Search

Digital publishing is the process of informatizing the content of traditional publishing. It not only involves the processing of information, but also includes the whole process of digital publishing enterprise management and operation. Compared with traditional publishing, digital publishing has a wider distribution channel with the advantages of more diverse forms and marketing aspects, the transition from traditional digital publishing to digital publishing has become an inevitable trend. But there are still many problems in digital publishing in our country. Including the transformation of digital copyright awareness and maintenance of digital copyright, the source and maintenance of digital publishing technology, and the scarcity of compound talent resources. In order to solve these problems, we must combine the digital publishing industry with modern information technology. This paper builds a digital market preference prediction model based on big data and fuzzy control algorithms. By analyzing and predicting each consumer's usage information, the digital consumer market preference is obtained. This research uses big data and fuzzy control algorithms to build a consumer market preference estimation model for digital publishing transformation. Through the observation of the consumer market, it can promote digital companies to make effective decisions and conduct reasonable organizational analysis, which can further improve The development process of digital publishing transformation promotes the overall development of the enterprise. Through verification, this model has high accuracy and reliability, can support the operation of actual enterprises, and plays an important role in the development of enterprises. Finally, based on the content of the article research, we put forward the following suggestions for the transformation and development of digital enterprises (1) conduct market analysis through big data and fuzzy control technology, and clarify market positioning (2) promote traditional publishing and digital publishing through big data and fuzzy control technology Integrated Development of Publishing (3) Cultivate Excellent Composite Talents for Digital Publishing Transformation. [ABSTRACT FROM AUTHOR]

A "Big Data" approach was used in part to study the use of printed paper in a 700 bed paperless hospital, in part to study the usefulness of the approach. Between 1,2 and 1,5 million pages were printed each month, corresponding to 10% of a citizens' monthly paper use. The identified use of printed paper did not seem to be high compared to other organisations. The big data approach was not able to answer all our questions, primarily because we did not get log data for the source programmes for the printing. The approach could consequently not provide the data needed to reduce paper printing. [ABSTRACT FROM AUTHOR]

The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm's benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms. [ABSTRACT FROM AUTHOR]

With the development of the Internet and the informatization construction of universities, the massive data accumulated by "campus big data" presents problems such as discreteness and sparseness. Students with abnormal behaviors have become an urgent problem to be solved in student behavior analysis. This paper proposes an early warning method for abnormal behaviour of college students based on multimodal fusion and an improved decision tree (EWMABCS-MFIDT). First, given the insufficient representation of student behavioral portraits and the problems of timeliness and dynamics in behavioral labels, a student behavioral portrait based on the multimodal fusion method is proposed. Second, aiming at the timeliness and backwardness of abnormal behavior prediction, based on student behavior classification prediction, this paper proposes an improved decision tree-based early warning method for abnormal student behavior. Finally, we design a student behavior analysis and early warning framework under the campus big data environment. Taking the abnormal early warning of students' academic performance as an example, compared with other early warning algorithms, the EWMABCS-MFIDT method can improve the accuracy of early warning and make students' educational work more targeted, personalized, and predictive. [ABSTRACT FROM AUTHOR]

The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. [ABSTRACT FROM AUTHOR]

With the advent of the era of Industry 4.0, big data and the Internet of Things came into being. The rise of big data and the Internet of Things has had an impact on all walks of life. The development of big data and the Internet of Things provides an opportunity for the evolution of street form. With the rapid development of smart cities, the street shape is also changing more and more towards the direction of intelligence. Taking Chunxi Road in Chengdu as an example, this paper studies the generation mechanism of street morphology. The factors influencing the generation of street form are divided into two categories: (1) legal factors. (2) Architectural design factors. By building a model, the article quantitatively analyzes the impact of these two types of factors on street form. At the same time, the article conducts an in-depth study on the evolution of ancient Chinese street morphology. The ancient Chinese street form is divided into four stages, namely the Tang and Song Dynasties, the Yuan, Ming and Qing Dynasties, the Late Qing Dynasty, and the founding of New China. The influencing factors of street shape are divided into four categories, namely, the influence of commodity economy, the influence of foreign culture, the influence of building materials, and the influence of traffic. This paper elaborates the influence mechanism of these four factors on the street form in China. The article finally puts forward the development trend of my country's future street form, and points out that the evolution of my country's street form will develop in the direction of intelligence, and intelligent transportation, intelligent garbage classification system, etc. will be applied. The number of brick-and-mortar stores in my country's street layout may decrease. [ABSTRACT FROM AUTHOR]

