The entire world is suffering from a novel disease called covid-19 caused by a coronavirus since 2019. The main reason for the seriousness of the disease is the lack of efficient legitimate medication or vaccine. The World Health Organization (WHO) suggested several precautions to regulate the spread of disease and to reduce the contamination thereby reducing deaths. In this paper, we analysed the covid-19 dataset available in Kaggle. The previous contributions from several authors of similar work focused on covid-19 datasets having a limited number of samples. Our paper used the dataset updated till November 15th 2020. Three different aspects are considered mainly in this paper, namely the number of confirmed cases, number of recovered cases, and number of death cases. All the aspects are analysed in a daily and weekly manner. We applied linear regression, polynomial regression, and holt’s method to predict the future number of confirmed, recovered, and death cases. This analysis is useful for the health sectors and frontline workers to help reduce the contamination caused by this disease.
Drug identification is an issue with real-time population growth.To improve drug detection in real-time biotechnical applications.In many nations,agriculture is the single most important factor in increasing the supply of anti-cancer drugs.Cancer-related illnesses are effective in lowering production quality and volume while also costing computer vision applications money.While many writers have utilized machine learning and other forms of soft computing to develop methods for automatically predicting disease, the complexity of identifying cancer has made it a particularly vigorous field of study.This article proposes a Novel Heuristic Hybrid Model (NHHM) that combines SOM and CNN to automatically identify cancer diseases.Feature extraction is key to identifying cancer diseases,therefore dimensionality reduction is done in SOM to filter noise from Cancersubcancer medication data sets and distinguish disease-affected patches.CNN is used to explore color histogram-based linguistic features existing in Cancer and predict which patch is related to disease in Cancers.It is an effective option to predict accurate detection of diseases where Cancersubcancer drugdata set is matched with virus/bacteria-affected patch in Cancers.The proposed hybrid model was tested using publicly available Cancers cancer drug data sets (which are available in the UCI repository) to obtain subCancerdrug data sets from peach Cancers.This approach is used to identify the disease present in Cancers based on the subCancer drug data sets.The proposed hybrid model also requires less training to investigate disease from subCancer drug data sets, and it performs with an accuracy of almost 99.93% on various testing Cancersubcancer drug data sets. When compared to several state-of-the-art methodologies, this reduces the major running time and performs effectively in various disease detection characteristics like precision, recall, sensitivity, and specificity.
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