Narrow Results

In order to deeply analyze the causes of English learners’ procrastination in e-learning and its influence on learning effect, an artificial intelligence (AI)-based method is designed to analyze the influencing factors of procrastination. By using K-means algorithm, this method divides learners’ online learning procrastination into two categories: active procrastination and passive procrastination, and collects corresponding learning state data samples. Then, taking into account various factors, including students, teachers, and the environment, we identified 11 key factors that may contribute to learning procrastination. Then, using the artificial intelligence-based procrastination factor ranking analysis model and the cuckoo search algorithm-trained XGBoost model, we trained multiple decision tree models to learn and predict the association between these influencing factors and different procrastination types of learning states. The experimental results show that after the application of this method, through in-depth analysis of the phenomenon of procrastination in students’ online English learning, different types of procrastination and their influencing factors are successfully identified, and an effective intervention model is designed based on the analysis results, which significantly improves students’ learning efficiency and provides strong support for the intervention of procrastination. It is proved that this method has certain significance for the accurate analysis of learning delay factors and effective intervention of procrastination in English e-learning.

With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies.

For the contemporary student teaching system, the information-based education management system has played a positive role in the rapid promotion of teaching. In order to get the teaching exploration data of modern information management from the data increment on the Internet, this paper updates the technology of data mining. This paper constructs a quantitative table for teacher positions and conducts ideological and political job evaluations in the database. This paper mainly focuses on the management and construction of ideological informatization based on the $k$-means algorithm. By using the $k$-means algorithm to analyze the daily behavior data of students, we can discover their behavior patterns and trends, providing decision support for student management. For example, by analyzing data such as students’ internet time, social activities, and library borrowing records, we can understand their interests, hobbies, and learning habits, providing a foundation for personalized education. Cluster analysis of students’ ideological and political performance using the $k$-means algorithm can identify student groups with different ideological tendencies. This helps schools to carry out targeted ideological and political education work, and improve the pertinence and effectiveness of ideological and political education. Through quality tracking analysis of educational evaluation, it has carried out educational informatization tracking management using an information model. In the process of judging and analyzing different subjects, it utilized the hot topics of universities for a more comprehensive information intelligent tracking management.

A method for arbitrary surface reconstruction from 3D large scattered points is proposed in this paper. According to the properties of 3D points, e.g. the non-uniform distribution and unknown topology, an implicit surface model is adopted based on radial basis functions network. And because of the property of locality of radial basis function, the method is fast and robust in surface reconstruction. Furthermore, an adapted K-Means algorithm is used to reduce reconstruction centers. For features completeness, two effective methods are introduced to extract the feature points before the adapted K-Means algorithm. Experiment results show that the presented approach is a good solution for reconstruction from 3D large scattered points.

An efficient partitional clustering technique, called SAKM-clustering, that integrates the power of simulated annealing for obtaining minimum energy configuration, and the searching capability of K-means algorithm is proposed in this article. The clustering methodology is used to search for appropriate clusters in multidimensional feature space such that a similarity metric of the resulting clusters is optimized. Data points are redistributed among the clusters probabilistically, so that points that are farther away from the cluster center have higher probabilities of migrating to other clusters than those which are closer to it. The superiority of the SAKM-clustering algorithm over the widely used K-means algorithm is extensively demonstrated for artificial and real life data sets.

Aiming at the problem of portrait of members in shopping malls, this paper analyzes the similarities and differences of consumption behaviors between member groups and nonmember groups, and constructs the LRFMC model with $k$-means algorithm to analyze the value of membership. Second, active states of members are divided according to the consumption time interval, and KNN algorithm model is established to predict member states and used to predict the membership status. Finally, it discusses which types of goods are more suitable for promotional activities and can bring more profits to the shopping mall.

