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As an educational concept based on learning output, OBE (Outcome-Based Education) is student-centered and emphasizes students' personal progress and learning achievement. Based on BD (big data) analysis, this paper proposes a new talent training mode for the electronic information science and technology specialty. This model employs BD analysis technology to examine the correlation index between social demand and talent cultivation, based on the employment situation of students in the country in previous years. The teaching reform was carried out under the OBE concept, and a new training scheme was formed. Training objectives, graduation requirements, curriculum system, and continuous improvement mechanism were all determined. This paper proposes an algorithm for determining the degree of similarity between the knowledge required by the organization and the knowledge held by its employees. The person with the highest similarity is identified as the training candidate by the algorithm, and the training candidate is then trained according to the knowledge that the organization requires. Talent training in the field of electronic information science and technology has yielded positive results. [ABSTRACT FROM AUTHOR]

In order to improve the effect of copying and recreation of painting works, this paper combines mobile digital multimedia big data technology to improve the image coding algorithm, identify the characteristics of existing works, apply the algorithm to the detailed analysis of painting works, and construct the main functional structure modules of the system. Moreover, this paper combines the existing hardware equipment to construct the painting works' recreation system and obtains the image processing module. After the system is constructed, the effect of copying and recreating painting works is analyzed through the mobile digital multimedia big data analysis technology. Finally, this paper constructs the system of this paper through simulation methods and uses experiments to calculate the feature recognition effect and copy effect of the painting works of the system. Through experimental analysis, it can be known that the copying and recreation system of painting works based on mobile digital multimedia big data analysis proposed in this paper can help painters effectively improve the effect of recreation. [ABSTRACT FROM AUTHOR]

In digital marketing, the core advantages of scientific and technological means such as artificial intelligence and big data analysis gradually appear and pay attention to them. This paper studies the accuracy of digital marketing and proposes an intelligent algorithm based on data analysis, which improves the effect of marketing communication. Through the combination of intelligent algorithms and big data analysis, the data are convincing. Through the comparison and improvement of intelligent algorithm logistic regression and XGBoost, this paper puts forward an improved algorithm of XGBoost based on Bayesian optimization parameters, which can improve the efficiency of digital marketing communication and enhance the social influence of digital marketing. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent fertilisation, according to actual fertilisation needs, this paper combines big data technology to perform data analysis, and designs a variable fertilisation mechanical structure that can be used for mixed fertilisation to realise mixed fertilisation of multiple crops. Moreover, from the perspective of precise fertilisation, this paper combines the actual needs of variable fertilisation to construct a control system, and adopts an adaptive federated filter structure to solve the influence of equipment errors on system position estimation. In addition, this paper solves the problems of excessive partial application and low fertiliser utilisation in traditional fertilisation, as well as the resulting waste, environmental pollution, and poor quality of agricultural products. Furthermore, this paper designs and manufactures a dual-variable application test device, and conducted a fertiliser discharge test on the test device. Finally, this paper uses big data technology to analyze the experimental data of variable fertilisation operation machinery. From the experimental research, it can be seen that the effect of the fertilisation operation machinery designed in this paper meets actual needs. [ABSTRACT FROM AUTHOR]

In order to promote the development of agro-ecological economy and improve the reliability of the development model of agricultural circular economy, this paper explores the general framework of the coordinated development of the agricultural logistics ecosystem from the perspective of the construction of the agricultural logistics ecosystem, the composition of the ecosystem, the synergy stage, and the synergy goal. Moreover, this paper combines big data and Internet of Things technology to analyze its own path of the cooperative development mechanism of the agricultural logistics ecosystem, and uses typical cases to show the internal relationships between the main bodies of the agricultural logistics ecosystem. In addition, this paper attempts to explain the core essence of the agricultural logistics ecosystem and the internal relations of the subjects through the practice of enterprises. After constructing the system framework, this paper combines the intelligent algorithm to construct the intelligent model structure, and analyzes the system function realisation process based on the actual situation. Finally, this paper evaluates the performance of the algorithm model constructed in this paper through experimental research. From the experimental statistical results, it can be seen that the algorithm model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects. [ABSTRACT FROM AUTHOR]

