syedzayyan/paper_mill_benchmark huggingface dataset:
Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

With the increase in the number of online shopping users, customer loyalty is directly related to product sales.This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis.This research mainly uses machine learning clustering algorithm to simulate customer loyalty.Call the k-means interactive mining algorithm based on the hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained.After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained.The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process.The correctly parsed data will be stored for the next step of data cleaning or data analysis.For writing a java program to parse html documents, first set the subject keyword and url and parse the html from the obtained file or string by analyzing the structure of the website.Secondly, use the css selector to find the web page list information, retrieve the data, and store it in elements.In the overall fit test of the model, the root mean square error approximation (rmsea) value is 0.053, between 0.05 and 0.08.The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Risk Factor Analysis of Hepatic Encephalopathy and the Establishment of Diagnostic Model

To identify laboratory diagnostic indicators of hepatic encephalopathy (he), the present study established a he diagnostic model to explore the diagnostic value of serum homocysteine, lactic acid, procalcitonin, and bile acid levels in he identification.371 patients with liver cirrhosis were selected as research objects, who were admitted to the department of hepatic diseases, affiliated hospital of northwest minzu university from august 2019 to august 2020.The spearman correlation results indicated that between lactic acid, procalcitonin, bile acid, serum homocysteine, and he, the coefficients were ‐0.15, 0.41, 0.29, and ‐0.19, respectively.Univariate and multivariate analysis methods were adopted for inpatient analysis to identify the influencing factors of he occurrence, and the diagnosis of the he identification model was subsequently constructed.The univariate logistic regression showed that risk of developing he increased as bile acid level ( p = 0.00434) and serum homocysteine ( p = 0.058) increased.Multivariate logistic regression diagnostic model of bile acid level and serum homocysteine revealed that the auc value of the area under the roc curve was 0.7201, indicating that the diagnostic model produced a satisfactory evaluation effect.The model formula referred logistic ( p ) = −2.4544 + 0.0117 bile acid levels + 0.0198 serum homocysteine.In this study, the he diagnostic model was established using logistic regression analysis, which could benefit patients in early he differential diagnosis.Particularly, combined detection of serum homocysteine and bile acid levels was considered to be more significant.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network

Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly.The examination of cup‐to‐disc ratio is considered essential in diagnosing glaucoma.It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio.In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (m rnn).The m rnn use the combination of recurrent neural network (rnn) with fully convolutional network (fcn) that exploits the intra‐ and interslice contexts.The fcn extracts the contents from an input image by constructing a feature map for the intra‐ and interslice contexts.This is carried out to extract the relevant information, where rnn concentrates more on interslice context.The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc.The results of simulation show that the proposed method m rnn is efficient in improving the rate of segmentation than the other deep learning models like drive, stare, messidor, origa, and diaretdb.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging

The pursuit of health and longevity is the eternal theme of humanity.Guangxi has a remarkable phenomenon of longevity in long‐lived regions and ranks with the highest number of longevity villages in china, thus providing a natural advantage for health longevity research.Methods .In this study, we selected 117 natives of a longevity area in guangxi, covering a large age range (38‐118 years old) as subjects to measure peripheral leukocyte telomere length (ltl).Nineteen physical examination indicators and two inflammatory factor levels were measured.Results .Pearson’s analysis revealed a significant negative correlation between age and ltl ( r = −0.3694, p = 0.003), as well as alanine aminotransferase, albumin, total bilirubin, direct bilirubin, γ ‐glutamyltransferase, triglycerides, interleukin‐10, and tumor necrosis factor type‐ α .Systolic blood pressure and blood urea nitrogen were positively correlated with age.In addition, ltl decreased in people aged 38–89 years, and an upward trend was observed in people aged older than 90 years.Conclusions .Longevity individuals have characteristics, such as longer ltl, good hepatic function, and lower triglycerides and inflammation levels.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Resetting Proteostasis of CIRBP with ISRIB Suppresses Neural Stem Cell Apoptosis under Hypoxic Exposure

Neurological disorders are often progressive and lead to disabilities with limited available therapies.Epidemiological evidence implicated that prolonged exposure to hypoxia leads to neurological damage and a plethora of complications.Neural stem cells (nscs) are a promising tool for neurological damage therapy in terms of their unique properties.However, the literature on the outcome of nscs exposed to severe hypoxia is scarce.In this study, we identified a responsive gene that reacts to multiple cellular stresses, marked cold-inducible rna-binding protein (cirbp), which could attenuate nsc apoptosis under hypoxic pressure.Interestingly, isrib, a small-molecule modulator of the perk-atf4 signaling pathway, could prevent the reduction and apoptosis of nscs in two steps: enhancing the expression of cirbp through the protein kinase r- (pkr-) like endoplasmic reticulum kinase (perk) and activating transcription factor 4 (atf4) axis.Taken together, cirbp was found to be a critical factor that could protect nscs against apoptosis induced by hypoxia, and isrib could be acted upstream of the axis and may be recruited as an open potential therapeutic strategy to prevent or treat hypoxia-induced brain hazards.

