COVID-19 emerged in 2019 in china, the worldwide spread rapidly, and caused many injuries and deaths among humans. Accurate and early detection of COVID-19 can ensure the long-term survival of patients and help prohibit the spread of the epidemic. COVID-19 case classification techniques help health organizations quickly identify and treat severe cases. Algorithms of classification are one the essential matters for forecasting and making decisions to assist the diagnosis, early identification of COVID-19, and specify cases that require to intensive care unit to deliver the treatment at appropriate timing. This paper is intended to compare algorithms of classification of machine learning to diagnose COVID-19 cases and measure their performance with many metrics, and measure mislabeling (false-positive and false-negative) to specify the best algorithms for speed and accuracy diagnosis. In this paper, we focus onto classify the cases of COVID-19 using the algorithms of machine learning. we load the dataset and perform dataset preparation, pre-processing, analysis of data, selection of features, split of data, and use of classification algorithm. In the first using four classification algorithms, (Stochastic Gradient Descent, Logistic Regression, Random Forest, Naive Bayes), the outcome of algorithms accuracy respectively was 99.61%, 94.82% ,98.37%,96.57%, and the result of execution time for algorithms respectively were 0.01s, 0.7s, 0.20s, 0.04. The Stochastic Gradient Descent of mislabeling was better. Second, using four classification algorithms, (eXtreme-Gradient Boosting, Decision Tree, Support Vector Machines, K_Nearest Neighbors), the outcome of algorithms accuracy was 98.37%, 99%, 97%, 88.4%, and the result of execution time for algorithms respectively were 0.18s, 0.02s, 0.3s, 0.01s. The Decision Tree of mislabeling was better. Using machine learning helps improve allocate medical resources to maximize their utilization. Classification algorithm of clinical data for confirmed COVID-19 cases can help predict a patient’s need to advance to the ICU or not need by using a global dataset of COVID-19 cases due to its accuracy and quality.

COVID-19 has appeared in china, spread rapidly the world wide and caused with many injuries, deaths between humans. It is possible to avoid the spread of the disease or reduce its spread with the machine learning and the diagnostic techniques, where the use classification algorithms are one of the fundamental issues for prediction and decision-making to help of the early detection, diagnose COVID-19 cases and identify dangerous cases that need admit Intensive Care Unit to provide treatment in a timely manner. In this paper, we use the machine learning algorithms to classify the COVID-19 cases, the dataset got from dataset search on google and used four algorithms, as (Logistic Regression, Naive Bayes, Random Forest, Stochastic Gradient Descent), the result of algorithms accuracy was 94.82%, 96.57%, 98.37%, 99.61% respectively and the execution time of each algorithm were 0.7s, 0.04s, 0.20s,0.02s respectively, and with the mislabeling Stochastic Gradient Descent algorithm was better.

The database replication refers to distributing a database among multiple locations. There are three kinds of database replication system: snapshot, transactional and merge. The snapshot replication refers to the fragment of database items and distributing them to multi databases at once. An important goal in this paper is to experiment with a distributed database study the snapshot replication and examine the issues associated with it. In this work, the data from another database is used to increase availability and flexibility as well as provide the information exchange between databases. In this process, the data is infrequently updated at specified periods by copying and changing the data from the original database towards the subscriber database. The work of agents in this technology will do the most of the work to achieve the stated goal. The experimental results show that at both vertical and horizontal fragmentation, the proposed approach of replicating distributed database is efficient and the performance is significantly improved in terms of data transfer time, load sharing and update of database fragmentation. Hence the snapshot replication system is much schedulable and protective replication in business markets.

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.

The database replication refers to distributing a database among multiple locations. There are three kinds of database replication system: snapshot, transactional and merge. The snapshot replication refers to the fragment of database items and distributing them to multi databases at once. An important goal in this paper is to experiment with a distributed database study the snapshot replication and examine the issues associated with it. In this work, the data from another database is used to increase availability and flexibility as well as provide the information exchange between databases. In this process, the data is infrequently updated at specified periods by copying and changing the data from the original database towards the subscriber database. The work of agents in this technology will do the most of the work to achieve the stated goal. The experimental results show that at both vertical and horizontal fragmentation, the proposed approach of replicating distributed database is efficient and the performance is significantly improved in terms of data transfer time, load sharing and update of database fragmentation. Hence the snapshot replication system is much schedulable and protective replication in business markets.   http://dx.doi.org/10.25130/tjps.23.2018.177