This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students' personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education. [ABSTRACT FROM AUTHOR]

Along with the development and promotion of Internet technology, new media are increasingly diversified, enriching and changing our lives. This paper focuses on outdoor interactive advertising as a communication method, analyzing its creative features and applications. Unlike traditional advertising, where audiences receive passively, new media interactive advertising establishes a more direct communication method for consumers and products, creating different sensory stimulation and emotional experience environments according to the characteristics of products and conducting a series of interactions with consumers' sensory systems such as vision, hearing, smell, taste, and touch. The advertising method has also changed from passive reception to active participation. Finally, combining the characteristics of accurate delivery and the diversity of presentation forms of interactive advertising under the big data platform, the theme conception and form design of interactive advertising is proposed. The interactive design principles of highlighting the simplicity of the theme are explored. The interactive design principle of highlighting the simplicity of the theme is proposed, and the combination of color, text, and graphics is explored by the aesthetic visual design of the audience and the premise of focusing on the audience's feelings. Starting from the visual effect of outdoor interactive advertising, the change in the way advertising is conveyed is interpreting to us the arrival of a new communication concept, the arrival of the Internet-centered and technology-centered data integration era. The research in this paper aims to provide useful support for the diversified development of outdoor interactive advertising in the new media environment by exploring how to skillfully realize the creative expression of outdoor interactive advertising in the new media environment. [ABSTRACT FROM AUTHOR]

In a research environment characterized by the five V's of big data, volume, velocity, variety, value, and veracity, the need to develop tools that quickly screen a large number of publications into relevant work is an increasing area of concern, and the data-rich food industry is no exception. Here, a combination of latent Dirichlet allocation and food keyword searches were employed to analyze and filter a dataset of 6102 publications about cold denaturation. After using the Python toolkit generated in this work, the approach yielded 22 topics that provide background and insight on the direction of research in this field, as well as identified the publications in this dataset which are most pertinent to the food industry with precision and recall of 0.419 and 0.949, respectively. Precision is related to the relevance of a paper in the filtered dataset and the recall represents papers which were not identified in the screeningmethod. Lastly, gaps in the literature based on keyword trends are identified to improve the knowledge base of cold denaturation as it relates to the food industry. This approach is generalizable to any similarly organized dataset, and the code is available upon request. Practical Application: A common problem in research is that when you are an expert in one field, learning about another field is difficult, because you may lack the vocabulary and background needed to read cutting edge literature froma new discipline. The Python toolkit developed in this research can be applied by any researcher that is new to a field to identify what the key literature is, what topics they should familiarize themselves with, and what the current trends are in the field. Using this structure, researchers can greatly speed up how they identify new areas to research and find new projects. [ABSTRACT FROM AUTHOR]

With the emergence of social media and the Internet, bulks of data are generated every day in different fields. However, filtering out useful information, which can be turned into knowledge, requires significant effort. This can be achieved using various Big Data Technologies, statistics, data mining and a variety of machine learning algorithms that corporates and businesses are using to improve their performance every day for a long time now. In this paper, we analyze YouTube trending video data that analyses the target that grabs the user's attention over a relatively short time. We present our analysis by measuring, mining, analyzing and comparing key aspects of time-series YouTube data with respect to its view and audience response statistics from 40 000 trending YouTube videos collected over 205 days. We have performed an exploratory data analysis (EDA) on all of it's aspect to get data insights and used statistics to find similarities between them to understand viewing pattern of different video categories. We also compare and observe the variation of activity over time with the nature of the event that affects the quality of our analysis. We make the viewership pattern clear belonging to each category separately by direct analysis. The results and findings can help us predict the category of the video that could probably be trending next in the upcoming days and help individuals get boosted views and performance when they upload video as per the predictions and analysis. [ABSTRACT FROM AUTHOR]