Transformational advances in environmental sensing have been made possible by the convergence of Cloud Computing and the Internet of Things (IoT). The potential for these technologies is to work together and improve environmental monitoring data analytics. Integrating IoT in the cloud creates a robust environment for advanced analytics by overcoming previous hurdles in data granularity, real-time monitoring, and geographical coverage. These developments aren't without their own set of difficulties, however, including data security, interoperability, and ethical concerns. This paper explores integrating Cloud IoT with environmental sensing. The paper addresses these issues and emphasises the need to establish ethical data-gathering methods, implement standardised communication protocols, and address privacy concerns. The study concludes by examining the challenges, concerns, and potential of integrating Cloud IoT. The integration of Cloud Computing and IoT not only transforms environmental sensing but also provides a solid foundation for collaborative research, data-driven decision-making, and environmentally conscious management. [ABSTRACT FROM AUTHOR]

The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering in several cases by orders of magnitude. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition. [ABSTRACT FROM AUTHOR]

Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students’ abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students’ behavior data. In this study a visual analysis and interactive interface of students’ abnormal behavior based on a clustering algorithm were examined and designed. Firstly, this paper discusses the development of traditional methods for analyzing students’ abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students’ behaviors. By collecting a large number of students’ behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students’ abnormal behaviors. To visually display the clustering results and help users analyze students’ abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users’ in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. The experimental results show that this method can accurately detect and visualize students’ abnormal behaviors and provide intuitive analysis results. This paper makes full use of the advantages of big data to understand students’ behavior patterns more comprehensively and provides a new solution for students’ management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students’ behavior data and needs. [ABSTRACT FROM AUTHOR]

Governments have vast data resources related to a wide-variety of policies and programs. Integrating and sharing data across agencies and departments can add value to these data resources and bring about significant changes in public services as well as better government decisions. However, in addition to the lack of standards and an adequate information architecture, the main obstacles to a centralized government data-sharing strategy are security and privacy concerns. Blockchain - a decentralized peer-to-peer distributed ledger technology - provides a new way to develop sharing mechanisms. In addition, blockchain-based systems are difficult to tamper with and are highly traceable. Based on the current problems of a big data center in the city of Ningbo, China, this paper identifies limitations of this approach and explores the potential of some data sharing mechanism based on blockchain technology. Our analysis describes some potential advantages and the feasibility of using distributed data sharing and automated management mechanisms based on blockchain smart contracts. We also explore implementation challenges and provide some practical recommendations. [ABSTRACT FROM AUTHOR]

Over the last decade, the explosion of "Big Data" and its fusion with AI has led many to believe that the development and integration of AI systems in healthcare will usher in a transformative revolution that democratises access to high quality healthcare and collectively improve patient outcomes. However, the nature of market forces in the evolving data economy, has started to show evidence that the opposite is more likely to be true. This paper argues that there is a poorly understood "Inverse Data Law" that will exacerbate the widening health divide between affluent and marginalised communities because: (1) data used to train AI systems favour individuals that are already engaged with healthcare, who have the lowest burden of disease, but the highest purchasing power; and (2) data used to drive market decisions around investment in AI health technology favours tools that increase the commodification of healthcare through over-testing, over-diagnosis, and the acute and episodic management of disease, over tools that support the patient to prevent disease. This dangerous combination is more likely to cripple efforts towards preventative medicine, as data collection and utilisation tends to be inversely proportional to the needs of the patients served -- the inverse data law. The paper concludes by introducing important methodological considerations in the design and evaluation of AI systems to promote systems improvement for marginalised users. [ABSTRACT FROM AUTHOR]

