Based on cloud computing and statistics theory, this paper proposes a reasonable analysis method for big data of film and television. The method selects Hadoop open source cloud platform as the basis, combines the MapReduce distributed programming model and HDFS distributed file storage system and other key cloud computing technologies. In order to cope with different data processing needs of film and television industry, association analysis, cluster analysis, factor analysis, and K-mean + association (...) class='Hi'>analysis algorithm training model were applied to model, process, and analyze the full data of film and TV series. According to the film type, producer, production region, investment, box office, audience rating, network score, audience group, and other factors, the film and television data in recent years are analyzed and studied. Based on the study of the impact of each attribute of film and television drama on film box office and TV audience rating, it is committed to the prediction of film and television industry and constantly verifies and improves the algorithm model. (shrink)

Chow illustrates the important role played by significance testing in the evaluation of research findings. Statistics and the goals of research should be treated as both interrelated and separate parts of the research evaluation process – a message that will benefit all who read Chow's book. The arguments are especially pertinent to the debate over the relative merits of confidence intervals and significance tests.

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". A (...) kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate "explicativity", which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the "reduction" of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II. (shrink)

Insight is an important and repeated component of most qualitative research studies. Yet insight is often a vague concept that is not well articulated in textbooks and research reports. The late James Loder of Princeton University posited a theologically-based process he termed ‘the transforming moment’ that identifies predictable phases in a wide variety of transformations, including those of a psychological, scientific, and spiritual nature. This process corresponds at many levels with the role of insight in qualitative research. As a result, (...) careful examination of Loder's outline of the transformative process can help clarify what is meant by insight and how it informs research activities. (shrink)

A book aimed at advanced undergraduates and graduates in cognitive science and artificial intelligence, linguistics, applied mathematics and data analysis.

In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the (...) advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system. (shrink)

Data analysis has become most widespread field of research and it has extended in almost every field of study. Considering the recent trends and developments in the field of communication and information technology, there is a scope of combining the monitoring of substation equipment with big data analysis technology. That will result in an improved data analysis ability, information sharing and utilization rate of monitoring data. In the proposed work, the authors have introduced (...) the big data analysis and its corresponding application in the monitoring of substations. Basic concepts and the procedures of the typical data analysis for general problems are also discussed. As a main part of the paper, different types of distributed data analysis techniques have been proposed, in which two relational online analysis, namely Hive and Impala and one H Base multidimensional online analysis are important. These data analysis techniques are proposed considering the analysis efficiency, storage performance from the business development requirements point of view of the substation. The result obtained depicts that the proposed model has an advantage in storage overhead and roll-up performance, when compared with the traditional method, although the data loading speed is approximately 1.7–1.9 times of the traditional model. Some experiments are carried out in order to verify the validity of the model. (shrink)

In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited (...) to, biology, and we label it the microarray paradigm. In this new framework, mathematics provides powerful techniques and general ideas which generate new computational tools. But it is missing any explicit isomorphism between a mathematical structure and the phenomenon under consideration. This methodology used in data analysis suggests the possibility of forecasting and analyzing without a structured and general understanding. This is the perspective we propose to call agnostic science, and we argue that, rather than diminishing or flattening the role of mathematics in science, the lack of isomorphisms with phenomena liberates mathematics, paradoxically making more likely the practical use of some of its most sophisticated ideas. (shrink)

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user (...) interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

Big data has brought about opportunities for landscape architecture, changing the design thinking of layout optimization simulation, expanding the platform for public participation in layout optimization simulation design, reflecting social and humanistic care, and promoting the integration of discipline cooperation and data. At the same time, it also brings about challenges. The proposal of data theory, the acquisition and analysis of data, and the protection of privacy are all issues that we need to face and (...) solve. First, build a layout optimization simulation program under the background of big data. Follow the procedures of conventional layout optimization simulation, add big data analysis technology to the preliminary analysis, layout optimization simulation design, and later evaluation management, introduce new data processing methods, and build a theoretical framework of landscape optimization simulation methods that integrate new and traditional data. Second, studying the evolution of the landscape image layout of the three-dimensional pastoral complex is different: the overall landscape image layout of the three-dimensional pastoral complex shows a trend of fragmentation and heterogeneity. Among them, the arable land is gradually fragmented, the wetland and water area are simplified in form, the woodland and grassland patches are gradually distributed, the fragmentation is reduced by 76.19%, and the connectivity index is gradually increased. From a spatial perspective, the edge area outside the Fourth Ring Road becomes the most obvious area of fragmentation tendency. Finally, by introducing big data into the landscape layout optimization simulation, a layout optimization simulation method based on big data is constructed and used in the layout optimization simulation of the landscape image layout of the three-dimensional pastoral complex to guide the participatory layout optimization simulation. (shrink)

