Computer Network and Database Security Technology Optimization

With the continuous development of computer network technology, its applications in daily life and work have become increasingly widespread, greatly improving efficiency. However, certain security risks remain. To ensure the security of computer networks and databases, it is essential to enhance the security of both through optimization of technology. This includes improving management practices, optimizing data processing methods, and establishing comprehensive laws and regulations. This paper analyzes the current security risks in computer networks and databases and proposes corresponding solutions, offering reference points for relevant personnel.

Computational and bioinformatics tools for understanding disease mechanisms

Computational methods have significantly transformed biomedical research,offering a comprehensive exploration of disease mechanisms and molecular protein functions.This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states.The utilization of protein/gene interaction and genetic variation databases,coupled with pathway analysis can facilitate the identification of potential drug targets.By bridging the gap between molecular-level information and disease understanding,this review contributes insights into the impactful utilization of computational methods,paving the way for targeted interventions and therapeutic advancements in biomedical research.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

Network pharmacology and molecular docking to explore Polygoni Cuspidati Rhizoma et Radix treatment for acute lung injury

BACKGROUND Polygoni Cuspidati Rhizoma et Radix(PCRR),a well-known traditional Chinese medicine(TCM),inhibits inflammation associated with various human diseases.However,the anti-inflammatory effects of PCRR in acute lung injury(ALI)and the underlying mechanisms of action remain unclear.AIM To determine the ingredients related to PCRR for treatment of ALI using multiple databases to obtain potential targets for fishing.METHODS Recognized and candidate active compounds for PCRR were obtained from Traditional Chinese Medicine Systems Pharmacology,STITCH,and PubMed databases.Target ALI databases were built using the Therapeutic Target,DrugBank,DisGeNET,Online Mendelian Inheritance in Man,and Genetic Association databases.Network pharmacology includes network construction,target prediction,topological feature analysis,and enrichment analysis.Bioinformatics resources from the Database for Annotation,Visualization and Integrated Discovery were utilized for gene ontology biological process and Kyoto Encyclopedia of Genes and Genomes network pathway enrichment analysis,and molecular docking techniques were adopted to verify the combination of major active ingredients and core targets.RESULTS Thirteen bioactive compounds corresponding to the 433 PCRR targets were identified.In addition,128 genes were closely associated with ALI,60 of which overlapped with PCRR targets and were considered therapeutically relevant.Functional enrichment analysis suggested that PCRR exerted its pharmacological effects in ALI by modulating multiple pathways,including the cell cycle,cell apoptosis,drug metabolism,inflammation,and immune modulation.Molecular docking results revealed a strong associative relationship between the active ingredient and core target.CONCLUSION PCRR alleviates ALI symptoms via molecular mechanisms predicted by network pharmacology.This study proposes a strategy to elucidate the mechanisms of TCM at the network pharmacology level.

基于Kohonen神经网络的地图自动输入技术

本文介绍一种利用神经网络对彩色地图进行识别 ,从而自动建立地图数据库的方法 .首先采集样本对 Koho-nen神经网络进行训练 ,然后通过神经网络对地图中的道路进行识别和抽取 ,再利用去噪算法进行去噪 ,最后进行城市识别和矢量化 ,得到地图数据库 .

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Inferior outcomes of liver transplantation for hepatocellular carcinoma during early-COVID-19 pandemic in the United States

BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the impact and outcomes of early COVID-19 pandemic on liver transplantation(LT)for hepatocellular carcinoma(HCC)in the United States.METHODS WHO declared COVID-19 as a pandemic on March 11,2020.We retrospectively analyzed data from the United Network for Organ Sharing(UNOS)database regarding adult LT with confirmed HCC on explant in 2019 and 2020.We defined pre-COVID period from March 11 to September 11,2019,and early-COVID period as from March 11 to September 11,2020.RESULTS Overall,23.5%fewer LT for HCC were performed during the COVID period(518 vs 675,P<0.05).This decrease was most pronounced in the months of March-April 2020 with a rebound in numbers seen from May-July 2020.Among LT recipients for HCC,concurrent diagnosis of non-alcoholic steatohepatitis significantly increased(23 vs 16%)and alcoholic liver disease(ALD)significantly decreased(18 vs 22%)during the COVID period.Recipient age,gender,BMI,and MELD score were statistically similar between two groups,while waiting list time decreased during the COVID period(279 days vs 300 days,P=0.041).Among pathological characteristics of HCC,vascular invasion was more prominent during COVID period(P<0.01),while other features were the same.While the donor age and other characteristics remained same,the distance between donor and recipient hospitals was significantly increased(P<0.01)and donor risk index was significantly higher(1.68 vs 1.59,P<0.01)during COVID period.Among outcomes,90-day overall and graft survival were the same,but 180-day overall and graft were significantly inferior during COVID period(94.7 vs 97.0%,P=0.048).On multivariable Coxhazard regression analysis,COVID period emerged as a significant risk factor of post-transplant mortality(Hazard ratio 1.85;95%CI:1.28-2.68,P=0.001).CONCLUSION During COVID period,there was a significant decrease in LTs performed for HCC.While early postoperative outcomes of LT for HCC were same,the overall and graft survival of LTs for HCC after 180 days were significantly inferior.

