Fuzzy C-means Clustering Algorithm Research Articles

Robust Fuzzy C-Means Clustering Algorithm Based on Normal Shrink and Membership Filtering for Image Segmentation

Robust Fuzzy C-Means Clustering Algorithm Based on Normal Shrink and Membership Filtering for Image Segmentation

Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models.

Fuzzy clustering-based neural networks (FCNNs) based on information granulation techniques have been shown to be effective Takagi-Sugeno (TS)-type fuzzy models. However, the existing FCNNs could not cope well with sequential learning tasks. In this study, we introduce incremental FCNNs (IFCNNs), which could dynamically update themselves whenever new learning data (e.g., single datum or block data) are incorporated into the dataset. Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions. In the aforementioned partition, the dynamic FCM adaptively adjusts the position of its prototypes by using sequential data. Due to the time-sharing arrival of training data, compared with batch learning models, incremental learning methods may lose classification (prediction) accuracy. In order to tackle this challenge, we utilize quasi-fuzzy local models (QFLMs) based on modified Schmidt neural networks to replace the popular linear functions in TS-type fuzzy models to refine and enhance the ability to represent the behavior of fuzzy subspaces. Meanwhile, the recursive least square error (LSE) estimation is utilized to update the weights of QFLMs from one-by-one or block-by-block (fixed or varying block size) learning data. In addition, the L2 regularization is considered to ameliorate the deterioration of generalization abilities caused by potential overfitting when carrying out weight estimation. The proposed method leads to the construction of FCNNs in a new way, which can effectively deal with incremental data as well as deliver sound generalization capability. Extensive machine-learning datasets and a real-world application are employed to show the validity and performance of the presented methods. From the experimental results, we show that the proposal can maintain sound classification accuracy when effectively processing sequential data.

Performance study of fuzzy C-mean clustering algorithm based on local density in network security

The development and utilization of network big data is also accompanied by data theft and destruction, so the monitoring of network security is particularly important. Based on this, the study applies the fuzzy C-mean clustering algorithm to the network security model, however, the algorithm has major defects in discrete data processing and the influence of feature weights. Therefore, the study introduces the concept of local density and optimizes the initial clustering center to solve its sensitive defects as well as empirical limitations; at the same time, the study introduces the adaptive methods of fuzzy indicators and feature weighting, and uses the concepts such as fuzzy center-of-mass distribution to avoid problems such as the model converging too fast and not being able to handle discrete data. Finally, the study does a simulation analysis of the performance of each module, and the comparison of the overall algorithm with the rest of the models. The experimental results show that in the comparison of the overall algorithm, its false detection rate decreases by 8.57% in the IDS Dataset dataset, compared to the particle swarm algorithm. Therefore, the adaptive weighted fuzzy C-Means algorithm based on local density proposed in the study can effectively improve the network intrusion detection performance.

A Side-Channel Hardware Trojan Detection Method Based on Fuzzy C-Means Clustering and Fusion Distance Algorithms

A Side-Channel Hardware Trojan Detection Method Based on Fuzzy C-Means Clustering and Fusion Distance Algorithms

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

ObjectiveThe feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. Methodsk-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. ResultsThe experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. ConclusionRegarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

A Method for Identifying Gross Errors in Dam Monitoring Data

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

Wind power prediction holds significant importance for the stable operation of power systems and the enhancement of energy utilization efficiency. Interval prediction, capable of providing more uncertain information, has garnered widespread attention. In this study we have designed a hybrid prediction framework for both deterministic and interval prediction. Initially, the original wind power data undergo cleaning using the quartile method and the fuzzy C-means (FCM) clustering algorithm. This approach not only eliminates scattered outliers but also identifies concentrated outliers more effectively. Subsequently, five distinct single prediction models were employed for point predictions. The critic weight method was applied to yield point forecasting results with higher prediction accuracy. Nonparametric kernel density estimation (KDE) and normal distribution (ND) were incorporated to calculate the prediction interval (PI) under varying confidence levels. Finally, an improved northern goshawk optimization (INGO) algorithm was proposed, integrating three enhancement methods: levy flight, sinusoidal mapping, and a reverse learning strategy. This optimization aims to fine-tune the weight coefficients of the aforementioned two interval prediction methods. The resulting PI overcomes the limitations of a single-interval prediction model. To effectively demonstrate the forecasting performance of the proposed prediction framework, two datasets from a wind turbine in northwest China were selected for simulation verification. In deterministic prediction, the designed hybrid forecast model demonstrates a reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by 8.5%, 16.98%, and 11.49%, respectively, compared to other single prediction models. In interval prediction, the prediction interval coverage probability (PICP) is enhanced, while the prediction interval normalized average width (PINAW) is effectively reduced. The designed interval prediction model can reduce the comprehensive evaluation index coverage width-based criterion (CWC) by 10.51%, 9.62%, and 12.95% on average at 95%, 90%, and 80% confidence levels, respectively. Simulation results verified the validity and feasibility of the proposed prediction framework.

