Genetic Clustering Algorithm Research Articles

Exploring the Link Between Roommate Compatibility and Academic Outcomes: A Systematic Review of Personality-Based Matching Systems

The collegiate experience significantly influences students’ academic, personal, and social development. Roommate relationships, a central part of shared living arrangements, are crucial for students’ psychosocial well-being and academic success. This systematic review aims to evaluate the effectiveness of personality-based roommate matching systems in improving roommate compatibility, fostering personal development, and enhancing academic performance. A comprehensive search of the Google Scholar and Scopus databases was conducted to identify studies on automated and personality-based systems. Selected studies were screened and analyzed to assess the impact on resource allocation, student satisfaction, and housing efficiency. Personality-based systems - such as fuzzy clustering models, genetic algorithms, and rule-based algorithms - significantly outperform traditional methods in enhancing compatibility and resource allocation. Findings highlight the positive impact of roommate compatibility on students’ self-confidence, communication, and independence, with female students facing unique challenges. Positive relationships, especially among students with high extraversion and conscientiousness, correlate with improved academic outcomes, while negative interactions and depressive symptoms are linked to lower performance and well-being. Personality assessments, like the Open Four Temperaments Scales Test, show promise in reducing conflicts and increasing satisfaction. The study suggests that integrating personality profiling with technology can provide supportive living environments in higher education. However, gaps remain in understanding their impact across different institutional and cultural contexts. Further research is needed to explore the long-term efficacy of these systems. This review provides insights for institutions seeking to optimize hostel management practices and support student development.

Precise solar radiation forecasting for sustainable energy integration: A hybrid CEEMD-SCM-GA-LGBM model for day-ahead power and hydrogen production

In response to the critical need for sustainable energy solutions, this study introduces a groundbreaking hybrid model for enhancing solar radiation forecasting, which is crucial for optimizing solar power integration and hydrogen production. Leveraging a sophisticated amalgamation of Ensemble Empirical Mode Decomposition (EEMD), Sample Entropy-based System Clustering Method (SCM), Genetic Algorithms (GA), and Light Gradient Boosting Machine (LGBM), the model sets a new benchmark in forecasting accuracy. Tested across diverse seasonal data from Jiangsu province, China, it outperforms conventional models like Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, achieving exceptional accuracy with an average coefficient of determination (R2) of 0.99, root mean square error (RMSE) of 31.17, and mean absolute error (MAE) of 24.03. The model's practical efficacy was validated through a simulated system comprising a photovoltaic array, a DC-DC converter, and an Alkaline electrolyzer, demonstrating a peak hydrogen production of 0.25 kg at 2:30 p.m. on June 18. This indicates the model's superior capability to forecast solar irradiance accurately, which is essential for enhancing the efficiency of solar energy systems and hydrogen generation. The CEEMD-SCM-GA-LGBM hybrid model's adoption is advocated for solar radiation forecasting, promising significant advancements in the integration of solar energy and the optimization of hydrogen production, aligning with global sustainability objectives.

A survey of genetic algorithms for clustering: Taxonomy and empirical analysis

Clustering, an unsupervised learning technique, aims to group patterns into clusters where similar patterns are grouped together, while dissimilar ones are placed in different clusters. This task can present itself as a complex optimization problem due to the extensive search space generated by all potential data partitions. Genetic Algorithms (GAs) have emerged as efficient tools for addressing this task. Consequently, significant advancements and numerous proposals have been developed in this field.This work offers a comprehensive and critical review of state-of-the-art mono-objective Genetic Algorithms (GAs) for partitional clustering. From a more theoretical standpoint, it examines 22 well-known proposals in detail, covering their encoding strategies, objective functions, genetic operators, local search methods, and parent selection strategies. Based on this information, a specific taxonomy is proposed. In addition, from a more practical standpoint, a detailed experimental study is conducted to discern the advantages and disadvantages of approaches. Specifically, 22 different cluster validation indices are considered to compare the performance of clustering techniques. This evaluation is performed across 94 datasets encompassing diverse configurations, including the number of classes, separation between classes, and pattern dimensionality. Results reveal interesting findings, such as the key role of local search in optimizing results and reducing search space. Additionally, representations based on centroids and labels demonstrate greater efficiency and crossover and mutation operators do not prove to be as relevant. Ultimately, while the results are satisfactory, real-world clustering problems introduce additional complexity, especially for algorithms aiming to determine the number of clusters, resulting in diminished performance and the need for new approaches to be explored. Code, datasets and instructions to run algorithms in the LEAL library are available in an associated repository, in order to facilitate future experiments in this environment.