This paper studies the feasibility of intelligent power design mode, and proves that the intelligent power design mode has significant advantages over the traditional mode by using the method of comparative study. Comparative studies are more distinctive in the breadth and depth of content. With the further acceleration of the urbanization process and the popularization of electronic equipment, people's demand for electricity is gradually increasing. The overall operation of the transmission line is actually highly related to the design mode of the line. The quality of electrical design will be closely related to people's electricity consumption. The stability of the electrical design is directly linked to the sustainability, and it will also be linked to the reputation of the power design company and the safety of public life. The use of a variety of new technologies in the electrical design process to guide the design scheme and identify design problems as soon as possible is the latest trend in the electrical design of current transmission lines. In the context of the rapid development of data science, the functions of comprehensive sorting and providing suggestions provided by big data technology have become the mainstream choice for solving problems in various industries. At present, big data technology has begun to be associated with the power industry, and big data technology has been applied to transmission lines. Electrical design can also solve various dilemmas that exist in current design. Artificial intelligence technology enables computers to have the ability to think like ordinary people through algorithms and training data. Applying artificial intelligence technology to electrical design of transmission lines can make scheme design more intelligent. In order to analyze the current status of electrical design of transmission lines, this paper conducts a questionnaire survey on professionals and practitioners of electrical design of transmission lines. Through a detailed analysis of the questionnaire data, it is found that the electrical design of current transmission lines mainly includes unreasonable line path arrangement, line support towers unreasonable models and insufficient consideration in safety protection design, and combined with big data and artificial intelligence technology, the intelligent arrangement of line paths, tower-shaped intelligent matching and intelligent safety protection strategies are designed. The respondents of the questionnaire conducted a second survey and found that the efficiency, cost, intelligence, sustainability and safety of the electrical design of transmission lines were improved after combining big data and artificial intelligence technology. The intelligent circuit design scheme constructed by big data and artificial intelligence technology can improve the quality of circuit transportation, solve the problems of unreasonable route layout, unreasonable line support tower type and insufficient consideration of safety protection design. Intelligent circuit design scheme compared to the traditional circuit design scheme in the design efficiency, cost, sustainability is significantly improved. [ABSTRACT FROM AUTHOR]

In this paper, the sequential tree recognition method of sensitive data in energy big data center based on rule matching is studied, to accurately identify sensitive data in energy big data center, and improve the operation security of energy big data center. The RETE rule matching algorithm is used to match the sensitive data rules of the energy big data center. The algorithm automatically finds the optimal rete topology, reduces the join intermediate node data, and realizes rule matching. The data cut points after rule matching are divided into balanced cutting points and unbalanced cutting points. The maximum sorting mutual information only exists at the unbalanced cut points. The ordered decision tree can be constructed by traversing the unbalanced cutting points. The data to be identified can be retrieved in the form of data flow to obtain the word frequency, regional information and sensitivity of sensitive words, and the sensitive data can be identified according to the sensitivity calculation results. The experimental results show that the proposed method can effectively identify the sensitive data of energy big data center with high recognition accuracy, and can be applied to the practical application of energy big data center. [ABSTRACT FROM AUTHOR]

Although the global wind energy industry has made considerable progress in recent years, wind turbines suffer from frequent failures since the systems are complicated and the working conditions are far from being satisfactory. For the wind turbines to function well, it is imperative to study the overall status of the wind turbine unit, evaluate the performance of the wind farm, apply intelligent operation and maintenance technology, and improve operation and maintenance strategies on an on-going basis, all of which are based on the operation data of the unit. This paper focuses on the evaluation and extension of the lifetime of wind turbines. Based on relevant knowledge and theories from previous studies, an evaluation method based on big data was designed to do the evaluation, and the results of which were verified by real cases. With the duration of catastrophic failures taken into account, the proposed life prediction algorithm was proved to be effective. If the bearing runs for 34 days, the actual remaining life of wind turbines is 0.2 days. The number predicted for LRM is 0.8 days and that predicted for ILRM is 0.31 days. Compared with LRM, the prediction for ILRM is much more accurate. [ABSTRACT FROM AUTHOR]

Red culture was born in the revolutionary war era. It contains a rich revolutionary spirit and profound historical and cultural connotations. It plays a pivotal role in education, politics, economy and society, and has a crucial influence in all aspects. In this paper, based on the analysis of big data and IoT-related theories, the current issues of red culture dissemination and influence development are obtained through interviews and questionnaire surveys. In terms of red culture dissemination channels, based on big data, solutions such as establishing a digital red culture database, pinpointing demand groups and combining recommendation algorithms are proposed to improve the speed and impact of dissemination. The coordinated red culture has been improved in terms of dissemination speed, dissemination efficiency, depth of influence, number of influencers, and breadth of influence. The innovation of this paper is to study the development of red culture through big data and Internet of things technology, explore the problems existing in the development of red culture from a new perspective, and put forward corresponding strategies, which is of great innovation. [ABSTRACT FROM AUTHOR]