This paper considers the use of secondary data analysis in educational research. It addresses some of the promises and potential pitfalls that influence its use and explores a possible role for the secondary analysis of numeric data in the 'new' political arithmetic tradition of social research. Secondary data analysis is a relatively under-used technique in Education and in the social sciences more widely, and it is an approach that is not without its critics. Here (...) we consider two main objections to the use of secondary data: that it is full of errors and that because of the socially constructed nature of social data, simply reducing it to a numeric form cannot fully encapsulate its complexity. However, secondary data also offers numerous methodological, theoretical and pedagogical benefits. Indeed by treating secondary data analysis with appropriate scepticism and respect for its limitations, by demanding that tacit assumptions about the unreliability of secondary data are applied equally to other research methods, and crucially by combining secondary data analysis with small-scale in-depth work, this paper argues for a return to prominence of secondary data analysis in its own right as well as becoming a central component of the new political arithmetic tradition of social research. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative (...) and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

In the current research, sleep duration, sedentary behaviour, physical activity, and their relationship with several anxiety symptoms among college students were examined. This study was a cross-sectional study, and study respondents were recruited from college students. A total of 1,475 of college students were included for analysis. Sedentary behaviours and physical activity were assessed by the International Physical Activity Questionnaire Short Form, while sleep duration was assessed by the Chinese version Pittsburgh Sleep Quality Index. To assess the anxiety symptoms (...) of study respondents, the Generalised Anxiety Disorder-7 was used. The compositional data was analysed in R to estimate the associations between replacements among physical activities, sedentary behaviours, and sleep and anxiety symptoms. Results indicated a greater impact was imposed on the alleviation of anxiety symptoms by substituting sedentary behaviours with physical activity at moderate to vigorous intensity. In the current research, the significance of moderate to vigorous physical activity should be highlighted in preventing anxiety among Chinese college students. (shrink)

In the age of big data, machine learning models are globally used to execute default risk prediction. Imbalanced datasets and redundant features are two main problems that can reduce the performance of machine learning models. To address these issues, this study conducts an analysis from the viewpoint of different balance ratios as well as the selection order of feature selection. Accordingly, we first use data rebalancing and feature selection to obtain 32 derived datasets with varying ratios of (...) balance and feature combinations for each dataset. Second, we propose a comprehensive metric model based on multimachine learning algorithms to select the best-derived dataset with the optimal balance ratio and feature combination. Finally, the convolutional neural network is trained on the selected derived dataset to evaluate the performance of our approach in terms of type-II error, accuracy, G-mean, and AUC. There are two contributions in this study. First, the optimal balance ratio is found through the classification accuracy, which changes the deficiency of the existing research that samples are imbalanced or the balance ratio is 1 : 1 and ensures the accuracy of the classification model. Second, a comprehensive metric model based on the machine learning algorithm is proposed, which can simultaneously find the best balance ratio and the optimal feature selection. The experimental results show that our method can noticeably improve the performance of CNN, and CNN outperforms the other four commonly used machine learning models in the task of default risk prediction on four benchmark datasets. (shrink)

The spatial time-frequency distribution matrix and associated Rényi entropy is proposed as the basis for a method that may be useful for estimating the significance of nonlocal neocortical interactions in the analysis of scalp EEG data. Implications of nonlocal interactions for source estimation are also considered.

College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large (...) class='Hi'>data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis. (shrink)

In terms of scenic marketing, big data research also plays an important role in the precise marketing of scenic spots. This paper has focused on the big data related to scenic spots as the research object, explores the relationship between various subdivision big data and the number of tourists in scenic spots, and investigates the difference and influence of the consumption behavior of the secondary consumption items in the scenic area, to find the potential of the scenic (...) area’s business growth and to promote the continuous and stable growth of the scenic area’s sales and tourism economy. Using the relevant theories and analysis methods, such as consumer behavior, big data, and tourism consumer behavior, the content mainly focuses on the establishment of the analysis model of the number of tourists in the scenic spot, the data collection, the estimation of the model parameters, the various types of big data, the calculation of the contribution rate of the data to the number of tourists in the scenic spot, and the difference analysis of the secondary consumption items of different types of tourists in the scenic spot. Results show that a multi-objective analysis model is established based on the relevant econometric theories, and an optimization plan is proposed after the multicollinearity diagnosis of the model; to establish a data envelopment analysis model of the difference and influence of different types of tourists’ consumption behavior in scenic spots and study the consumption behavior characteristics of different types of tourists when they purchase secondary consumption items in scenic spots; the econometric model is used to analyze the big data, adjust the linear relationship of some variables, then adopt the method of gradually adding variables combined with the consumer theory, and finally determine the number of daily tourists as the explained variable, the number of internet protocol, Baidu index, and the virtual value of the weekend, dummy variables for variables, bounce rate, and air pollution as explanatory variables. (shrink)