Divide and conquer algorithm is a common strategy applied in big data. Model averaging has the natural divide-and-conquer feature, but its theory has not been developed in big data scenarios. The goal of this paper is to fill this gap. We propose two divide-and-conquer- type model averaging estimators for linear models with distributed data. Under some regularity conditions, we show that the weights from Mallows model averaging criterion converge in L2 to the theoretically optimal weights minimizing the risk of the model averaging estimator. We also give the bounds of the in-sample and out-of-sample mean squared errors and prove the asymptotic optimality for the proposed model averaging estimators. Our conclusions hold even when the dimensions and the number of candidate models are divergent. Simulation results and a real airline data analysis illustrate that the proposed model averaging methods perform better than the commonly used model selection and model averaging methods in distributed data cases. Our approaches contribute to model averaging theory in distributed data and parallel computations, and can be applied in big data analysis to save time and reduce the computational burden. [ABSTRACT FROM AUTHOR]

One of the problems in implementing DRPs is the lack of sufficient understanding of consumers' behaviour when implementing DRPs. This paper compares consumers' load patterns annually by the improved Weighted Fuzzy Average (WFA) K‐means clustering method. According to the collected data, DRPs are discussed annually using a combined Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). To make CNN‐LSTM a practical algorithm for executing DRPs, Time Series Prediction (TSP) operations must be performed, and input data must be memorized. Finally, according to the TSP done for the obtained data, the implementation of DRPs in practice is facilitated. Then, the DRPs executed with the Deep Learning (DL) model and the power model will be prioritized by Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method, and decision indicators will be determined and weighted using the Shannon entropy method. The numerical studies section shows that the CNN‐LSTM algorithm is able to simulate DRPs with the Mean Absolute Percentage Error (MAPE) almost below 1% in the residential cluster and the highest MAPE in the commercial cluster of 15%. Nevertheless, this algorithm could easily give the same answer as the DRPs power model in the optimal selection of programs. [ABSTRACT FROM AUTHOR]

Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms have been studied for text data analysis. Most of them were analytical methods based on the generalized linear model (GLM). For the GLM, text data analysis is performed based on the assumption of the error included in the given data and follows the Gaussian distribution. However, the GLM has shown limitations in the analysis of text data, including data sparseness. This is because the preprocessed text data has a zero-inflated problem. To solve this problem, we proposed a text data analysis using the generalized linear mixed model (GLMM) and Bayesian visualization. Therefore, the objective of our study is to propose the use of GLMM to overcome the limitations of the conventional GLM in the analysis of text data with a zero-inflated problem. The GLMM uses various probability distributions as well as Gaussian for error terms and considers the difference between observations by clustering. We also use Bayesian visualization to find meaningful associations between keywords. Lastly, we carried out the analysis of text data searched from real domains and provided the analytical results to show the performance and validity of our proposed method. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is an important preprocessing step in data analytics. It is used to select a subset of the original feature set such that the selected subset does not affect the classification performance significantly. Its objective is to remove irrelevant and redundant features from the original dataset. FS can be done either in offline mode or in online mode. The basic assumption in the former mode is that the entire dataset has been available for the FS algorithm; and the FS algorithm takes multiple epochs to select optimal feature subset that gives good accuracy. In contrast, the FS algorithms in online mode take input data one instance at a time and accumulate knowledge by learning each one of them. In online mode each instance of the original dataset is considered as training and testing sample as well. The offline FS algorithms require long time periods, if the data to be processed is large such as Big data. Whereas online FS algorithms will take only one epoch to learn the entire data and can produce the results swiftly which is highly desirable in the case of Big data. This paper deals with the online FS problem and provides a novel Feature Selection algorithm which uses the Sparse Gradient method to build a sparse classifier. In this proposed method, an online classifier is built and maintained throughout the learning process and feature weights, which are limited to a particular boundary limit, are reduced in a step by step decrement process. This method creates sparsity in the classifier. Effectively, the built classifier is used to select optimal feature subset from the incoming data. As this method reduces the weights in the classifier in step by step manner, only those important features which have value higher than the boundary survive from this repeated decrement process. The resultant optimal feature subset is formed using these non-zero weighted features. Most significantly, this particular method can be used with any learning algorithm. To show its applicability with different learning algorithms, various online feature selection models have been built using Learning Vector Quantization, Radial Basis Function Networks and Adaptive Resonance Theory MAP. In all these models, the proposed Sparse Gradient method is used. The encouraging results shows the effectiveness of the proposed method with different learning algorithms in medium and large sized benchmark datasets. [ABSTRACT FROM AUTHOR]

