COVID-19 in India: Lockdown analysis and future predictions using Regression models

Prathyusha, K; Helini, K; Raghavendran, Ch. V.; Kurumeti, Nsl Kumar

doi:10.1109/confluence51648.2021.9377052

Cited by 7 publications

(2 citation statements)

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“…The Exploratory Data Analysis (EDA) is a main step in modeling Machine Learning algorithms. EDA helps to evaluate the data sets and to outline the statistical features [18][19][20][21]. The workflow of the proposed methodology is represented in Fig 4.…”

Section: Discussionmentioning

confidence: 99%

Network Intrusion Detection using ML Techniques for Sustainable Information System

Chandra Mouli,

Indupriya,

Ushasree

et al. 2023

E3S Web Conf.

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Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explores various machine learning algorithms employed for intrusion detection and evaluates their performance. Findings indicate that machine learning algorithms exhibit a significant improvement in intrusion detection accuracy compared to traditional methods, achieving an accuracy rate of approximately 90 percent. It is worth noting that the previous work experienced computational challenges due to the time-consuming nature of the utilized algorithm when processing datasets. In this paper, we propose the exertion of more efficient algorithms to compute datasets, resulting in reduced processing time and increased precision compared to other algorithms to provide sustainability. This approach proves particularly when computational resources are limited or when the relationship between features and target variables is relatively straightforward.

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Section: Discussionmentioning

confidence: 99%

Network Intrusion Detection using ML Techniques for Sustainable Information System

Chandra Mouli,

Indupriya,

Ushasree

et al. 2023

E3S Web Conf.

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“…They collected X-ray chest images of 1560 covid patients and with their model they achieved an accuracy of 90%. Prathyusha K. [4] et.al applied various machine learning regression algorithms like linear regressor, polynomial regressor and achieved the best results with the polynomial regression technique. A. Lakshmanarao [5] et.al applied various regression techniques for analyzing and predicting corona disease and achieved good results with linear regression.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

2021

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<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>

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COVID-19 Vaccination Monitoring Using IoT and Machine Learning

Chahal

Gulia

Gill

2022

Lecture Notes on Data Engineering and Communications Technologies

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COVID-19 in India: Lockdown analysis and future predictions using Regression models

Cited by 7 publications

References 10 publications

Network Intrusion Detection using ML Techniques for Sustainable Information System

Network Intrusion Detection using ML Techniques for Sustainable Information System

An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

COVID-19 Vaccination Monitoring Using IoT and Machine Learning

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