During the ongoing worldwide crisis, researchers, clinicians, and medical care specialists around the world continue looking for another innovation to help in handling the COVID-19 pandemic. The proof of Machine Learning (ML) and Artificial Intelligence (AI) application on the past pestilence empower scientists by giving another point to battle against the novel Coronavirus episode. This paper intends to thoroughly audit the part of AI and ML as one critical technique in anticipating SARS-CoV-2 and its related epidemic. Coronavirus is an irresistible illness, and it does serious harm to the lungs. Coronavirus causes disease in people and has executed numerous individuals in the whole world. Nonetheless, this infection is accounted for as a pandemic by the World Health Organization. (WHO) and all nations are attempting to control and lockdown all spots. This work's main principle goal is to predicting the spread of COVID-19 across Egypt and analyzing the development rates. For this aim, we access real datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). And European Union open dataset. We have implemented the results by using R Language.
Queso Balanco cheese made from cows or buffaloes milk standardized to 3% fat. As acidulants three organic acids namely , lactic , Citric and acetic acids were applied while , to the fourth treatment, pure yoghurt culture was added to the milk at 42 C o for acid development to reach 0.35 % acidity. Samples of fresh , 7, 14 and 21 days were chemically, microbilogically and organoleptically analysed. Results obtained showed that yield of buffaloes milk cheese was higher than those of cows milk cheese. The addition of yoghurt culture (fourth treatment) gave the highest yield. On the other hand cows milk cheese contained higher moisture as compared with buffaloes milk cheese. Cultured yoghurt cheese contained the highest moisture, followed by acetic , citric and lactic acid cheese , for both cows and buffaloes milk cheese. Fat content of cows milk was higher than buffaloes milk cheese owing to the high losses of buffaloes milk fat during cheese making on the other hand lactic acid cheese contained the highest fat and protein contents .While yoghurt culture cheese gave the lowest value. Buffaloes milk cheese contained higher protein content than cows milk cheese .Cows milk cheese acidity was higher than those of buffaloes milk cheese as well citric acid cheese resulted in the highest acidity for both milk while yoghurt culture cheese gave the lowest value. Buffaloes milk cheese contained higher total microbial count (T.M.C.) than those of cows milk cheese. Cultured milk cheese contained the highest T.M.C. while , the lowest was for citric acid cheese. No moulds and yeasts were detected in fresh cheese samples while they gradually increased during the storage period. Cows milk cheese gained higher organoleptic score than those of buffaloes milk. Starter culture cheese was the best one followed by lactic, citric acid while acetic acid cheese obtained the lowest scoring points and refused by most panalists who complained the taste of rotten food (taste of cvinegar).
In the recent years, Big Data became a prominent tendency in many different fields. It refers to any data that are intricate and gigantic in volume, with tremendous velocity and vast variety, which transcend the processing ability of conventional tools. Heterogeneity is one of the eminent problems facing Big Data. Most of the data that are gathered from diverse resources are semi-structured and unstructured, which makes information extraction a difficult task. In this paper, a framework and algorithms are proposed to provide a solution that can help in handling the heterogeneity problem of big data through increasing the homogeneity and decreasing the heterogeneity of data to a certain degree or level for text formats. The proposed solution is implemented using R language, R AnalyticFlow software environment and Apache Spark which is one of the big data processing tools.
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