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future.We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research. [ABSTRACT FROM AUTHOR]

In this paper, we examine the properties of the Jones polynomial using dimensionality reduction learning techniques combined with ideas from topological data analysis. Our data set consists of more than 10 million knots up to 17 crossings and two other special families up to 2001 crossings. We introduce and describe a method for using filtrations to analyze infinite data sets where representative sampling is impossible or impractical, an essential requirement for working with knots and the data from knot invariants. In particular, this method provides a new approach for analyzing knot invariants using Principal Component Analysis. Using this approach on the Jones polynomial data, we find that it can be viewed as an approximately three-dimensional subspace, that this description is surprisingly stable with respect to the filtration by the crossing number, and that the results suggest further structures to be examined and understood. [ABSTRACT FROM AUTHOR]

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners' dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance. [ABSTRACT FROM AUTHOR]

This paper reflects on a number of trends towards a more open and reproducible approach to geographic and spatial data science over recent years. In particular, it considers trends towards Big Data, and the impacts this is having on spatial data analysis and modelling. It identifies a turn in academia towards coding as a core analytic tool, and away from proprietary software tools offering 'black boxes' where the internal workings of the analysis are not revealed. It is argued that this closed form software is problematic and considers a number of ways in which issues identified in spatial data analysis (such as the MAUP) could be overlooked when working with closed tools, leading to problems of interpretation and possibly inappropriate actions and policies based on these. In addition, this paper considers the role that reproducible and open spatial science may play in such an approach, taking into account the issues raised. It highlights the dangers of failing to account for the geographical properties of data, now that all data are spatial (they are collected somewhere), the problems of a desire for n = all observations in data science and it identifies the need for a critical approach. This is one in which openness, transparency, sharing and reproducibility provide a mantra for defensible and robust spatial data science. [ABSTRACT FROM AUTHOR]

Agricultural product network marketing still has problems such as weak infrastructure, incomplete logistics system, and unremarkable product brand, and there is a little combination of basic theory and practice. In order to improve the marketing effect of agricultural products, based on the intelligent recommendation algorithm, this paper combines data mining technology to construct an intelligent agricultural product business and product marketing recommendation model. Moreover, from the perspective of users and commodities, this paper proposes a general method of reordering recommendation results. In addition, this paper understands the results from the user perspective and the results from the commodity perspective as row sorting and column sorting respectively, and designs a linear fusion method of row sorting matrix and column sorting matrix to obtain new recommendation results. Finally, this paper constructs a framework model for agricultural product promotion, and analyses the performance of the model constructed in this paper from an experimental perspective. On the whole, the agricultural product marketing model constructed in this paper is effective. [ABSTRACT FROM AUTHOR]

Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment. [ABSTRACT FROM AUTHOR]

Most researchers establish research directions in their study of new fields by providing expert advice or publishing expert papers. The existing academic search services display papers by field but do not provide experts by field. Therefore, researchers are left to judge experts in each field by analyzing the papers for themselves. In this paper, we design and implement an expert search system based on papers that have been published in the academic societies. The academic expert search system is based on a big data processing system to handle a large amount of data in academic fields. It calculates an expert score using quality and influence factors. The quality factor is calculated based on the citations, impact factor, and recentness of a paper. The influence factor is measured by the sparsity of a field and the degree of contributiveness of an author. The proposed system provides various services such as expert searches, keyword searches, the hot topics, expert relationships, and academic society statistics. By finding experts in a specific field, our system can support researchers' research activities. [ABSTRACT FROM AUTHOR]