We conducted an integrative data analysis to examine the hedonic character of nostalgia. We combined positive and negative affect measures from 41 experiments manipulating nostalgia. Overall, nostalgia inductions increased positive and ambivalent affect, but did not significantly alter negative affect. The magnitude of nostalgia’s effects varied markedly across different experimental inductions of the emotion. The hedonic character of nostalgia, then, depends on how the emotion is elicited and the benchmark to which it is compared. We discuss implications for (...) theory and research on nostalgia and emotions in general. (shrink)

In this paper, we discuss about what and how of data science and data analysis: i.e., the approach and the mechanism that analysts employ while working with data. A Philosophical approach to analysis of data and data science has been undertaken those peeks into the conceptual world of aspects of the epistemology of data science. The paper also highlights the role played by analysts, tools, and specialized techniques that analysts employ in (...) class='Hi'>data science to derive insights from data. The discussion demonstrates the complexities associated with data science, and by what mechanism and how organizations and businesses draw insights that constitute the real value of data, and that which lay hidden deep within datasets constituting as a form of resource and asset for the organizations. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense (...) of data produced in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

Does direct democracy enhance political efficacy? This article examines the effect of direct democracy on political efficacy. Normative theorists have suggested that direct democracy has educative effects on citizens, such as promoting political efficacy. While a number of studies have examined the corresponding hypothesis, their empirical findings are not clear-cut. This study attributes the inconsistent results to two problems of the existing studies: the employment of cross-sectional data and the heterogeneity of popular vote issues. This study closes this gap (...) by examining the effect of direct democracy in local politics on political efficacy in a more systematic and controlled way. More concretely, it utilizes the Japanese case: In the first decade of this century, more than 400 Japanese municipalities held a popular vote for the first time because the Japanese national government promoted municipal merger. Therefore, the Japanese case provides multiple popular votes on comparable substantive topics that can be conceived as an homogeneous treatment. By applying multilevel modeling to panel survey data, this study demonstrates the causal effect that the popular vote increases the level of internal political efficacy. (shrink)

This research is an exploration into the realms of epistemology and philosophy of data science and data analysis. It introduces the conceptual aspects of data analysis in data science research. We describe the philosophical implications of data analysis and the role that epistemology and the expertise of analysts play in analysing data, big or small. The process of data analysis is a complex job as it involves advanced statistical tools (...) that play a crucial role in deciphering what the data tells. It also perceives the value of data analysis as a knowledge resource and wealth for organizations that are in the business of data analytics. (shrink)

The target article is important not only for black-box studies, but also for those interested in tracing cognitive processing and/or subjective experience. I provide two examples taken from my own research. I then proceed to discuss how best to analyze data from the n = 1 study, which has a factorial design.

Teachers’ teaching psychological behavior and classroom development are the current research hotspots in the field of educational psychology. How to realize the data analysis of teachers’ teaching psychological behavior and classroom development is a problem that researchers urgently need to solve. Based on the theory of data correlation analysis, this paper uses modern Internet technology and big data analysis teacher teaching system to quantitatively and qualitatively analyze the potential of students, and build a corresponding (...) model. Through rule correlation technology, the article studies various internal correlations between teachers’ teaching psychological behavior, extracts valuable information from various daily data of students through big data analysis technology, and the WEB teacher’s teaching psychological behavior analysis system based on B/S structure solves the problem that the traditional model cannot measure. In the simulation process, the system is implemented by MVC three-tier architecture, the database uses MYSQL 5.0, the prediction questionnaire is formulated on the basis of the literature method and interviews, and the scale is compiled and tested after repeated revisions. Project analysis and factor analysis are performed on the data obtained from the table test to construct and screen indicators. The experimental results show that the teacher’s classroom teaching behavior index system adopted by the system is practical and feasible, including three first-level indicators, 10 s-level indicators, and 21 third-level indicators. The system has 87.1% completeness, which effectively improves teachers’ teaching psychology. (shrink)