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. [ABSTRACT FROM AUTHOR]

Pedagogic systems are gaining traction in the provision of training, learning, and continuing professional development (often required to maintain professional qualifications). An essential element in pedagogic systems is the matching of teachers (mentors) and students (mentees). In this paper we present an intelligent context-aware learning system based on profile criteria developed using big data analytic solutions. The proposed system is designed to provide systematic support for mentors based on student profiles. The goal of the proposed system is to match the mentor profiles with the type of pedagogic system, the student profile, the student requirements, and the student's goals and expectations. The proposed system is predicated on the use of fuzzy logic definitions with a maximal length matching algorithm using expert knowledge. The proposed system implements a mentor (teacher) and mentee (student) matching algorithm based on their profile criteria. The proposed system has been successfully tested by matching mentor and mentee profiles and preferences. Experimental results show that the proposed system can access multi-factorial mentor and mentee profiles, effectively match suitable mentors (teachers) with appropriate mentees (students), and meet the mentee expectations. [ABSTRACT FROM AUTHOR]

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data and artificial intelligence, e-commerce enterprises have used a large number of advanced technologies and knowledge management methods to improve work efficiency. In the context of e-commerce, the innovation of enterprise marketing management model has become one of the important contents of the company's business development in the e-commerce era. Focusing on the core concept of "e-commerce marketing model innovation", this paper conducts a comprehensive and systematic research on the e-commerce marketing model innovation of enterprise e-commerce, and focuses on the two aspects of e-commerce marketing model innovation and e-commerce model performance evaluation. The purpose of this paper is to understand the importance of innovation factors in the innovation of e-commerce marketing models through questionnaires, so as to provide new ideas for the innovation path of e-commerce marketing models. This paper adopts the questionnaire survey method and data analysis method. According to the survey results, 39, 31, 33, and 35 of the respondents believe that market positioning, business strategy, marketing promotion, and operation management should be prioritized as innovative elements, among which market positioning accounts for a relatively high proportion, followed by operation management. It can be seen that most of the respondents believe that in order to innovate the marketing model of e-commerce, we must start from the aspects of market positioning, business strategy, marketing promotion, and operation management. Combined with the era background and related content of big data and artificial intelligence, this paper studies e-commerce and marketing models, so as to provide new ideas for the innovative path of e-commerce marketing models. [ABSTRACT FROM AUTHOR]

Big data refers to the data set that cannot be captured, managed and processed by conventional software tools within a certain period of time. In the seventh paper [[1]], in order to overcome the problem of poor recovery and stability of traditional big data fault-tolerant technology, a fault-tolerant technology of big data cluster in distributed flow processing system is proposed. By using spark application framework, a fault-tolerant model of big data cluster is built to realize the fault-tolerant of big data cluster in distributed flow processing system. [Extracted from the article]

Medical events are often infrequent, thus becomes hard to predict. In this paper, we focus on predictor that forecasts whether a medical event would occur in the next year, and analyzes the impact of event's frequency and data size via predictor's performance. In the experiment, we made 1572 predictors for medical events using Medical Insurance Claims (MICs) data from 800,000 participants and 205.8 million claims over 8 years. The result revealed that (a) forecasting error will be increased when predicting low-frequency events, and (b) increasing the number of training dataset reduces errors. This result suggests that increasing data size is a key to solve low frequency problems. However, we still need additional methods to cope with sparse and imbalanced data.

Background: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics., Objective: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision., Methods: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data., Results: The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos., Conclusion: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

MOTIVATION: With the enhancement of people's awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users' privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users' personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy. [ABSTRACT FROM AUTHOR]

