Big Data Analysis Methods Research Articles

Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge

Large amplitude vortex-induced vibration (VIV) of long suspenders, caused by vortex-shedding and wake flow, will decrease the service life of high-strength wires and its anchorage systems, hence listed as an essential monitoring indicator of the structural health monitoring system in the suspension bridges. Traditionally, a static vibration amplitude limitation is usually predefined and used to identify VIV from continuously monitored acceleration data of bridge suspenders. Due to the complexity of the starting vibration of the suspenders, it may not be able to flexibly adapt to the suspenders VIV under different conditions. In addition, transient but significant vibration events may not be captured since it relies only on static vibration amplitude limits without considering the temporal information of vibration. To address the above-mentioned issues, a novel framework to autonomously identify the suspenders VIV is proposed using the multimodal fusion techniques with deep neural networks. The main contributions of the methodology are: i) by calculating the wind field and vibration characteristic, two vibration response parameters are identified as indexes for the identification of suspenders VIV based on the significant difference method of big data analysis; ii) Two different modal information of time history images and power spectral density (PSD) sequences are used as inputs to the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to extract the suspenders vibration response features from the time and frequency domain perspectives, respectively; and iii) The multimodal information is concentrated and enhanced by the multi-head attention (MHA) mechanism to achieve automated identification of the suspenders VIV. The proposed framework is validated with data collected from a full-scale long-span suspension bridge in comparison with the base model. The results show that the proposed framework can efficiently identify the full-cycle development process of the suspenders VIV. This study provides a convenient strategy for suspenders VIV identification, which can be used for bridge management and vibration mitigation device design.

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--a case study of Bohai a oilfield

The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai A Oilfield, this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity. Firstly, by compiling the abundant exploration and development sandbodies data in the study area, typical sandbodies with reliable connectivity were selected. Then, sensitive seismic attribute were extracted to obtain training samples. Finally, based on the Deep Forest algorithm, mapping model between attribute combinations and sandbody connectivity was established through machine learning. This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield. Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis, this method can combine the sandbody characteristics of the study area in the process of machine learning, and jointly judge connectivity by combining multiple seismic attributes. The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies. Applied to the Bohai A Oilfield, it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization. This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions.

A bigdata analytics method for social media behavioral analysis

A bigdata analytics method for social media behavioral analysis

Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment

Credit risk assessment (CRA) plays an important role in credit decision-making process of financial institutions. Today, developing big data analysis and machine learning methods have marked a new era in credit risk estimation. In recent years, using machine learning methods in credit risk estimation has emerged as an alternative method for financial institutions. The past demographic and financial data of the person whose CRA will be performed is important for creating an automatic artificial intelligence credit score prediction model based on machine learning. It is also necessary to use features correctly to create accurate machine learning models. This article aims to investigate the effects of dimensionality reduction and data splitting steps on the performance of classification algorithms widely used in the literature. In our study, dimensionality reduction was performed using Principal Component Analysis (PCA). Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms were chosen for classification. Percentage splitting (PER, 66–34%) and k-fold (k = 10) cross-validation techniques were used when dividing the data set into training and test data. The results obtained were evaluated with accuracy, recall, F1 score, precision, and AUC metrics. German data set was used in this study. The effect of data splitting and dimension reduction on the classification of CRA systems was examined. The highest ACC in PER and CV data splitting was obtained with the RF algorithm. Using data splitting methods and PCA, the highest accuracy was observed with RF and the highest AUC with NB, with 13 PCs in which 80% of the variance was obtained. As a result, the data set consisting of a total of 20 features, expressed by 13 PCs, achieved similar or higher success than the results obtained from the original data set.

Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview

Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities.

How Does Digital Technology Inspire Global Fashion Design Trends? Big Data Analysis on Design Elements

Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of digital technology on fashion design elements (e.g., color, print and graphic, textiles, and style and details) was evident through various keywords related to digital technology, humans, and nature, and the relationships between these keywords were confirmed. The analysis of the implicit meanings and directions of the derived keywords resulted in four clusters: (1) human- and nature-oriented design in the digital world as a new reality; (2) new textiles reflecting digital technology; (3) sustainable design technology; and (4) new utility fashion in the digital space. This study proposed a new design research methodology in which big data were incorporated and could be applied to educational curricula, allowing students to derive practical design elements through big data analysis and serving as a guide for planning and developing technology-inspired designs. Practically, it provided specific information on the direction of digital-technology-inspired fashion design trends, which could assist fashion designers and aspiring entrepreneurs in planning.

