Deep Convolutional Neural Network Approach for COVID-19 Detection

Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses.As by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons.The accurate detection of Covid-19 cases poses some questions to scientists and physicians.The two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected previously.Rou-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is used.Medical image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the virus.This imaging approach can detect certain characteristic changes in the lung associated with Covid-19.In this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images.The entire model proposed is categorized into three stages:dataset,data pre-processing andfinal stage being training and classification.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.

The Technical Design and Implementation of Cross-Platform Industrial Product Order System

According to some data in the Industrial Purchasing Trends report released by China in 2017,we can see that e-commerce purchasing channels have ranked first among all industrial products purchasing channels in China compared with European and American countries.In addition,in the whole industrial product purchasing market,we can also see that both manufacturers and suppliers are making active e-commerce transformation,and some other Internet giants are also actively entering the industrial product e-commerce industry.But at present,the revenue of all kinds of subjects is still a lot of room for improvement compared to the United States industrial giants.Although the domestic e-commerce market of industrial products has a variety of problems,also contains huge opportunities and development space.Today mobile Internet technology is becoming more and more popular.It is particularly important to develop a cross-platform industrial product order system that supports the collaborative work and unified experience of Android,iOS,and Web.This system uses a uni-app framework to develop front-end applications,which can realize an order management system with code running across multiple platforms.The back end is built based on LNMP architecture.Linux is the most popular free operating system.Nginx is a free and efficient web server with good stable performance,rich functions,simple operation and maintenance,fast processing of static files,and minimal system resource consumption.MySQL database is one of the most widely used relational databases in Web application data processing.The server side is written by PHP script under ThinkPHP framework,which is quick,open-source,and cross-platform in system construction.And these four kinds of software are free,open-source software,together,they can become a free,efficient,highly extensible website service system.