The Erasmus+ project, entitled 'Digital Erasmus – a roadmap to using building performance simulation to achieve resilient design' (DesRes), seeks to transform the learning experience of students in built environment disciplines using a continuous digital learning cycle. Three universities play a part in this project: Graz University of Technology (TU Graz), Delft University of Technology (TU Delft) and the University of Strathclyde (UoS), each developing and delivering a module to complete the learning experience in building simulation. This paper describes the aims and learning objectives associated with the workshops taught at TU Graz as part of the module dedicated to energy monitoring. These workshops tackle the complexity of working with large data sets, which commonly arise from energy monitoring research. In particular, the workshops aim to provide a practical understanding of how to identify, handle, reshape, clean up and evaluate important summary statistics from incomplete data sets. These are fundamental skills in building simulation where model validation and calibration are increasingly commonplace. [ABSTRACT FROM AUTHOR]

Social network (SN) plays an important role in issue focusing, public opinion filtering, and scheme policy making in the dissemination of ideological and political education (IAPE), which promotes it towards a more accurate and safe public health direction. After studying the information dissemination process and diffusion mode of IAPE, this paper puts forward the UECSR model of its information dissemination model of blockchain SN, adds the unique consensus node state of that, and redefines the information state transition process and dissemination probability calculation. Finally, the influence of the propagation probability of blockchain is discussed through experimental simulation classification, which can verify the model's validity and rationality. [ABSTRACT FROM AUTHOR]

In order to guide and meet people's health needs, various resources are flowing to the health sector, and the regional health economy is bound to develop rapidly, which increases the possibility of integrating and developing the sports economy and health industry in the context of the public health sector. As the health of the population improves, life expectancy increases, science and technology develops, and lifestyles change, the health economy sector is becoming increasingly active and both sport and healthcare will contribute to the development of the regional health economy. Big data, as a new management mindset and technical tool, brings new opportunities and challenges to the integrated development of sports economy and health industry. Based on the basic characteristics of big data, this paper provides a strategy for the development of the sports and health integration industry by using research methods such as grey correlation analysis, expert interviews, and agglomeration measures, starting from the problems faced by the integration development of China's sports industry to guarantee the construction of conditions. The experiment is analysed through the big data presentation of the health industry and the big data modelling ideas of grey correlation analysis, which provides new ideas to expand the application research of grey correlation analysis in the field of integration development. [ABSTRACT FROM AUTHOR]

Language is a symbol reflecting ideas and is an important medium for people to realize information transfer and communication. Culture is all the material and spiritual wealth created during the historical development of human society. Language and culture are inseparable. When we learn a language, we must also understand the cultural content it contains. Learning English requires an understanding of the culture of British and American countries, and thus teachers must necessarily teach culture in the English classroom. At present, the software and hardware of Internet technology in our country have been developed in-depth, and the total amount of big data has been in the trend of growth, and China has officially entered the era of big data. This paper takes the reform of teaching mode of British and American culture courses as the main research object and explores how to build a diversified and blended teaching mode based on the existing teaching mode and integrating various online teaching resources under the premise of various technologies in the background of "big data." In order to accelerate the integration and optimization of such course resources, promote the development of theoretical teaching and practical teaching of such courses and improve students' professional ability. [ABSTRACT FROM AUTHOR]

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis. [ABSTRACT FROM AUTHOR]

The competition for talents in the modern society is constantly intensifying. College students not only have good physical and psychological quality but also bear hardships and stand hard work and adapt to the fast-paced working environment in order to adapt to the development of the times. With the advent of the era of big data, advanced technology has been applied to physical exercise and development, providing opportunities and challenges for the development of sports. Therefore, this paper focuses on the impact of expanding training on college sports training through extensive surveys on college students' outward bound training. The results show that data are the key data of analysis, which can be used to analyze college students' physical functions and other indicators scientifically and effectively. Universities should develop appropriate outward bound training according to the characteristics of the students themselves. The project helps to improve the sports performance and psychological and physical quality of college students. We hope to provide theoretical reference for experts and scholars who study the development of college sports. [ABSTRACT FROM AUTHOR]