Journal: Oxidative Medicine and Cellular Longevity (2022)
Publication types: Research Article;
Security Regulation and Enterprise Innovation in Communication Industry

This paper conducted an evolutionary game model of the interaction between the governments and communication enterprises and analyzed the impact of the government’s communication security regulation on the innovation decision-making of communication enterprises.The results show that the behavior of the governments depends on the social benefits, rent-seeking benefits, and regulating costs in strict and de security regulations.The communication enterprises’ behavior depends on the benefits of innovation and the costs in r&amp;d and rent-seeking.When government subsidies are relatively inadequate, the communication enterprises’ strategy under government security regulation swings from not-innovation finally to innovation.The policy implications of this study indicate that appropriate de security regulation by the government will help communication enterprises generate a good atmosphere for innovation, and the appropriate increase in subsidies will be more conducive to driving enterprise innovation.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Idelalisib promotes Bim-dependent apoptosis through AKT/FoxO3a in hepatocellular carcinoma

Idelalisib, a selective pi3kδ inhibitor, has been approved by the fda for chronic lymphocytic leukemia/small lymphocytic lymphoma treatment and for follicular lymphoma treatment when combined with rituximab.However, the mechanisms of effective action of idelalisib in hepatocellular carcinoma (hcc) remain unclear.In the current study, we aimed to investigate how idelalisib inhibits the growth of hcc cells and enhances the effects of other chemotherapeutic drugs.Our results show that idelalisib treatment promotes bim induction in hcc via the fox o3a pathway following pi3k/akt inactivation.Moreover, our results show that bim is required for idelalisib-mediated apoptosis in hcc.Idelalisib also synergizes with sorafenib or doxorubicin to induce significant apoptosis in hcc, and bim is also necessary for the induction of apoptosis by cotreatment.Furthermore, a xenograft experiment reveals that the bim deficiency abolishes apoptosis and antitumor effects of idelalisib in vivo.In summary, our results indicate a key role of bim in mediating the antitumor effects of idelalisib in hcc.Our results also support the clinical significance of the drug.

Journal: Cell Death & Disease (2018)
Publication types: Research Article;
Topological Optimum Approach for Improving Indicators for Agricultural Sustainability

The goal of this study is to improve the topological efficiency of agricultural production in saudi arabia’s allocated agriculture area.Method.The data envelopment analysis (dea) topology was adopted to efficiently identify agricultural land utilization from 2009 to 2013.Results.According to the constant return to scale (crs) topology, the area may be reduced by 8.7831667 percent in 2009, resulting in a given optimal crop yield scenario.Similarly, the area may be reduced by 3.9425506 percent in 2010 to attain a given optimum crop production topology, by 4.0198591 percent in 2011 to achieve the same amount of crop production, and by 1.0621519 percent in 2012 to attain the desired level of crop output.The topology of crop production could increase by 5.1334182 percent while retaining quality, according to the variable return to scale (vrs) topology while maintaining the same assigned area.Conclusion.Also using the linear programming approach to reproduce the results of crs and vrs models.

Journal: Journal of Chemistry (2022)
Publication types: Research Article;
Research on the Performance of Impressionist Painting Color Visual Communication Based on Wireless Communication and Machine Vision

In order to solve the problem of poor visual communication effect of impressionist painting colors and low accuracy of painting color enhancement by existing methods, this thesis proposes a research on the performance of impressionist painting color visual communication based on machine vision under the background of wireless network.This method can improve the vision, speed, and efficiency of communication, through the analysis of the characteristics of impressionist paintings, and determine the visual communication objects of impressionist painting colors.The process of visual communication is analyzed, and the color matching of impressionist painting is completed with the help of bp neural network algorithm.On this basis, the histogram method is used to process the image brightness of impressionist paintings, the image interpolation method is used to process the image brightness of impressionist paintings, and the image is corrected by gamma correction to complete the image performance research.The color vision communication of impressionist painting needs to correct the gray scale error of impressionist painting and enhance the color of impressionist painting.The experimental results show that the accuracy of this method for the color matching of impressionist painting images is about 98%, which has a certain degree of credibility.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