Concept graph construction and applied research of agricultural remote sensing

ABSTRACT Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application.

Monitoring and management of green information in water ecological environment based on sensors and big data

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

A Review of Research Progress on the Impact of Urban Street Environments on Physical Activity: A Comparison between China and Developed Countries

Creating healthy street environments to encourage physical activity is an effective strategy against non-communicable diseases exacerbated by rapid urbanization globally. Developing countries face more significant health challenges than developed ones. However, existing research predominantly focuses on the perspective of developed countries. To address the health challenges in developing nations, studies should not only draw on the findings from developed countries but also clearly define unique research processes and pathways. Consequently, this study conducts a comparative analysis between China, representing developing countries, and developed nations, using databases like China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) and tools such as Citespace, Bicomb, and Statistical Package for the Social Sciences (SPSS) to explore research hotspots, developmental trajectories, thematic categories, and trends. The findings reveal a shift in developed countries from macro-material to micro-environmental elements under multidisciplinary scrutiny, while future topics may include street space evaluations and psychological healing. In China, research has been dominated by different disciplines at various stages, starting with medical attention to chronic disease prevention, which then shifted to traffic engineering’s focus on constructing green travel environments, and finally expanded to disciplines like landscape architecture examining the impact of street environment elements on pedestrian behavioural perceptions. Future themes will focus on promoting elderly health and urban health transport systems. Generally, research in developed countries exhibits a “bottom-up” approach, with practical issues at a “post-evaluation” stage, primarily based on the “socio-ecological model” and emphasizing multidisciplinary collaboration. Chinese research shows a “top-down” characteristic, driven by national policies and at a “pre-planning” stage, integrating theories such as Maslow’s hierarchy of needs and attention restoration theory, with relatively loose disciplinary cooperation. Overall, research is shifting from macro to human-centric scales and is progressively utilizing multi-source and multi-scale big data analysis methods. Based on this, future research and development recommendations are proposed for developing countries, with China as a representative example.

Образовательная миграция из арктических регионов России, не имеющих самостоятельных вузов

One of the main reasons for young people to change their place of residence is the desire to get higher education. In the Arctic zone, where there is a lack of or insufficient level of development of the network of higher education organizations, young people are forced to leave for other regions. The relevance of the study is also conditioned by the fact that educational migration is often irrevocable, which strengthens negative migration processes. The study of educational migration with the help of traditional statistical sources is possible at the regional level, but does not reveal the spatial characteristics of migra-tion processes at the level of municipalities. Therefore, the purpose of this study is to approbate the methodology of studying migration from municipalities on the example of regions where there are no independent higher education institutions. The object of the study is the Arctic regions: Nenets, Chukotka, Yamalo-Nemets Autonomous okrugs. The scientific novelty of the study is determined by the use of new data sources and the application of the method of big data analysis to study migration processes at the municipal level. The database for the study was formed on the basis of the results of uploading data from the digital footprint of users of the social network “VKontakte”. The study regions included 15,186 users, the vast majority of whom indicated receiving higher education in other constituent entities of the Russian Federation. Based on the obtained data, the trajectories of educational migration at the municipal level were identified, the coefficients of concentration and uniformity of migration flow were calculated, and a typology of municipalities in the Arctic regions of Russia was carried out. The practical significance of the study lies in the possibility of using the results obtained to adjust migration policy in the Arctic regions, including at the municipal level.

텍스트 마이닝을 활용한 중학교 음악과 교육과정 원문 분석

The purpose of this study is to examine the characteristic changes in music curriculum over time using scientific analysis methods. To this end, the original texts of the Middle School music curriculum from the revised 2007 to the revised 2022 were analyzed using the TF-IDF technique of Text Mining, a big data analysis method based on artificial intelligence, to extract the core concepts of each curriculum and to examine the trends. As a result of the analysis, the following conclusions were drawn. First, Korean music curriculum have been in a state of flux, reflecting various factors such as global trends in music education, education policies, and needs of the educational community. Second, the paradigm of ‘curriculum reconceptualization’ has been increasingly accepted over time. Third, text mining can be used to analyze and visualize curricula in an objective way, making it easy to intuitively understand curriculum changes over time. It can be a useful tool for curriculum-related research.

