Forecast the death and recovery rate of COVID 2019 using ARIMA and PROPHET models

Sirikonda, Shwetha; Kumar, Sunil; Sravanthi, T.; Srinivas, J.; Manchikatla, Sai Teja; Kumaraswamy, Eelandula

doi:10.1063/5.0081771

Cited by 31 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tumors that are detected early are less likely to transmit to certain other portions of the body, but late detection makes treatments challenging and, in certain cases, catastrophic, along with affecting people's social lives. Different Machine Learning methods [19], Computer vision [17], neural network organization [21], and classification [22] [23] approaches have been used to help recognize, segment, and define skin cancerous growths. The different kinds of cancerous lesions described in this study are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

View full text Add to dashboard Cite

Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.

show abstract

Section: Introductionmentioning

confidence: 99%