The mental health of young college students has always been a social concern.Strengthening the supervision of college students’ mental health problems is an important research content.In this regard, this paper proposes to apply fuzzy cluster analysis to the health analysis of college students and explore college students through fuzzy clustering.Explore the potential relationship between the factors that affect the health of college students, and this will provide a reference for the early prevention and intervention of college students’ mental health problems.In view of this, an improved fuzzy clustering method based on the firefly algorithm is proposed.First, the chebyshev diagram is introduced into the firefly algorithm to initialize the population distribution.Then, an adaptive step size method is proposed to balance exploration and development capabilities.Finally, in the local search process, a gaussian perturbation strategy is added to the optimal individual in each iteration to make it jump out of the local optimal.The process has good optimization capabilities and is easy to obtain the global optimal value.It can be used as the initial center of the fuzzy c-means clustering algorithm for clustering, which can effectively enhance the robustness of the algorithm and improve the global optimization ability.In order to evaluate the effectiveness of the algorithm, comparative experiments were carried out on four datasets, and the experimental results show that the algorithm is better than the comparison algorithm in clustering accuracy and robustness.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Ing at the problem of low segmentation accuracy of greening landscape of urban community, a segmentation method of greening landscape of urban community based on improved u-net network is proposed by adding an encoder on u-net network.In addition, simulation is carried out on the remote sensing image data collected by gprs network, and the effectiveness of the method is verified.The simulation results show that the result of proposed method is close to that of manual annotation.Compared with the traditional segmentation algorithm svm and seg net method, as well as the u-net network before improvement, the proposed method has higher segmentation accuracy.The segmentation accuracy can reach up to 91%, the intersection ratio can reach 76%, and the mean pixel accuracy and mean intersection ratio can reach 89% and 74%, which indicates that the proposed method has certain validity and practicability.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches.Examine ecg signal generation and properties, as well as the notion and causes of heart rate variability.The ecg signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ecg signal.The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ecg signal and then selects the ecg signal characteristics.D-s evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ecg signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology.The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

The basic task of the logistics distribution center is to achieve the storage and distribution of materials, and to plan, implement, and manage the effective flow of materials from the supplying place to the consumption place.Scientific location of logistics distribution center can effectively reduce logistics cost, improve the speed of circulation, increase the profits of enterprises, and enhance the core competitiveness of enterprises.Combining the advantages of k-means clustering algorithm, this paper applies it to the location problem of logistics distribution center and proposes a logistics distribution center location method combining k-means clustering theory and d-s reasoning, which provides a better solution for the location problem of logistics distribution center.Through case analysis, k-means clustering algorithm can obtain reasonable location of logistics distribution center, which can be applied to the location of multilevel logistics distribution network, and has certain practical application value.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Research on Local Topology Tracking of Power Grid Based on Graph Theory

Power network topology identification, judgment, and tracking are the basic functional components of power system guarantee system and security management system.They can provide basic network structure data for other application software programs of power system.However, the traditional power grid topology method is not easy to implement and provides less relevant data that can be accurately analyzed, so that relevant personnel cannot fully understand the state of the power grid and give accurate commands, resulting in serious power accidents.Therefore, this paper proposes the research of power grid local topology tracking based on graph theory and constructs the power grid local topology tracking algorithm based on graph theory.The experimental results show that the local topology tracking algorithm based on graph theory can track the local topology of power grid quickly and effectively.Compared with the traditional method based on priority search, although the first power grid topology takes a relatively long time, it greatly improves the search and processing time after each time and has high efficiency in local topology.This shows that the local topology tracking algorithm based on graph theory needs less computation when carrying out the local topology of power grid.At the same time, the theory of power grid local topology tracking algorithm based on graph theory is relatively simple and easy to time, which is more practical than the traditional method.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Research on Evaluation Model of Hospital Informatization Level Based on Decision Tree Algorithm

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed.Using the decision tree algorithm combining fuzzy theory and id3, the decision tree is constructed to analyze the hospital information data.By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level.Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level.The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Research on Energy-Saving Optimization of Rural Houses in Specific Regions Based on Internet of Things

With the expansion of social energy use, the optimization of residential energy conservation has become urgent and important.The research on rural housing in areas with hot summers and cold winters started late and is relatively backward in terms of housing energy saving, which greatly hinders the sustainable development of rural areas.The application of the internet of things technology helps to provide a stable technical guarantee for energy-saving optimization.Therefore, this paper takes the energy-saving optimization design of rural houses in hot summer and cold winter areas based on the internet of things technology as the research theme.This article first takes h rural area as the research object and analyzes the climatic conditions of the place, that is, the typical characteristics of hot summer and cold winter.Then, the intelligent temperature acquisition system is designed, and the working process and main hardware modules of the system are introduced.This text combines gprs and internet of things technology, designs the temperature control system, and carries on the test to the system operation effect.The test results show that during the heating period in january, before and after the temperature control system controls the room temperature, the maximum relative error between the set temperature value and the actual temperature value is 0.49 and 0.27, respectively.It can be seen that the intelligent temperature energy-saving control system can collect the indoor temperature in real time and can well control the heating equipment.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;