3D printing technology is a popular new technology that combines information technology with mechanical engineering and material science, and is being widely used in various fields. From the computer point of view, the basic technology of 3D printing is to slice and scan each part of the 3D model. The slicing algorithm used directly affects the speed of slicing and the effect of post-printing, and the result of slicing needs to be optimized. This paper analyzes the slicing algorithm and partition scanning strategy related technologies of 3D printing models and solves the practical problems of models in the 3D printing process. This paper first studies the slicing algorithm in the 3D printing process, then obtains and intercepts the cross-sectional contour, and finally performs mosaic and partition scanning on the sliced polygon, and uses experiments to verify the effectiveness of the research. The experimental results show that compared with other 3D printing methods, the printing method in this paper reduces the slicing time by 23.61%, and the fitting error range and the fluctuation range of the residual height of the fusion volume are smaller. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 (Corona Virus Disease 2019), the Chinese government has taken strict measures to prevent and control the epidemic. Although the spread of the virus has been controlled, people's daily life and work have been affected and restricted to varying degrees. Thus people have different sentiments, these may affect people's implementation and compliance with the policies, thus affecting the effectiveness of epidemic prevention and control. At present, few pieces of literature have analyzed the relationships between people's feelings, policies, and epidemic trends. The object of this paper is to analyze the text content on social media, to find out the impact of the epidemic blockade policy on the public mood and the concerns expressed by the public about policies changes, and the interaction between policies and epidemic states at different stages of the epidemic. In this paper, we collected the posts of two cities where the epidemic occurred at the same time for analysis and comparative study. On the one hand, we revealed the changes in public attention and attitudes in the two regions during the epidemic, the other hand, it also reflects the differences in public sentiment between the two regions, as well as the correlation between emotions and policies and epidemic trends when different policies are adopted under different circumstances. The obtained results have a certain guiding significance for public health departments to formulate reasonable epidemic prevention policies. [ABSTRACT FROM AUTHOR]

Students' English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student's performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student's diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students' unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model's performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student's various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time. [ABSTRACT FROM AUTHOR]

In order to improve the early warning effect of equipment abnormal state and shorten the early warning time, this paper designs an early warning method of laboratory equipment abnormal state based on the Internet of things and running big data. Collect the running status data of laboratory equipment in the environment of Internet of things, and implement dimension reduction processing on the collected running status data. After the dimensionality reduction, extract the abnormal characteristics of big data of laboratory equipment running. On the basis of iterative update, the real-time feature analysis results are compared with the abnormal feature set, and the early warning response program is started according to the abnormal. According to the experimental results, the maximum false alarm rate of this method is only 1.34%, and the abnormal state response is always kept below 4.0 s when applied, which fully proves that this method effectively realizes the design expectation. [ABSTRACT FROM AUTHOR]

The ultimate goal of listed companies is to maximize shareholders' wealth. With the increasingly fierce market competition, enterprise managers are constantly exploring the key indicators that have an important impact on the financial performance (FP) of enterprises, and achieve the expected FP of shareholders by improving these key indicators. On the basis of the existing enterprise performance measurement system and index research, through expert scoring to determine the secondary indicators, this paper selects 87 small and medium-sized board listed companies which officially announced the implementation of equity incentive from 2009 to 2012 as the sample, takes the financial information disclosed in 2013 as the empirical data, and analyzes the traditional multiple linear regression analysis (MLR) When dealing with big data, especially the data with hierarchical structure, this paper proposes a partial regression coefficient calculation model based on hierarchical data, constructs a multiple nonlinear regression model, and concludes through empirical analysis that there is a nonlinear correlation between equity incentive ratio and corporate performance, and that there is an interval effect between equity incentive ratio and corporate performance. We also present Fuzzy based financial performance prediction of listed companies. Finally, we demonstrate Comparative analysis for financial prediction in term of accuracy between multiple regression model and fuzzy logic system and result deduce that fuzzy logic gives better accuracy than regression model. [ABSTRACT FROM AUTHOR]

In order to build a virtual urban planning model and improve the effect of urban planning, this paper builds a virtual urban planning design model based on GIS big data technology and machine learning algorithms, and proposes a solution that combines multiple features. With the development of polarized SAR in the direction of high resolution, a single feature often cannot fully express the detailed information of ground objects, resulting in poor classification results and low accuracy. The combination of multiple features can express feature information well. In addition, this paper uses the ELM method to plan SAR ground object classification, uses an extreme learning machine classification algorithm with fast learning speed and good classification effect, and uses ELM as a classifier. Finally, this paper designs experiments to explore the performance of the model constructed in this paper from two aspects: detection accuracy and planning score. The research results show that the model constructed in this paper meets the expected goals. [ABSTRACT FROM AUTHOR]

