Big Data Environment Research Articles

An early warning method for abnormal behavior of college students based on multimodal fusion and improved decision tree

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

A novel evaluation framework for recommender systems in big data environments

A novel evaluation framework for recommender systems in big data environments

Analysis of non-linear effects of international financial systemic financial risk on macroeconomic impact

Abstract This paper first analyzes the international financial systemic risk transmission mechanism in the big data environment, constructs an international financial systemic risk index system, and measures the index based on principal component analysis. Then, the existing international financial and macroeconomic literature is sorted out, a macroeconomic resilience index system is constructed, and the index is measured using the entropy value method. Finally, MS-VAR and TVP-VAR models are constructed based on the VAR model to analyze the non-linear effects of international financial systemic financial risks on macroeconomic impacts. The results show that a shock of 1 unit of CFSI causes a positive response of MR of about 0.23 in the long run, and CFSI all produce a negative shock to MI until this negative shock reaches a maximum of -0.023 in the 5th period.

A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

An Integration Model on Brainstorming and Extenics for Intelligent Innovation in Big Data Environment

Brainstorming is a widely used problem-solving method that generates a large number of innovative ideas by guiding and stimulating intuitive and divergent thinking. However, in practice, the method is limited by the human brain's capacity or special capabilities, especially by the experience and knowledge they possess. How does our brain create ideas like storming? Based on the new discipline of Extenics, the authors propose a new model that explores the process of how ideas are created in our brain, with the goal of helping people think multi-dimensionally and getting more ideas. With the support of information technology and artificial intelligence, we can systematically collect more information and knowledge than ever before to form a basic-element information base and build human-computer interaction models, to make up for the lack of information and knowledge in the human brain. In addition, the authors provide a methodology to help people think positively in a multidimensional way based on the guidance of Extenics in the brainstorming process.

User Electricity Utilization: Factor Recognition and Quantity Projection in the Big Data Environment

User Electricity Utilization: Factor Recognition and Quantity Projection in the Big Data Environment

Research on innovation and development of university instructional administration informatization in IoT and big data environment

Research on innovation and development of university instructional administration informatization in IoT and big data environment

Financial Risk Prediction and Management of Enterprises Based on Financial Big Data

With the financial globalization, the data management mode of the financial industry has shifted from data sets to multi-service integration, and the construction of data centers in the financial industry has also been rapidly launched around the needs of business development. Therefore, financial enterprises need a lot of data analysis as the basis for decision-making. It is necessary to conduct more in-depth exploration and research on how to effectively integrate BD(big data) with diverse structures in the financial field, realize efficient data collection, and improve data processing efficiency. In the BD environment, how to use big financial BD technology to prevent financial risks in financial audit has become a widespread concern. This paper expounds the era of BD and its characteristics, and puts forward how to build a data platform for enterprise financial risk prediction and how to collect, manage and analyze data under the background of BD, in order to put forward feasible suggestions for financial audit on how to prevent financial risks under the background of BD.

Retracted: Blockchain-Based Data Audit Mechanism for Integrity over Big Data Environments

Retracted: Blockchain-Based Data Audit Mechanism for Integrity over Big Data Environments

Fusion of XLNet and BiLSTM-TextCNN for Weibo Sentiment Analysis in Spark Big Data Environment

This article proposes a Weibo sentiment analysis method to improve traditional algorithms' analysis efficiency and accuracy. The proposed algorithm uses deep learning in the Spark big data environment. First, the input data are converted into dynamic word vector representations using the Chinese version of the XLNet model. Then, dual-channel feature extraction is performed on the data using TextCNN and BiLSTM. The proposed algorithm uses an attention mechanism to allocate computing resources efficiently and realizes feature fusion and data classification. Comparative experiments are conducted on two public datasets under identical experimental conditions. In the NLPCC2014 and NLPCC2015 datasets, the proposed model improves the precision and F1 metrics by at least 4.26% and 2.64%, respectively. In the weibo_senti_100k dataset, the proposed model improves the precision and F1 metrics by at least 4.66% and 2.69%, respectively. The results indicate that the proposed method has better sentiment analysis and prediction abilities than existing methods.

Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

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

Retracted: Emotion Analysis Based on Neural Network under the Big Data Environment.

[This retracts the article DOI: 10.1155/2022/7123079.].