Genetic Clustering Algorithm Based on the Division and Combination of Layer Series of Development

Petroleum is a critical industrial material. In the process of oilfield development, the Fisher’s optimal segmentation method is often used to solve the problem of division and combination of layer series of development. However, when encountering the problem of long samples, this method has particularly obvious drawbacks due to the high storage requirements of the calculation process. Therefore, in practical work, the Fisher’s optimal binary segmentation method is generally used instead. Although it avoids storage problems, it is prone to falling into local optima. On the basis of analyzing the shortcomings of Fisher’s optimal segmentation and optimal binary segmentation algorithms, this paper processes a genetic clustering algorithm. This algorithm overcomes the problem of Fisher’s optimal binary segmentation algorithm easily falling into local optima and solves the problem of high storage capacity requirements in the calculation process of Fisher’s optimal segmentation algorithm. Taking the data of 17 subzones in S-2 8-11 sand groups of the Shahejie Formation of the Lower Tertiary in the Dongxin area as an example, this algorithm is applied to divide and combine layer series of development. The experimental results show that the algorithm’s partitioning results are reasonable and can optimize the selection of development layers and provide decision support for the production of oil and gas resources.

Hybrid Machine Learning Algorithms to Evaluate Prostate Cancer

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure.

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Theoretical Utility of Data Value Metric and Genetic Algorithms for Variable Clustering in an Unsupervised Learning Environment

Theoretical Utility of Data Value Metric and Genetic Algorithms for Variable Clustering in an Unsupervised Learning Environment

Abstract 4343: Revealing ovarian cancer copy number variation in single cells

Abstract The mortality rate associated with ovarian cancer (OvCa) is disproportionately high in comparison to its incidence rate. This is partly due to the heterogeneous nature of the disease, which reduces treatment efficacy and contributes to high rates of relapse and chemotherapy resistance. Most OvCa are epithelial in origin and can be classified into four main subtypes: serous, mucinous, endometrioid, and clear cell. Of these, high grade serous ovarian cancer (HGSOC) is the deadliest. Epithelial ovarian carcinomas (EOC) typically exhibit widespread chromosomal and arm-level copy number abnormalities across most of the genome; in HGSOC, focal amplifications and microdeletions are especially prevalent and indicative of high genomic instability. To understand the heterogeneity of aneuploidy in EOC and HGSOC, we performed shallow single-cell whole genome sequencing on four EOC samples: two HGSOC, one clear cell, and one mixed clear cell and endometrioid. All samples were late stage and treatment naïve, and one sample had a known BRCA2 mutation. Sequencing data was processed by two complementary methods to call copy number alterations. First, we used the Cell Ranger DNA pipeline (10x Genomics) to align cell-identified sequencing reads to human reference genome GRCh38 for coverage-based copy number estimation. Resulting copy number calls were cleaned up for mappability, quality, and noisiness. Each sample was then subject to clustering and subclustering analysis using maximum likelihood genetic clustering algorithms. All samples exhibited a high level of aneuploidy, including characteristic alterations known to be associated with EOC. Two tumors contained readily distinguishable clonal populations, and all samples contained main tumor clones that could be further divided by unique subclonal characteristics. Evidence of polyploidy was also seen in all four specimens, with some tumor clusters exhibiting triploid and tetraploid baselines. In parallel, sequencing data was analyzed by the Copy-number Haplotype Inference in Single-cell by Evolutionary Links (CHISEL) algorithm. CHISEL utilizes both binned read depth ratio and B-allele frequency data to determine allele- and haplotype-specific copy numbers in single cells. Results from CHISEL confirmed the copy number calls from Cell Ranger DNA, and revealed widespread loss of heterozygosity in all samples. These findings were corroborated with allele-specific copy number data derived from matched tumor-normal whole exome sequencing. Furthermore, CHISEL detected polyploidy in one-third of the tumor cells with no preference for the A or B alleles. Overall, our findings highlight that the known heterogeneity of ovarian cancer extends to the level of aneuploidy and CNAs, shedding light on factors which pose significant barriers to effective personalized medicine implementation. Citation Format: Yuxin Jin, Rania Bassiouni, Lee D. Gibbs, Jing Qian, Solomon Rotimi, Michelle G. Webb, Seeta Rajpara, David W. Craig, Lynda D. Roman, John D. Carpten. Revealing ovarian cancer copy number variation in single cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4343.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