Big data requires new technologies and tools to process, analyze and interpret the vast amount of high-speed heterogeneous information. A simple mistake in processing software, error in data, and malfunctioning in hardware results in inaccurate analysis, compromised results, and inadequate performance. Thus, measures concerning reliability play an important role in determining the quality of Big data. Literature related to Big data software reliability was critically examined in this paper to investigate: the type of mathematical model developed, the influence of external factors, the type of data sets used, and methods employed to evaluate model parameters while determining the system reliability or component reliability of the software. Since the environmental conditions and input variables differ for each model due to varied platforms it is difficult to analyze which method gives the better prediction using the same set of data. Thus, paper summarizes some of the Big data techniques and common reliability models and compared them based on interdependencies, estimation function, parameter evaluation method, mean value function, etc. Visualization is also included in the study to represent the Big data reliability distribution, classification, analysis, and technical comparison. This study helps in choosing and developing an appropriate model for the reliability prediction of Big data software. [ABSTRACT FROM AUTHOR]

This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus. We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it's a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. [ABSTRACT FROM AUTHOR]

By comparing several cloud computing of big data network center during COVID-19, this paper proposes a new topology model, which realizes two functions of cloud computing big data center caching and big data real-time distribution. In addition, cloud computing network requires higher performance than traditional application big data center, which makes the consideration of network platform construction performance different from the traditional understanding. During COVID-19, we deeply understood the underlying attributes of cloud, combined with the topology model, we can realize the decoupling of cloud computing big data system, change the situation of direct connection between upstream and downstream, and have more reliable and efficient transmission of message and command big data. [ABSTRACT FROM AUTHOR]

In today's big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies. [ABSTRACT FROM AUTHOR]

Big data and the idea of the Internet of Everything have become popular technologies at the moment. The application of these two technologies has also promoted the development of various industries. The education and training industry is the focus of the country and the public. Piano education is the cradle of artists. The application of Internet of Things technology to the piano education industry will help to upgrade the industry teaching model. In order to investigate the current situation of online and offline hybrid teaching in piano education, this paper uses the method of questionnaire distribution to conduct a survey on teachers and students in 50 colleges of music and music training institutions. It was found that after using big data and Internet of Things technology to innovate the online and offline hybrid teaching mode of piano education, so the teaching efficiency of teachers and the learning effect of students have been greatly improved. [ABSTRACT FROM AUTHOR]

With the continuous progress of data science, big data technology has been continuously integrated into human daily life. The application of big data technology in the field of education to improve the teaching and learning efficiency of teachers and students has become the focus of current scholars. With the popularization of the concept of the Internet of Everything, the Internet of Things technology has gradually become a hot spot for technology fans. It is widely used in home and body-worn devices. Applying the Internet of Things technology in the education field to obtain student status feedback data can make teachers more accurately understand the goals of student learning. The continuous raging of the epidemic has led to the change of many courses from offline face-to-face teaching to home online teaching, which has brought about a huge change in the traditional offline face-to-face physical education teaching method. Therefore, in order to better protect against the epidemic and prevent the spread of the epidemic more effectively, X Academy responded to the country's call to "suspend classes without teaching, and without stopping learning", so that physical education courses other than other cultural courses will be conducted offline and face-to-face. Teaching has changed to online physical education. This paper firstly defines the concepts of big data, Internet of Things and online physical education teaching, and then conducts research on the current situation and countermeasures of online physical education teaching. Find optimized countermeasures, so that X College can get better teaching results when it conducts online physical education teaching again in the face of similar public health emergencies in the future, allowing students to exercise, improve physical fitness, enhance sports skills, and enhance their own resistance. power goal. The research method of this paper mainly adopts the data analysis method of questionnaire survey and statistics. Through the second survey, it is found that all aspects of online physical education teaching have been improved after adopting the improved strategy. [ABSTRACT FROM AUTHOR]

Around 10% to 20% of patients experience Long COVID after recovering from COVID-19. Many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their opinions and feelings regarding Long COVID. In this paper, we analyse text messages in the Greek language posted on the Twitter platform in 2022 to extract popular discussion topics and classify the sentiment of Greek citizens regarding Long COVID. Results highlighted the following discussion topics: Greek-speaking users discuss Long COVID effects and time required to heal, Long COVID effects in specific population groups like children and COVID-19 vaccines. 59% of analysed tweets conveyed a negative sentiment while the rest had positive or neutral sentiment. The analysis shows that public bodies could benefit from systematically mining knowledge from social media to understand public’s perception of a new disease and take action. [ABSTRACT FROM AUTHOR]

