传播形式:感染一个人后传播需要一段时间,感染一个人之后立即传播。这使得所提出的模型与其他现有技术的模型不同。它给出的结果比其他最先进的模型更准确。所提出的扩散预测模型预测了未来4周内预计出现的新病例数量。该模型预测了确诊病例、康复病例、死亡病例和活跃病例的数量。该模式可以帮助政府为这场疫情的任何突然上升做好充分准备。从精度和错误率方面对其性能进行了评估,并与支持向量机、逻辑回归模型和卷积神经网络的预测结果进行了比较。结果证明了该模型的有效性。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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