With the growing development of the era of big data, data acquisition and analysis have become hot spots, and Python-based crawler technology is one of the most widely used tools in data analysis work at present. In this paper, we apply Python crawler key technology to acquire data of movie list and hot movies on Cat's Eye movie network, analyze data based on Python development environment Spyder, use the Numpy system to store and process large data, Chinese Jieba word separation tool to crawl data for word separation text processing, Snownlp library to process text sentiment, and finally by the word cloud map and web dynamic map display information such as viewers' emotional tendency and movie rating statistics, and provide decision support for users' movie viewing. [ABSTRACT FROM AUTHOR]

Ideological and political education (IPE) is aimed at achieving people's free and all-around growth through the use of appropriate methods, and the use of educational methods is integral to the execution of education. Under the influence of big data, it is imperative to strengthen the research on the accuracy of ideology education in colleges and universities (IPECU), which necessitates that ideology educators adopt big data thinking, investigate novel pedagogical approaches, and consistently develop new IPECU conditions. In this paper, a collaborative filtering- (CF-) based algorithm for IPE resource recommendations is presented. Users are given recommendations for educational resources based on their browsing history, browsing patterns, and preferences. The accurate recommendation system can determine users' needs by examining how they use the website in order to suggest more useful information to them. In comparison to the conventional algorithm, the accuracy of the ideological and political education precision recommendation model in this study is 16.75% greater. Teachers can use big data technology to gather students' data information that is dispersed throughout cyberspace, understand students' states in real time, and deliver accurate instructional materials in accordance with students' various states and needs by utilizing the intelligent ideology mode. [ABSTRACT FROM AUTHOR]

Comparative literature and world literature were originally two independent disciplines, but now they are merged into one. In the era of big data, building an efficient information management method is one of the important contents of university reform. Based on the construction of comparative literature and world literature, this paper applies the DSS (Decision Support System) structure based on data warehouse to the construction of comparative literature and world literature, and constructs the DSS structure of comparative literature and world literature. The related knowledge of DM (data mining) is used for research, and the application design and implementation process of DM are introduced into the subject quality evaluation system. The research shows that the experimental results of NB (Naive Bayes) algorithm will not take too long, about 56.08 s, and the compression ratio is 0.897, when the parameters are basically the same. This model can effectively help schools to analyze data and make decisions, and improve the level of information construction in schools. [ABSTRACT FROM AUTHOR]

In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multivariate failure time data are frequently analyzed using the marginal proportional hazards models and the frailty models. When the sample size is extraordinarily large, using either approach could face computational challenges. In this paper, we focus on the marginal model approach and propose a divide‐and‐combine method to analyze large‐scale multivariate failure time data. Our method is motivated by the Myocardial Infarction Data Acquisition System (MIDAS), a New Jersey statewide database that includes 73,725,160 admissions to nonfederal hospitals and emergency rooms (ERs) from 1995 to 2017. We propose to randomly divide the full data into multiple subsets and propose a weighted method to combine these estimators obtained from individual subsets using three weights. Under mild conditions, we show that the combined estimator is asymptotically equivalent to the estimator obtained from the full data as if the data were analyzed all at once. In addition, to screen out risk factors with weak signals, we propose to perform the regularized estimation on the combined estimator using its combined confidence distribution. Theoretical properties, such as consistency, oracle properties, and asymptotic equivalence between the divide‐and‐combine approach and the full data approach are studied. Performance of the proposed method is investigated using simulation studies. Our method is applied to the MIDAS data to identify risk factors related to multivariate cardiovascular‐related health outcomes. [ABSTRACT FROM AUTHOR]

As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction. [ABSTRACT FROM AUTHOR]