This article presents our experience as a multidisciplinary team systematizing and analyzing the transcripts from a large-scale (1.775 conversations) series of conversations about Chile’s future. This project called “Tenemos Que Hablar de Chile” [We have to talk about Chile] gathered more than 8000 people from all municipalities, achieving gender, age, and educational parity. In this sense, this article takes an experiential approach to describe how certain interdisciplinary methodological decisions were made. We sought to apply analytical variables derived from social science (...) theories and operationalize them through modern linguistics to guide a more theoretically informed natural language processing. The analysis was divided into three stages: (1) a descriptive analysis adapting descriptions of computational grounded theory, (2) a futurization analysis operationalizing concepts from futures studies, and (3) an argumentative analysis operationalizing concepts from argumentation theory. Overall, our methodological experimentation shed light on potential learnings for integrating a multidisciplinary perspective on NLP analysis with sensitive social content. Firstly, we developed a strategy for translation of knowledge based on the construction of what we called "analytical categories” in which a normative expectation or descriptive dimension was identified in the body of literature, operationalized through linguistics, and programmed in Python or R. Ultimately, we seek to reflect on the importance interdisciplinarity not only as means to find new analysis ideas but rather, to incorporate the critical, political and epistemological points of view to understand analysis as complex socio-technical processes. (shrink)

This is the third of four papers to be published in Research Ethics Review in 2009, that address methodological issues of relevance to research ethics committees. It focuses on three issues: the representativeness of study participants, the size of the study and data analysis. Differences between best practices in qualitative and quantitative research are highlighted. The paper argues that, while lack of representativeness may not be unethical, the ethical implications of unnecessary restrictions on eligibility should be considered by (...) committees. Studies that are either too small or too big can pose problems. Research teams need to have the necessary competence to enable an appropriate analysis focused on the main objectives and interpreted in the context of the limitations of the study design. (shrink)

Fyodor Mikhailovich Dostoevsky is one of the classic Russian writers who has been well known by Chinese readers. At the beginning of the twentieth century, Dostoevsky came to China along with the dissemination of Russian literature, and his works participated in the process of the development of Chinese literature in a unique way. After more than a century of development, Dostoevsky studies in China have accumulated more than two thousand pieces of literature, which record the tireless interpretations and explorations of (...) Chinese scholars into the thought and art of Dostoevsky’s works. With the help of data analysis and bibliometric methods, this paper will analyze the research and dissemination of Dostoevsky in China over the past one hundred years, and present a visualization of the trajectory of the development of Dostoevsky studies in China, the research hotspots, and the studies of the representative scholars, to outline the course and appearance, as well as characteristics and routines, of the development of Dostoevsky studies of in China. (shrink)

Colleges and universities are in an important position to train builders and successors of the socialist cause whilst promoting quality education. Mental health education is an important foundation and condition for comprehensively improving students' overall quality. This research explores adjustment methods for college students' mental health based on virtual reality under the background of positive psychology. It discusses the importance of system requirements analysis in the software development process, analyzes the system's functional requirements, safety requirements, and software and hardware (...) requirements, and uses the Apriori algorithm to explore the influencing factors of college students' mental health. Based on the system engineering method and using the data mining clustering method undertake detailed analysis and research on the mental health of college students, it then designs an anomaly mining algorithm based on clustering to quickly find anomalous data health problems. The interface design of the system is concise and the operation is simple. Users can conveniently input, query, and count information according to the various controls on the interface, which fully embodies human-oriented characteristics. Exploration of the characteristics of students' frequent Internet access ensures the efficiency, accuracy, and comprehensiveness of the evaluation and consultation work, facilitating psychological counseling for teachers and students, and saving paper. By establishing a data mining model, mining the database, and learning about different student groups and their respective characteristics, we discuss our research on student psychology and summarize the mental health status and gender, adaptation and anxiety, introversion, emotionality, and calmness of college students. We also consider the relationship between sex, negative, and courage. Using positive psychology theory, we examine the positive experiences of students and interconnected qualities, to build a mental health practice system. In the experiment, the happiness index evaluation of the virtual reality treatment system group was significant, P = 0.002 < 0.05. Mental health education plays an important role in cultivating the healthy psychology of college students, developing their psychological potential, enhancing their adaptability, and improving their personality. This analysis based on actual data provides a reliable basis for psychological educators to improve the efficiency and effectiveness of school psychological counseling and to facilitate schools in establishing new methods of early prevention and intervention for psychological disorders, enabling institutions to create a healthy atmosphere for college students. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)