In the modern era, digital data processing with a huge volume of data from the repository is challenging due to various data formats and the extraction techniques available. The accuracy levels and speed of the data processing on larger networks using modern tools have limitations for getting quick results. The major problem of data extraction on the repository is finding the data location and the dynamic changes in the existing data. Even though many researchers created different tools with algorithms for processing those data from the warehouse, it has not given accurate results and gives low latency. This output is due to a larger network of batch processing. The performance of the database scalability has to be tuned with the powerful distributed framework and programming languages for the latest real-time applications to process the huge datasets over the network. Data processing has been done in big data analytics using the modern tools HADOOP and SPARK effectively. Moreover, a recent programming language such as Python will provide solutions with the concepts of map reduction and erasure coding. But it has some challenges and limitations on a huge dataset at network clusters. This review paper deals with Hadoop and Spark features also their challenges and limitations over different criteria such as file size, file formats, and scheduling techniques. In this paper, a detailed survey of the challenges and limitations that occurred during the processing phase in big data analytics was discussed and provided solutions to that by selecting the languages and techniques using modern tools. This paper gives solutions to the research people who are working in big data analytics, for improving the speed of data processing with a proper algorithm over digital data in huge repositories. [ABSTRACT FROM AUTHOR]

Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand. [ABSTRACT FROM AUTHOR]

Firstly, the paper analyzes the information propagation mode of Sina Weibo, and proposes a new MURank algorithm based on PageRank algorithm, which fully considers the relationship between users, the transmission relationship between users and microblogs, and the forwarding relationship between microblogs. At the same time build the user-microblog mode diagram. The design of the thesis is also more in line with the information dissemination mode of the actual Weibo platform. Experiments prove that MURank can effectively solve the impact of "zombie powder" and "level sinking" problems, and more realistically reflect the level of influence of users. Secondly, the paper combines BP neural network algorithm to improve the QoE hierarchical index system, and proposes a five-level index model. The mobile video service is taken as an example to illustrate the establishment process of the indicator system and the QoE quantitative evaluation method. By verifying on the open source distributed platform Hadoop platform, it is found that the proposed method is a solution to deal with massive data analysis. [ABSTRACT FROM AUTHOR]

Stored-grain temperature is the most important factor in grain storage. According to the measured data, the temperature in the grain pile can be effectively predicted, which can find problems in advance, reduce grain loss and increase grain quality. Long Short-Term memory (LSTM) can perform better in longer sequences than ordinary RNN. This paper is applied to the analysis of big data of grain storage and the early warning of grain storage temperature. In this paper, the selected LSTM is optimized and the early warning model of grain situation is established, and the analysis steps of the early warning model are given. In order to verify the availability of the improved LSTM network structure, RNN and three variants were used to predict the grain temperature under the same conditions, the prediction effect of the improved CLSTM is better. [ABSTRACT FROM AUTHOR]

Consider a simple graph G = 〈V, E〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, ..., n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k. In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K2n - {ne} into n (n - 1) 2 edge distinct copies of C4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. [ABSTRACT FROM AUTHOR]

This paper aims to explore unsupervised cross-lingual word representation learning methods with the specific task of acquiring a bilingual translation lexicon on a monolingual corpus. Specifically, an unsupervised cross-lingual word representation co-training scheme based on different word embedding models is first designed and outperforms the baseline model. In this paper, we adeptly tackles the obstacles encountered in higher education foreign language teaching and underscores the necessity for inventive teaching methods, and design and implement a linear self-encoder-based principal component acquisition scheme for the interpoint mutual information matrix obtained from a monolingual corpus. And on top of this, a collaborative training scheme based on linear self-encoder for cross-language word representation is designed to improve the learning effect of cross-language word embedding. The results of the study show that the most obvious rise in the pre and post tests of the experimental class in the practical application of the foreign language teaching model based on the method of this paper is the word sense guessing, which rose by 23.12%. Sentence meaning comprehension increased by 23.39%, main idea by 16.61%, factual details by 15.47%, and inferential judgment by 10.28%. Thus, the feasibility of the unsupervised cross-linguistic word representation learning collaborative training method is further verified. [ABSTRACT FROM AUTHOR]