In recent years, with the development of enterprises to the Internet, the demand for cloud database is also growing, especially how to capture data quickly and efficiently through the database. In order to improve the data structure at all levels in the process of database analysis engine, this paper realizes the accurate construction and rapid analysis of cloud database based on big data analysis engine technology and deep learning wolf pack greedy algorithm. Through the deep learning strategy, a big data analysis engine system based on the deep learning model is constructed. The functions of deep learning technology, wolf greedy algorithm, and data analysis strategy in the cloud database analysis engine system are analyzed, as well as the functions of the whole analysis engine system. Finally, the accuracy and response speed of the cloud database analysis engine system are tested according to the known clustering data. The results show that compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence. The research application is that the proposed engine system can significantly improve the effect of the analysis engine and greatly improve the retrieval accuracy and analysis efficiency of fixed-point data in the database. [ABSTRACT FROM AUTHOR]

However, the number of college students enrolling in our country has only begun to increase on a large scale in the 21st century. As educational big data gets more and more attention, it has its own distinct characteristics in the field of education and at the same time consolidates the overall trend of big data development. The countermeasure analysis of innovation and entrepreneurship education under the reform of the education supply side has become a subject to be explored in an all-round way. This paper is mainly aimed at the importance of a big data analysis model to college students, explores the current development status of innovation and entrepreneurship education for college students, finds some problems in the application of big data, and improves the existing problems based on the purpose. [ABSTRACT FROM AUTHOR]

The development of a new type of international relations is the advancement and improvement of diplomatic thinking among contemporary nations. It also serves as a crucial yardstick for assessing the future global pattern and the direction of order changes. Proper interaction between major powers can foster the growth of new international relations and has a significant impact on advancing global cooperation and the promotion of human peace. The goal of this essay is to examine how friendly interactions between major powers have affected the development of new international relations. A deep learning network model is presented for this purpose. The deep learning model was used to identify the emotions of the survey results, analyze each person's emotional tendencies, and summarize and compare the data. Relevant questionnaire surveys were conducted using the online questionnaire survey method on individuals in various countries. The survey results in this paper demonstrate that 96.5 percent of Chinese, 89.3 percent of Russians, and 81.6 percent of Americans support friendly relations between major nations. Only a very small percentage of the investigators supported hostile relations, with their support being 1.06 percent, 3.11 percent, and 2.94 percent, respectively. Therefore, creating a win-win partnership between major powers is exactly what the people of all nations are calling for. In contrast to the past, it is no longer hostile and violent. People anticipate that more great powers will coexist peacefully. [ABSTRACT FROM AUTHOR]

It is urgent to effectively monitor the public opinion of the news communication platform. The platform designed in this paper takes microblog public opinion as the research goal, uses MongoDB to build a distributed computing platform for sensitive information of news communication platform, establishes a corpus of sensitive event topics, introduces PageRank algorithm to deal with microblog social relations, obtains the characteristics of sensitive information of news communication platform, and carries out information screening, so as to accurately screen and mine the keywords in high impact information. To ensure the practical application effect of sensitive information mining method of news communication platform based on big data analysis. Finally, the experiment proves that the sensitive information mining method of news communication platform based on big data analysis has the advantages of high timeliness and high accuracy, which fully meets the research requirements. This is fully in line with the requirements of the study. [ABSTRACT FROM AUTHOR]

This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors' knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works. [ABSTRACT FROM AUTHOR]

In the process of promoting school aesthetic education, some schools have some problems, such as insufficient construction of campus aesthetic education environment, lack of aesthetic thinking in various disciplines, and so on. In view of these problems, combined with the concept of the flipped classroom and the characteristics of artificial intelligence task-driven teaching, taking PHP, HTML + CSS + JS, and other development technologies as the main development technologies, and relying on the flipped classroom teaching mode of network learning space, this paper constructs an artificial intelligence core course website as a teaching platform for graduate teaching and undergraduate extended learning. The platform seeks the optimal solution of multiple combination optimization based on a genetic algorithm effectively improves the teaching quality of artificial intelligence courses and students' learning efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a 'neutral' emotion. The other emotions—'bad', 'good', 'terrible', and 'great'—were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that "Twitter for Android" was associated with 35.2% of tweets. It was followed by "Twitter Web App", "Twitter for iPhone", "Twitter for iPad", "TweetDeck", and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021. [ABSTRACT FROM AUTHOR]