Genetic Algorithm for Multi-hop VANET Clustering Based on Coalitional Game

Genetic Algorithm for Multi-hop VANET Clustering Based on Coalitional Game

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates.

Regional integrated energy system long-term planning optimization based on multi-energy complementarity quantification

The regional integrated energy system (RIES) is vital to utilizing added renewable energy and improving energy efficiency. Multi-energy complementarity is the primary characteristic and advantage of RIES. A quantitative complementarity analysis is critical to reveal its long-term effects and realize the flexible construction and coordinated operation in RIES. This paper proposes an innovative complementarity quantification method by analyzing the correlation of multi-energy net load. A multi-objective two-layer model combined with comprehensive cost analysis is established, considering renewable energy allocation, operation, investment, and maintenance. System complementarity indicators are evaluated dynamically according to the various devices’ power output time series in the long-term planning process. Regional load, meteorological characteristics clustering and non-dominated sorting genetic algorithm are also developed to improve solver efficiency. Then, simulations of daily operations and long-term planning are carried out. The scheduling modes of three main energy carriers and the output fluctuation of renewable energy in different planning schemes are further compared to demonstrate the mechanism and effectiveness of multi-energy complementation. The results revealed quantitative relationships between complementarity and economy on the early investment and long-term scales, which helped to present optimal system configuration strategies. The complementarity effectively alleviated the grid peak pressure by 11.5% in a daily operation case and reduced the annualized total cost by €11,957 per year in a 20-year planning simulation with 10% load growth.

Class Thresholds Pre-Definition by Clustering Techniques for Applications of ELECTRE TRI Method

The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers’ expertise, which sometimes might be imprecise. To fill that gap, in this paper, a comparative analysis using the multi-criteria method ELECTRE TRI and clustering algorithms is performed to obtain an auxiliary procedure to define initial thresholds for the ELECTRE TRI method. In this proposed methodology, K-Means, K-Medoids, Fuzzy C-Means algorithms, and Bio-Inspired metaheuristics such as PSO, Differential Evolution, and Genetic algorithm for clustering are tested considering a dataset from a fundamental problem of sorting in Water Distribution Networks. The computational performances indicate that Fuzzy C-Means was more suitable for achieving the desired response. The practical contributions show a relevant procedure to provide an initial view of boundaries in multi-criteria sorting methods based on the datasets from specific applications. Theoretically, it is a new development to pre-define the initial limits of classes for the sorting problem in multi-criteria approach.

A Two-Stage Strategy for UAV-enabled Wireless Power Transfer in Unknown Environments

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic Clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any apriori knowledge of their information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that the proposed algorithms outperform their counterparts.

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Epigenetics is the study of phenotypic variations that do not alter DNA sequences. Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers. One of these alterations is DNA methylation; an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer. Therefore, studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences. Currently, microarray technologies; such as Illumina Infinium BeadChip assays; are used to study DNA methylation at an extremely large number of varying loci. At each DNA methylation site, a beta value (β) is used to reflect the methylation intensity. Therefore, clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers as well as identify the relevant loci without user bias. This study proposed a Nested Big Data Clustering Genetic Algorithm (NBDC-GA); a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites. The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas (TCGA); a cancer genomics program created by the National Cancer Institute (NCI) and the National Human Genome Research Institute. The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm (IGA) that was tested using the same data sets. The NBDC-GA outperformed the IGA in terms of convergence performance. Furthermore, the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximum of 67% and of 94.5% for individual cancer type and collective cancer, respectively. The proposed NBDC-GA was also able to identify two chromosomes with highly contrasting DNA methylations activities that were previously linked to cancer.