Baseline Isolated Printed Text Image Database for Pashto Script Recognition

The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.

Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

FC-InfoNet-Warehouse:信息网络数据仓库建模及实现

信息网络是一种对复杂网络进行高度一致性抽象、用于处理图数据的新型概念。为了更加灵活高效地读取数据,提升研究效率,提出一种以图为主题结合信息网络与数据仓库优势的FC信息网络数据仓库模型。实验表明该模型在查询效率,存储空间及查询灵活性等方面较前人所提EN信息网络数据仓库模型均具有明显优势。

Network Analysis Modeling Towards GIS Based on Object-Relation Database

This paper compares the differences between the mathematical model in graph theory and GIS network analysis model. Thus it claims that the GIS network analysis model needs to solve. Then this paper introduces the spatial data management methods in object\|relation database for GIS and discusses its effects on the network analysis model. Finally it puts forward the GIS network analysis model based on the object\|relation database. The structure of the model is introduced in detail and research is done to the internal and external memory data structure of the model. The results show that it performs well in practice.

Using Database Management System to Generate, Manage and Secure Personal Identification Numbers (PIN)

A number of problems are associated with the generation, management and security of PINs, a subset of password. The PINs may be recharge card used by GSM operators or for authentication in ATM. The problems associated with the use of these PINs range from scratching off any of the recharge PIN numbers in recharge card to loss of PIN number or entering invalid number in the case of authentication. It usually takes time for the customer service of the service provider or system administrator to provide convincing solution to these problems promptly when it occurred. PINs generation could seem like simply arranging ranges of number and feeding it into the telecommunication systems such as mobile handsets or ATM to grant access but it requires a specialized and secured way to generate, store and manage it in order to achieve prompt access. This paper focused on the development of database concept to provide solution to these problems by desiging a system by which the PINs generated can be effectively stored and managed so that userss can have immediate access to the PINs if they can provide the identification number on the card. Succintly, the paper discusses the design of a system that generates, manages and secures PINs application using Visual Basic Version 6.0 for designing the front and interface and Microsoft Access 2007 as the database. The system was implemented using real data and the result was successful.

A New Approach for Database Fragmentation and Allocation to Improve the Distributed Database Management System Performance

The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.

Design and production of the network courses based on the database technology

Online teaching is an effective means and the inevitable trend of the development of modern education, its shared educational resources, to expand the scale of education to improve educational efficiency; build a lifelong education system plays an important role. In this paper, by analyzing the common feature of database system and network courses, it proposes a network-based database technology curriculum design and fabrication methods and principles, to make the curriculum from web centric processing teaching content production to around a shared database of teaching resource center changes.

Storage and Parallel Loading System Based on Mode Network for Multimode Medical Image Data

Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique.

Routing Protocols for Transmitting Large Databases or Multi databases Systems

Most knowledgeable people agree that networking and routing technologies have been around about 25 years. Routing is simultaneously the most complicated function of a network and the most important. It is of the same kind that more than 70% of computer application fields are MIS applications. So the challenge in building and using a MIS in the network is developing the means to find, access, and communicate large databases or multi databases systems. Because general databases are not time continuous, in fact, they can not be streaming, so we can't obtain reliable and secure quality of service by deleting some unimportant datagrams in the databases transmission. In this article, we will discuss which kind of routing protocol is the best type for large databases or multi databases systems transmission in the networks.

Networked control and supervision system based on LonWorks fieldbus and Intranet/Internet

A networked control and supervision system （NCSS） based on LonWorks fieldbus and lntranet/Intemet was designed, which was composed of the universal intelligent control nodes （ICNs）, the visual control and supervision configuration platforms （VCCP and VSCP） and an Intranet/Internet-based remote supervision platform （RSP）. The ICNs were connected to field devices, such as sensors, actuators and controllers. The VCCP and VSCP were implemented by means of a graphical programming environment and network management so as to simplify the tasks of programming and maintaining the ICNs. The RSP was employed to perform the remote supervision function, which was based on a three-layer browser/server（B/S） structure mode. The validity of the NCSS was demonstrated by laboratory experiments.

Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks

Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.