A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis

A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis

Methods of machine learning and big data analysis to establish the molecular mechanisms of the effects of racetams on the metabolism of adipose tissue

Racetams exhibit not only nootropic effects, but also affect the metabolism of fats and carbohydrates. Experimental studies have indicated the possibility of using phenylpiracetam in the treatment of obesity. At the same time, the molecular mechanisms of this pharmacological effect of phenylpiracetam are practically unknown. The paper presents the results of a new artificial intelligence (AI) method for comparative chemoreactome analysis of fonturacetam, piracetam, aniracetam, pramiracetam and levetiracetam. Another AI method, chemoneurocytological analysis, made it possible to compare the neuroprotective effects of molecules on neurons in culture. Despite the similarity of the proteomic interaction profiles of the studied molecules, differential analysis made it possible to establish the molecular mechanisms of the effect of phenylpiracetam on weight loss. Phenylpiracetam can activate β3-adrenoceptors, adenosine, glucagon-like peptide, sphingosine phosphate, and peroxisome proliferator receptors (PPARG); specifically inhibit cannabinoid, opioid, histamine, glutamate, nociceptin, neuropeptide Y and orexin receptors, which is important for normalizing appetite and improving the metabolism of adipose tissue. The synthetic AI method - pharmacoinformatic analysis indicated the advisability of taking phenylpiracetam together with vitamins C, D and group B.

The Development Trend of Ubiquitous Learning in Age of New Media

The development of new media, which has changed people’s lives significantly in China, has promoted ubiquitous learning. The research attempts to explore the possibilities of developing ubiquitous learning steadily in the new media age and find out the developmental potentials and further hindrances, to realize the integration of ubiquitous learning and new media properly. Meanwhile, this study seeks to find an effective way to integrate fragmented learning resources in the new media environment and personalize people’s services. Based on the Modern Pragmatic Theory and Comprehensive Ability Practice and Integration Theory, the realistic interview, big data information collection, and analytical and comparative methods have been used to investigate 100 people and 30 domain experts to figure out the existing advantages, disadvantages, and the further developmental potentials and hindrances.

МЕТОД ПОШУКУ ЗАЛЕЖНОСТЕЙ У ВЕЛИКИХ НАБОРАХ ДАНИХ ІЗ ПРОПУСКАМИ НА ПРИКЛАДІ ДАНИХ ПРО ПОШИРЕННЯ COVID

In the article, a method of big data analysis with omissions is developed on the example of the construction of advisory rules regarding adequate state policy to reduce the spread of new Covid-19 cases. Association rules and rule generation in Big Data have a number of challenges, the main one being the presence of large numbers of vectors and multivalued datasets. That is why in the paper the system of these rules is based on a new ensemble of machine learning techniques such as associative rules, regression tree and clustering. This study used pooled data from the Government's COVID-19 Response Tracker and ECDC's Covid-19. Clustering was performed using the k-means method. Gap statistics allow finding an appropriate number of clusters, and in the case study three clusters were selected. The clusters differ in the recommendations and actions of the correspondent governments. Thus, the first countries of the cluster chose to close schools and control international travel as the main recommendations; countries in the second cluster recommended staying at home, while the main recommendations from the governments of countries in the third cluster were to stay at home and cancel public events. The same country can be attached to different clusters at different time intervals. Therefore, the clustering by countries will not be so unambiguous. That is why the time series for the detached country can be interesting and will be the subject of further study. In addition, the impact of such clustering on the spread of COVID-19, the position and duration of the peak, and the mortality rate will also be the subject of our further investigation. A regression decision-making tree was built, a set of rules was obtained from the decision tree and applied rules for generating associative dependencies. The resulting dependencies can be used for strategic planning in the healthcare system.