In the era of big data, the complexity of data is increasing. Problems such as data imbalance and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of imbalanced data has become increasingly prominent, it is necessary to find appropriate methods to enhance classification performance of classifiers on such datasets. In response, this paper proposes a mixed sampling method (ISODF-ENN) based on iterative self-organizing (ISODATA) denoising diffusion algorithm and edited nearest neighbors (ENN) data cleaning algorithm. The algorithm first uses iterative self-organizing clustering algorithm to divide minority class into different sub-clusters, then it uses denoising diffusion algorithm to generate new minority class data for each sub-cluster, and finally it uses ENN algorithm to preprocess majority class data to remove the overlap with the minority class data. Each sub-cluster is oversampled according to sampling ratio, so that the oversampled minority class data also conforms to the distribution of original minority class data. Experimental results on keel datasets demonstrate that the proposed method outperforms other methods in terms of F-value and AUC, effectively addressing the issues of class imbalance and class overlap. [ABSTRACT FROM AUTHOR]

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm's longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method's performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

This paper took the course recommendation as an example to prove that the personalized intelligent detection and recommendation algorithm system provides a convenient means for this problem. In view of the problems such as low recommendation accuracy, lack of personalization and long running time of the current personalized course recommendation system, this paper studied the personalized intelligent detection and recommendation algorithm system with the help of big data technology, hoping to solve the existing problems. The general search results of the traditional recommendation system have been unable to meet the needs of users. In order to improve the course recommendation system, this paper used big data technology to build a scientific framework, and made an in-depth analysis of various problems existing in the traditional system and strove to solve them. The results showed that using big data intelligent systems to search for ten sets of intelligent courses had a search accuracy of between 96% and 98%, indicating that big data can achieve more accurate and efficient results in the construction of intelligent course recommendation systems, and can construct a new course recommendation system to meet user needs. [ABSTRACT FROM AUTHOR]

RFID is used in logistics in the automotive industry to automate processes and optimise material flow. However, the data generated by RFID installations during operation offer more potential for further analyses to collect even more benefits from the technology. Therefore, in this paper, RFID data will be used to create a digital twin of the RFID-enabled material flow (DTRMF) in real-time and to programme various big data analyses. The architecture of the DTRMF must meet various qualitative requirements. Since the big data and digital twin architectures available in the literature either do not optimally fulfil all these requirements, or they are not described in enough detail to support real applications, this paper presents a new digital twin architecture for RFID-enabled material flow. This architecture consists of the data ingestion layer, data processing and analyses layer, data storage layer, visualisation layer, and the optional semantic layer. In addition, suitable technologies for the implementation of the architecture are described, and the feasibility of the architecture is demonstrated and verified by means of a case study. [ABSTRACT FROM AUTHOR]

For the pattern recognition, most classification models are solved iteratively, except for Linear LDA, KLDA and ELM etc. In this paper, a nonlinear classification network model based on predefined evenly-distributed class centroids (PEDCC) is proposed. Its analytical solution can be obtained and has good interpretability. Using the characteristics of maximizing the inter-class distance of PEDCC and derivative weighted minimum mean square error loss function to minimize the intra-class distance, we can not only realize the effective nonlinearity of the network, but also obtain the analytical solution of the network weight. Then, the sample is classified based on GDA. In order to further improve the performance of classification, PCA is used to reduces the dimensionality of the original sample, meanwhile, the CReLU activation function are adopted to enhances the expression ability of the features. The network transforms the samples into the higher dimensional feature space through the weighted minimum mean square error, so as to find a better separation hyperplane. In experiments, the feasibility of the network structure is verified from pure linear 푾 , 푾 + Tanh, and PCA + 푾 + Tanh respectively on many small data sets and large data sets, and compared with SVM and ELM in terms of training speed and recognition rate. The results show that, in general, this model has advantages on small data set both in recognition accuracy and training speed, while it has advantages in training speed on large data sets. Finally, by introducing a multi-stage network structure based on the latent feature norm, the classifier network can further significantly improve the classification performance, the recognition rate of small data sets is effectively improved and much higher than that of existing methods, while the recognition rate of large data sets is similar to that of SVM. [ABSTRACT FROM AUTHOR]