B2C online marketing mode is the development trend of future marketing. The key to improve the efficiency of such a mode is to make efficient use of the current large-scale data and mining the corresponding potential value. To control the cost of B2C online marketing mode, this paper analyzes the UJRP model and proposes a hybrid bat difference algorithm (BADE). Experimental results are verified the effectiveness of the proposed method in diversity and cost control. Furthermore, we utilize multimodel fusion strategy (linear weighted fusion) to achieve better performances in B2C online marketing on cost than BADE method. Finally, in the random and diverse online marketing environment, such an improved method can provide the decision-makers of B2C online marketing mode with more flexible choices. [ABSTRACT FROM AUTHOR]

Plant variety rights are essential for agricultural development and food security. This paper mainly starts with the cost-benefit issue of plant variety rights that enterprises are concerned about, and introduces the cost-benefit analysis method into the research and development, application, and protection of plant variety rights. This research takes the new plant variety rights as the research object, clarifies the development status of the world plant variety rights from the perspective of time series development characteristics and spatial distribution pattern, explores the location of the variety rights in the world, and uses methods such as knowledge measurement and data mining, to carry out in-depth research on the application and authorization of plant variety rights, the scope of protection catalogues, the structure of variety rights, and the applicant unit. Finally, the regression model is tested from five aspects: regression model significance test, model goodness-of-fit test, Hosmer goodness-of-fit test, goodness-of-fit test, and overall accuracy test of model prediction. The regression coefficient of plant variety rights cooperation in the research process was 1.908, and the statistical test value was significant at the 5% level (P = 0.022 < 0.05). Using system dynamics to evaluate its risks, it provides new ideas for the risk analysis and prevention of seed industry enterprises' variety rights capitalization operation. [ABSTRACT FROM AUTHOR]

As an important public utility organization and academic research institute, Colleges feature high density people, high social attention, and relatively free atmosphere of interchange ideas, where network opinion crisis are frequent. Therefore, it is critical for colleges to concentrate more on the control and management of the network opinion crisis. In the paper, it compares events of college students' online public opinion crisis with other references. Social online public opinion crisis solutions is use for reference. Aiming at the peculiarities of college students' mental health, it is concluded the research coping strategies based on innovative and comprehensive use of big data technology. We establish a complete emergency protection system to effectively deal with the mental health problems of college students. [ABSTRACT FROM AUTHOR]

The paper deals with a robust autonomous path-following functionality, in which the safe velocity and the motion profile of the vehicle with varying tyre-road contact must be guaranteed. The integration of Linear Parameter-Varying (LPV) control and the results of the machine learning-based analysis on the big data of autonomous vehicles is proposed. The integration is achieved in two steps. First, an estimation method on the adhesion coefficient through decision trees is presented. The result of the estimation is incorporated in the robust LPV control through a scheduling variable. During the control design, the error of the machine-learning algorithm is incorporated. Second, an optimisation method of the longitudinal velocity on a predicted horizon is proposed, which is aided with the machine learning-based reachability set approximation of the steering intervention. The effectiveness of the control strategy is illustrated through CarSim simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a "small" contextual analysis approach to big data and reports our experimental application of this approach in evaluating the effects of social distancing on focused subpopulations in U.S. society. We recognize the common and critical limitations of big data, especially the unrepresentativeness and the unpublished methodology of accessible datasets. Our proposed methodological approach is built upon recent works on data ontology, especially the recognition that big data are essentially remaining digital footprints of human life in need of additional data of contextual factors for valid and meaningful interpretation. It guides the selection and processing of big data to make big data small and structured and thus articulable with traditional social sciences data and usable to conventional social sciences methods. In our experimental case study, we apply our sampling strategy developed from traditional social science data to Google's mobility dataset for our analysis using primarily a Difference In Difference (DID) model. The results of this case study are of timely value to policy evaluation and public decision-making in the pandemic. We call for more proactive methodological innovations that confront the critical limitations of accessible big data especially in times of urgent needs. [ABSTRACT FROM AUTHOR]