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With the increase of enterprise informatisation, database security and compliance operation management have become increasingly important. Therefore, it is essential to design an efficient database proxy approval system. In this paper, we develop a database proxy approval system based on the MySQL protocol for fortress machines, which provides a real-time customised configuration scheme for high-risk commands, designs a real-time approval process for six types of high-risk commands, and creates a simple and efficient matching algorithm for high-risk commands. We designed a large number of experiments to test the system's connection success rate, operation stability, response time, CPU resource consumption, matching algorithm performance, and other aspects. The experimental results show that this database proxy approval system has good configuration flexibility, high accuracy, and good time performance. This system has a wide range of applications in electric power, finance, petroleum, and other fields.

Exploratory research requires models that can explain the underlying phenomena of interest in new research areas. We present the design and application of discrete Bayesian networks (DBN) for knowledge discovery in a hospital discharge dataset. For the learning phase of the network, the automated learning methods are preceded by customising the initial network. Structural learning is done using three state-of-the-art algorithms and is inter-validated. A new method is suggested for drawing selective inferences from the posterior conditional probability tables (CPT). As an illustration, functional inferences are drawn on length of stay and treatment charges for three disease groups using the developed method. Our analysis shows that for longer hospital stays, hospital visits involving mental disorders cost less than visits with other types of health conditions. This study contributes to data science research by demonstrating the application of Bayesian networks, evaluating different structure learning methods for given contexts, and developing a measure for selective inference using the CPT of the network.

This paper proposes a new Del distribution and discusses its applications in the model selection. The authors attempted to propose the exact distribution Del, which is defined as the difference between two generalized information criteria (GIC) of the ith and jth fitted models, and identified the relationship of Del with Correlated F-Ratio. Moreover, the density function and moments of the Del distribution were derived, and the critical points were also computed for different sample sizes at 5% and 1% significance levels. Finally, the application of the proposed distribution was used to scrutinise the equality between the pair of fitted candidate models. If the Del is said to be statistically significant, then the information loss between the models is not equal, and the predictive power of the models is not the same. This was numerically illustrated by fitting four different types of stepwise regression models based on 30 independent variables belonging to macroeconomic factors.

Exploratory research requires models that can explain the underlying phenomena of interest in new research areas. We present the design and application of discrete Bayesian networks (DBN) for knowledge discovery in a hospital discharge dataset. For the learning phase of the network, the automated learning methods are preceded by customising the initial network. Structural learning is done using three state-of-the-art algorithms and is inter-validated. A new method is suggested for drawing selective inferences from the posterior conditional probability tables (CPT). As an illustration, functional inferences are drawn on length of stay and treatment charges for three disease groups using the developed method. Our analysis shows that for longer hospital stays, hospital visits involving mental disorders cost less than visits with other types of health conditions. This study contributes to data science research by demonstrating the application of Bayesian networks, evaluating different structure learning methods for given contexts, and developing a measure for selective inference using the CPT of the network.

At present, the Internet of Things (IoT) has improved people's lives. IoT provides users with various intelligent sorting, networked devices, and applications across different fields. Therefore, detecting anomalies in IoT devices with intelligent sorting is crucial to minimise threats and improve safety. The convolutional neural network-assisted anomaly detection (CNN-AD) method has been developed to enhance security by detecting anomalies in the IoT environment with intelligent sorting. The Anomaly detection method uses a focused event system to increase its efficiency in intelligent sorting with event grouping tasks and improve detection accuracy. The event privacy is obtained by utilising the feature selection, mapping, and normalisation to enhance security. CNN automatically extracts characteristics from data and identifies and classifies the different types of events and attacks in intelligent sorting. The performance analysis and assessments of CNN are based on detecting different classes of attacks and computation times that are significantly shorter.

As an important research direction in artificial intelligence (AI), machine learning (ML) has been widely used in many complex systems. This paper aimed to study how to improve and train graph based semi-supervised learning algorithm (GBSSLA) based on ML. This paper chooses decision trees (DT) and backpropagation neural networks (BPNN) as classifiers to train ML models. Experimental analysis shows that when the labelled data accounts for 20%, 50%, and 80% of the training set, the average error improvement rate of the improved graph based semi supervised learning algorithm (IGBSSLA) is always higher than that of the self training algorithm (STA) and cooperative training algorithm (CTA). From the experimental results, it could be seen that under the same experimental conditions, the same experimental data and the same classifier method, the final error of IGBSSLA and the percentage of error increase were better than STA and CTA.

As the range of customers in cellular era will increase at a geometrical rate, the call for and complexity of the provider is rising, making sure within side they want for cellular operators to shift the provider using difficulty to their core technology with the intention to switch extra information with excessive speed. The 5G cell networks is that the consequences of this paradigm shift and is currently being deployed in advanced nations regardless of the reality that they may be capable of put in force 6G cell networks. However, maximum of the growing nations in addition to Bangladesh ( Middle-earnings country) are, or will be, the usage of 3G or 4G cellular networks very lately for which the general position is not but complete. In this study, we are now exploring how a lot emphasis can be positioned on setting up the 5G cell networks in Bangladesh in addition to health effect of 4G and upcoming 5G cellular networks. We first take note of display that there may be capability for tackling foremost technological demanding situations in Bangladesh while putting in place the 5G cellular networks. The numerous protection factors of 5G cellular networks were provided that is an ongoing hassle and Bangladesh goes to technologically ready to deal with many protection issues of it. At the same time, it has been proven that the context of the usage of 5G networks in Bangladesh is relevant at present. As a result, putting in place the 5G networks in Bangladesh will take now not than expected. Finally, the outcomes of the usage of 4G, 5G cellular networks on human health had been presented.

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The outburst of COVID-19 pandemic had tremendous effect on the whole world and analysis of the data can be meaningful in many ways to better understand the effect it had in our society. This paper aims in the direction of analysis of COVID-19 daily information based on country and continent level in terms of understanding the number of cases and deaths and their relationship, besides this is aims to better understand the vaccination number by country and effect of cases/death how they have affected these numbers. The solution was created on analysis of a dataset that contains daily information on each country, and using MySQL, SQL and PowerBi to generate the results for this work in way of query results which have been transformed to visuals using PowerBi for better understanding for further research work on this topic.