Analysis for Online Music Education Under Internet and Big Data Environment

Online music teaching has brought great challenges to traditional music education. This paper investigates the paradigm of online music education and uses neural networks to evaluate its teaching quality based on the internet and big data. The main work is as follows: 1) The research progress of online music education at home and abroad is introduced. 2) This paper introduces the basic principle and algorithm steps of BPNN and establishes the evaluation index system of online music education quality. The experimental results show that the model proposed in this paper has high accuracy in evaluating online music teaching standards.

Support Vector Machine based Automatic Classification Method for IoT big Data Features

<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.</p> <p> </p>

Machine learning approaches to identify the data types in big data environment: An overview

The digital world has access to a multitude of data in the Fourth Industrial Revolution (4IR, also known as Industry 4.0) era, including Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. These data are produced from various sources. Knowledge of artificial intelligence (AI), specifically machine learning (ML), is essential to intelligently analyse these data and create the associated smart and automated applications. Many different kinds of machine learning algorithms exist in the field, including supervised, unsupervised, semi-supervised, and reinforcement learning. Additionally, deep learning, which belongs to a larger family of machine learning techniques, has the ability to effectively examine a lot of data. In this article, we provide a thorough overview of these machine learning techniques that may be used to improve the functionality and intelligence of an application. Determining the fundamentals of various machine learning approaches and how they can be used in identifying the data types and classify them to be placed in bigdata nodes for the effective storage and retrieval, is thus the core contribution of this work. Based on our findings, we also discuss the difficulties and potential possibilities for future research.

Retracted: Analysis on the Construction of Marxist Ideological Discourse Power in the Environment of Big Data.

[This retracts the article DOI: 10.1155/2022/5961735.].

Retracted: The Influence of Cultural Communication on the Psychological Health of University Students in the Environment of Big Data.

[This retracts the article DOI: 10.1155/2022/3037205.].

Retracted: Research on Organization Design of College Chinese Teaching under Big Data Environment.

[This retracts the article DOI: 10.1155/2022/2774072.].

Based on the Big Data Audit Risk Model Audit Process Research

With the gradual application of big data technology in enterprises, the way and connotation of audit have changed greatly. This paper is based on the impact of big data technology on auditing, Break down the audit risk into four risk elements, including inherent risk, control risk, big data technology risk and inspection risk, Building an audit risk model adapted to the big data environment, Summarize the new connotation of the four risk elements in the new model; Then, analyze and explore the impact of big data audit risk model on the audit implementation process from four stages: audit objective, audit risk identification and evaluation, audit plan and audit risk response, Combined with the audit practice, the big data audit process is sorted out again, Provide thinking guidance and solutions for big data audit.

A 5G-Enabled and Self-Powered Sensor Data Management Scheme for the Smart Medical Platform System

With the rapid development of information technology and the Internet, the advent of 5G era, combined with the self-powered sensors technology of the Internet of things, can meet the needs of the rapid popularization of intelligent devices and intelligent terminals. With the national economic growth and the continuous development of high-tech industry, the people’s demand for medical services and medical construction is gradually increasing. In order to improve the medical speed, treatment efficiency and treatment success rate of the medical platform, the intelligent medical system has become a system platform widely used in major hospitals. Based on 5G development and self-powered sensors development, this paper studies the construction of intelligent medical platform and system data management. We analyze the development of 5G and the application status of self-powered sensors in the medical field, and briefly describe the defects and problems existing in the intelligent medical system. Use 5G network propagation speed to optimize and improve the intelligent medical system, manage the system data information in the big data environment, and build an information integration platform. In the medical information data collection, the intelligent self-powered sensors technology is used to expand the retrieval range and improve the accuracy and reliability of the system. A set of intelligent medical data retrieval system is designed by using artificial intelligence algorithm and 5G communication technology to realize and optimize the function of patient service on the medical platform. Finally, the data management of medical platform is studied under the data security mechanism. Combined with advanced self-powered sensors technology, the purpose of dynamically monitoring patients’ heart rate and body test data is realized, and the information is transmitted to the medical management platform to help doctors and nurses analyze and diagnose. The results show that 5G technology and intelligent self-powered sensors technology play a good role in promoting the establishment of intelligent medical platform, and improve the overall accuracy and reliability of medical system.