Fuzzy modeling system based on GA fuzzy rule extraction and hybrid of differential evolution and Tabu search approaches: Application in synthesis gas conversion to valuable hydrocarbons process

In the present study, a fuzzy approach is employed in order to develop a model for the conversion of synthesis gas to valuable hydrocarbon process. The fuzzy rules are extracted by clustering technique in this study. The numbers of fuzzy sets for input and output variables are optimized by genetic algorithm (GA) so that the number of data which follows the inconsistent rules, is minimized. Firstly, the center values of clusters are estimated by a proposed logarithmic distance method and the clusters and their centers are then calculated by k-means method. The prediction of CO conversion and olefin and paraffin selectivities is performed based on five input variables: H2/CO content, Time on Stream (TOS), temperature, Weight Hourly Space Velocity (WHSV) and phosphorous content by the proposed fuzzy model so that the parameters of which are optimized by combination of Differential Elevation and Tabu Search optimization approaches (DETS). The comparison of the performance of the designed approaches with two other models (i.e. fuzzy and GA fuzzy models) is executed and the results clearly demonstrate that the developed DETS- GA clustering fuzzy model can be defined as a powerful tool in modeling of the conversion of synthesis gas to hydrocarbons process with regression analysis (R2) of over 96%.

Research on detection mechanism of vessel abnormal behavior based on immune genetic spectral clustering algorithm

Maritime transport accounts for over 90% of global trade, and maritime safety has been confirmed as a vital issue of maritime transport. Accordingly, vessel traffic service system (VTS) is capable of assisting the maritime department to complete real-time supervision of a certain water area and increasing the efficiency of water supervision. However, the VTS system has low effectiveness in vessel anomaly detection and supervision, thus resulting in regulatory blind spot in the VTS system. In this study, a vessel trajectory anomaly detection mechanism is developed using the immune genetic spectral clustering method. Moreover, the contour coefficient of scatter clustering is accurately analyzed, and the clustering center is optimized using the adopted method. On that basis, different abnormal features can be detected, and abnormal detection accuracy can be increased. In this study, the vessel trajectory anomaly detection model is built in accordance with the set sliding window and abnormal threshold parameters of vessel trajectory characteristics. The AIS data of Lianyungang to Qingdao port from March 2021 are selected for experimental analysis. The results suggest that the proposed method outperforms the conventional method in the detection accuracy and the false alarm rate, it facilitates the intelligent and automatic management of vessels by the VTS system.

Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition

In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance.

Image Processing Method Based on FGCA and Artificial Neural Network

With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.

Construction and Application of Farmers' Practical Teaching System in Vocational Education Based on Big Data Mining Technology.

With the establishment and perfection of social market economy, China has made changes to the disadvantages of farmers' vocational education system, such as singleness, backward educational means, and backward levels. Compared to traditional forms of farming, problems related to lack of farming expertise, poor scientific and technological awareness, and weak labor skills are analyzed by applying big data mining technology to retrieve key issues in order to establish a professional education system. Data mining can meet the needs of farming knowledge and rapidly develop into an automatic information farming model, which is an effective way to maximize and enhance professional knowledge. The establishment of a professional education system will train most scientific research members and further enhance diversified labor productivity. The experimental summary of this paper is as follows: (1) the relevant data need to be predicted before and after, the predicted experimental data will effectively improve students' grades, which is beneficial to the development of practical teaching, and the pretreatment stage plays a substantial role. (2) Among the three algorithms, the adaptive function of genetic clustering algorithm is obviously better than the other two algorithms, and the adaptive curve is relatively stable. (3) The comprehensive assessment of the course divides students into three categories: poor students, medium students, and excellent students, among which poor students account for 30%, medium students account for 50%, and excellent students account for 20%. (4) The standardization of the education system has brought users a good learning mechanism, in which the teaching resources have been strengthened, and users have very high satisfaction with the evaluation of the whole system.