Purchasing Patterns Analysis in E-commerce: A Big Data-driven Approach and Methodological

This research aims to analyze purchasing patterns in e-commerce using a Big Data-based approach and data analysis methods. Leveraging advanced technology and analytical methodology, this research explores consumer behavior, market trends, and factors influencing purchasing decisions in e-commerce. Through collecting transaction data from leading e-commerce platforms and applying rigorous data analysis techniques, this research identifies significant patterns and provides valuable insights for e-commerce companies. This research shows that big data has great potential in understanding consumer behavior and market trends in e-commerce. In contrast, sophisticated data analysis methods are essential in interpreting the large and complex data generated by e-commerce. The findings of this research significantly contribute to the development of the e-commerce industry by helping companies improve their marketing strategies and business decision-making. However, this research also needs help, as it requires data analysis skills and privacy issues. To overcome these challenges, collaboration between researchers, e-commerce companies, and governments is needed to develop the necessary data analysis expertise and ensure that consumer data is managed securely and ethically. Thus, this research provides a more holistic view of consumer behavior and market dynamics in e-commerce, assisting companies and policymakers in addressing challenges and opportunities in an ever-evolving landscape.

Satellite wave 2D spectrum partition based on the PI-vit-GAN(physically-informed ViT-GAN) method

The abundant spectral data provided by satellite technology are crucial for interpreting the complex marine environment, and the effective and accurate analysis of these data is particularly important for coastal engineering. In this regard, this study proposes a Physically Informed ViT-GAN (PI-ViT-GAN) automatic partitioning method, based on CFOSAT satellite wave spectrum data. Specifically, the model consists of a generator and discriminator. The generator utilizes a contrastive learning strategy as pretraining and through the self-attention mechanism of the ViT model, it focuses on key parts of the spectrum to extract wave group features and wave element parameters. Partitioning-head joint training realizes the output of wave group partition element indices. Subsequently, the discriminator uses the wave group features and a parametric model for spectrum reconstruction and computes the error with the original observed spectrum to evaluate the partition and reconstruction effects. Additionally, this model incorporates two physically corrected functions, wave system classification loss and merging loss, based on the wave age criterion, thereby guiding the training process, and enhancing model efficiency. The results indicate that the reconstructed theoretical spectrum, obtained through the utilization of this method, aligns well with the original sea wave spectrum, demonstrating a precision superior to the spectral partitioning product of CFOSAT's own SWIM. Combining the robust learning capability of the transformer and the regularization of physical prior knowledge, this model can achieve precise, low-cost automated analysis of satellite wave spectra, providing a new scalable method for big data analysis in marine and coastal engineering.

Внедрение методов анализа больших данных (Big Data) для управления рисками в нефтегазовом комплексе