With the rapid development of computer technology and electronics industry, computer processing capability and image processing technology have been greatly improved, making robots based on computer processing and image processing have entered a new development in the field of navigation path recognition research. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. The key to the successful execution of the mobile robot is to move according to the planned path and to avoid obstacles autonomously. These two points depend on the validity and accuracy of navigation path identification. At present, research on mobile robot navigation path recognition mainly uses visual navigation as the main method, which uses visual sensors to simulate human eye functions, obtains relevant information from external environment images, and processes them to realize related functions that the system needs to complete. The two major problems in visual navigation are poor recognition ability and insufficient ability to resist light source interference. The main purpose of this paper is to improve the recognition ability of mobile robot navigation path and the ability to resist light source interference. It mainly uses the $K$-means algorithm for visual navigation research. By simulating the acquired image and the selected color space, the results show that the average time taken to complete a path identification method is 152ms. Under different illumination environments, the information extraction rate of mobile robot navigation path can reach 90%, and the effect of strong light on navigation path recognition is effectively reduced under strong illumination environment. The results show that the recognition of the visual navigation path of a mobile robot using the $K$-means algorithm is more precise than the conventional method, and it takes less time to better meet the timeliness requirements of mobile robots.

Underwater signal classification has been an area of considerable importance due to its applications in multidimensional fields. The selection of the source specific features in a classifier is very significant, as it determines the efficiency and performance of the classifier. Discrete sine transform (DST)-based features possesses the essential traits suitable for the design of statistical models in underwater signal classifiers. These when incorporated in hidden markov models (HMMs), can provide an effective architecture which can be utilized in the classification of underwater noise sources. The design and performance analysis of a 12-state HMM-based classifier for underwater signals in Rayleigh fading channel conditions are presented in this paper. The HMMs utilizing the DST features are found to perform efficiently in underwater signal classification scenario, compared to existing cepstral feature-based classifiers. The fading channel estimation has been carried out and the classifier performance has been improved by providing Rayleigh fading compensation. The success rates of the classifier has been estimated under different operating conditions. The system performance has been analyzed in MATLAB^TM platform for the class of underwater signals, which include actual field collected data and the results have been presented in this paper.

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical $k$-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.

In this paper, we present a concept based on the similarity of categorical attribute values considering implicit relationships and propose a new and effective clustering procedure for mixed data. Our procedure obtains similarities between categorical values from careful analysis and maps the values in each categorical attribute into points in two-dimensional coordinate space using multidimensional scaling. These mapped values make it possible to interpret the relationships between attribute values and to directly apply categorical attributes to clustering algorithms using a Euclidean distance. After trivial modifications, our procedure for clustering mixed data uses the k-means algorithm, well known for its efficiency in clustering large data sets. We use the familiar soybean disease and adult data sets to demonstrate the performance of our clustering procedure. The satisfactory results that we have obtained demonstrate the effectiveness of our algorithm in discovering structure in data.

China is one of the world’s major producers and consumers of energy. The investment value of China’s energy industry has attracted the attention of investors at home and abroad. Few studies, however, have specifically investigated investment ratings in China’s traditional energy industry. This study, therefore, uses scientific analysis methods to help investors measure the investment value and returns of China’s energy industry. From the perspectives of market performance and earnings management, we select factors that influence stock value evaluation indicators and undertake an empirical analysis using financial statement data for 2020 from the Wind database. Based on a factor analysis of the main financial indicators (e.g. amplitude, turnover rate, gross profit margin of sales, growth rate of operating revenue), we obtain five main factors: stock market performance, trading heat, profit quality, profit scale, and profit potential. The $k$-means algorithm in Python is then used to analyse 56 stocks in China’s energy industry, and we divide their investment ratings into six grades: risk stocks, prudent holding, undetermined class, hold rating, ordinary rating, and buy rating. By identifying the group characteristics of different types of stocks, this study can provide a decision-making basis for investors while also having reference value for research institutions, financial departments, and government departments.

Through the in-depth study of the K-means data analysis algorithm, and on the basis of existing defects of Internet examination system platform, this paper designs and implements an algorithm based on maximum minimum distance algorithm and K-means data mining algorithm. The experimental results show that the new algorithm improves the speed and success rate of the test paper.

Clustering is an important unsupervised learning technique to discover the inherent structure of a given data set. In this paper, we propose a novel method to determine optimal classes and select optimal samples in data sets, the novel method is based on fuzzy c–means algorithm and the k–means algorithm. An illustrate example shows that our method is simple and valid for clustering and pattern recognition.