в данной научной статье рассматривается внедрение инновационных методов анализа больших данных (Big Data) для эффективного управления рисками в нефтегазовом комплексе. Актуальность данной темы обусловлена растущей необходимостью повышения эффективности и безопасности операций в нефтегазовой отрасли, которая сталкивается с множеством рисков, таких как аварии, простои оборудования, колебания цен на энергоносители и геополитическая нестабильность. Целью исследования является разработка комплексного подхода к управлению рисками на основе анализа больших данных, который позволит нефтегазовым компаниям принимать более обоснованные решения и минимизировать потенциальные убытки. В ходе исследования были использованы различные методы, включая сбор и обработку больших объемов структурированных и неструктурированных данных из различных источников, таких как датчики на месторождениях и НПЗ, финансовые отчеты, социальные сети и новостные ленты. Для анализа данных применялись передовые технологии, такие как машинное обучение, глубокое обучение, обработка естественного языка и предиктивная аналитика. Были разработаны специализированные алгоритмы и модели для выявления закономерностей, аномалий и потенциальных угроз в режиме реального времени. Результаты исследования показали, что внедрение методов анализа больших данных позволяет существенно повысить эффективность управления рисками в нефтегазовом комплексе. Например, с помощью предиктивной аналитики удалось снизить количество незапланированных простоев оборудования на 23% и сократить затраты на техническое обслуживание на 18%. Анализ данных о ценах на нефть и газ в сочетании с геополитической информацией позволил оптимизировать торговые стратегии и увеличить прибыль на 12%. Кроме того, выявление потенциальных угроз безопасности, таких как утечки и аварии, с помощью анализа данных с датчиков и камер наблюдения, позволило предотвратить серьезные инциденты и снизить экологические риски. Полученные результаты демонстрируют высокую эффективность применения методов анализа больших данных для управления рисками в нефтегазовом комплексе. Внедрение этих методов позволяет не только минимизировать потенциальные убытки, но и повысить общую эффективность и безопасность операций. Дальнейшие исследования в этой области могут быть направлены на разработку более совершенных алгоритмов и моделей, а также на интеграцию методов анализа больших данных с другими инновационными технологиями, такими как Интернет вещей и блокчейн. this scientific article discusses the introduction of innovative methods of big data analysis (Big Data) for effective risk management in the oil and gas industry. The relevance of this topic is due to the growing need to improve the efficiency and safety of operations in the oil and gas industry, which faces many risks such as accidents, equipment downtime, fluctuations in energy prices and geopolitical instability. The aim of the study is to develop an integrated approach to risk management based on big data analysis, which will allow oil and gas companies to make more informed decisions and minimize potential losses. Various methods were used during the research, including the collection and processing of large amounts of structured and unstructured data from various sources, such as sensors at fields and refineries, financial reports, social networks and news feeds. Advanced technologies such as machine learning, deep learning, natural language processing and predictive analytics were used to analyze the data. Specialized algorithms and models have been developed to identify patterns, anomalies and potential threats in real time. The results of the study showed that the introduction of big data analysis methods can significantly improve the effectiveness of risk management in the oil and gas industry. For example, predictive analytics has reduced the number of unplanned equipment downtime by 23% and reduced maintenance costs by 18%. The analysis of oil and gas price data in combination with geopolitical information allowed us to optimize trading strategies and increase profits by 12%. In addition, the identification of potential security threats, such as leaks and accidents, through the analysis of data from sensors and surveillance cameras, has prevented serious incidents and reduced environmental risks. The results obtained demonstrate the high efficiency of using big data analysis methods for risk management in the oil and gas industry. The implementation of these methods allows not only to minimize potential losses, but also to increase the overall efficiency and safety of operations. Further research in this area may be aimed at developing more advanced algorithms and models, as well as integrating big data analysis methods with other innovative technologies such as the Internet of Things and blockchain.

텍스트마이닝을 활용한 조직핵심인재 역량에 대한 직장인들의 인식

The purpose of this study is to investigate and analyze the competencies of organizational core talents as perceived by employees. To achieve this, interviews and survey questions were created through a review of prior research, using both structured and unstructured questions. Subsequently, data from 134 employees, excluding insincere respondents, were collected through offline interviews and online surveys. The analysis utilized text mining techniques, a big data analysis method, and was conducted by converting descriptive response content into text files using R 4.3.2. The research methodology began with a keyword frequency analysis, followed by visualization through cloud analysis. Then, a keyword difference analysis between organization sizes was conducted using TF-IDF weight comparison, and meaningful word pairs were extracted and categorized through N-gram semantic network analysis, with interview content provided as examples. The results revealed that employees perceive the core competencies of core talents to include problem-solving abilities, critical thinking skills, creativity, and communication skills, recognizing their contribution to project execution and work tasks. Based on these findings, the study discusses its significance, implications, limitations, and directions for future research.

Potential value of expression of receptor accessory protein 4 for evaluating the prognosis of lower-grade glioma patients.

REEP4 is involved in the regulation of the biological process of mitosis. Lower grade glioma (LGG), as a malignant tumor, is accompanied by abnormalities in mitosis, but there have been no reports of REEP4 so far. We collected transcriptome data, DNA methylation data and the clinical characteristics of thousands of patients with LGG. Various big data analysis methods and molecular biology experiments were employed to reveal the impact of REEP4 on the pathological process of LGG. It was found that the expression of REEP4 was significantly elevated and negatively regulated by its methylation site. Therefore, both the high expression of REEP4 and low methylation state of cg16311504 showed that the patients are correlated with lower patient survival rate. In addition, high REEP4 expression participates in the regulation of various cancer-related cellular signaling pathways, such as the cell cycle, MAPK signaling pathway, NOD-like receptor signaling pathway, etc. More importantly, the level of immune cell infiltration significantly increased in the high expression group of REEP4 in the LGG tumor microenvironment and REEP4 has a high positive correlation with PD-L1 and other immune checkpoints. In brief, this study is the first to introduce REEP4 in malignant tumors, which can be used as an independent risk factor that participates in the malignant process of LGG. More importantly, REEP4 has the potential to become a new star in the field of anti-tumor treatment.