A new FCM-XGBoost system for predicting Pavement Condition Index

Predicting Pavement Condition Index (PCI) is crucial for identifying potential pavement distresses and developing effective pavement maintenance strategies. Here, a new Fuzzy C-Means-eXtreme Gradient Boosting (FCM-XGBoost) PCI prediction system is developed. First, asphalt pavement sections and concrete pavement sections are distinguished, and the Fuzzy C-Means (FCM) clustering algorithm is employed to cluster the asphalt and concrete expressway sections respectively based on multiple factors influencing PCI. Next, an eXtreme Gradient Boosting (XGBoost) model is trained for each cluster of expressway sections. Finally, in the system application stage, the PCI of an expressway section for the next year is predicted using the XGBoost model trained for the specific cluster that the expressway section belongs to. Validation results based on actual pavement condition data indicate that the proposed FCM-XGBoost PCI prediction system can achieve slightly higher accuracy compared with several high performance benchmark models, which include the Random Forest model, the Gradient Boosting Decision Tree model, the Light Gradient Boosting Machine model and the XGBoost model. The developed PCI prediction system contributes to the improvement of PCI prediction approaches and can be potentially applied in deploying effective and cost-efficient pavement maintenance strategies.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Research on optimization of case adaptation and enhancement of knowledge application benefits for multi-decision class cases based on FASS-NRS and SAGA-FCM

PurposeTraditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.Design/methodology/approachThe attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.FindingsThe findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.Practical implicationsThis study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.Social implicationsThe adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.Originality/valueThis study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

Outdoor activity classification using smartphone based inertial sensor measurements

AbstractHuman Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model’s performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model’s capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports.

Bivariate regional drought frequency analysis using multivariate approaches: a case study in southwestern Iran

Abstract Bivariate approaches in Regional Frequency Analysis (RFA) address two issues: first, to evaluate the homogeneity of regions, and second, to estimate the joint return periods. This study was conducted to investigate the joint return period of a severe historical drought in southwestern Iran. Fifty-nine rain gauges were first clustered into three, four, and five regions using the fuzzy c-means clustering (FCM) algorithm. Then bivariate discordancy and homogeneity tests were applied to adjust the initial clusters. The results showed that only in the case of three clusters were all the regions homogeneous. Therefore, it can be inferred that combining clustering analysis and discordancy test is insufficient to form homogeneous regions. Finally, the joint return period, by choosing Generalized Logistic and Wakeby as marginal distributions and Clayton as a copula, was estimated for all the sites in the three regions. Since no three-parameter distribution function fitted well to the variable severity, the bivariate homogeneity index does not necessarily attest to region homogeneity regarding the marginal distribution functions. It is also deduced that sites with higher mean annual precipiataion (MAP) and, correspondingly, higher elevation are more likely to experience shorter return periods of same drought events, in contrast to sites with lower MAP or lower elevation.

Computational Evaluation and Quantitative Analysis of the Impact Factor of Large-Scale Events: A Case Study of the 2010 Shanghai World Expo

This paper conducts a computational evaluation and quantitative analysis of the impact of the Shanghai Expo on the economy of Shanghai, utilizing Shanghai’s GDP as the primary economic indicator. Initially, a vertical assessment of the Shanghai Expo’s influence on Shanghai’s GDP is undertaken, establishing a trend model that shows a periodic increase in the Expo’s impact on GDP. Subsequently, a horizontal evaluation of the Shanghai Expo’s impact on Shanghai’s GDP is performed, focusing on the Shanghai Expo alongside eight other Expos as research subjects. Five measurable indicators—participating countries, attendance numbers, exhibition area, and pre- and post-Shanghai Expo GDP situations—are selected for analysis. Utilizing fuzzy clustering analysis, a horizontal impact evaluation model of the Shanghai Expo on Shanghai’s GDP is developed. The study concludes that the Shanghai Expo and the Osaka Expo fall within the same classification using the Fuzzy C-mean Clustering Algorithm (FCA).

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Parameter selection of suppressed relative entropy fuzzy c-means clustering algorithm

The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a constant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate.

Adaptive fuzzy weighted C-mean image segmentation algorithm combining a new distance metric and prior entropy

The fuzzy C-mean clustering algorithm (FCM) is effective in image segmentation. However, its sensitivity to initialization settings and the limitation of the Euclidean distance metric in measuring similarity lead to unsatisfactory image segmentation. To overcome these shortcomings, we propose an adaptive fuzzy weighted C-mean image segmentation algorithm that combines a new distance metric and prior entropy. This algorithm significantly contributes in three aspects. First, we employ an improved sparse subspace clustering (SSC) algorithm based on the superpixel image for initialization settings. This step aims to obtain the initial number of clusters and the membership matrix, ensuring the algorithm’s convergence to the optimal solution. Second, to better capture differences between image regions, we introduce a novel distance metric that combines Euclidean and non-Euclidean distances. Simultaneously, the fuzzy weight is introduced to mitigate redundant feature interference, making the clustering result more reasonable. Finally, leveraging prior entropy – a maximum entropy under conditional constraints – we refine the algorithm’s adaptability to unknown features, thereby improving clustering accuracy. Comparative experiments with several state-of-the-art algorithms validate the effectiveness and robustness of our proposed algorithm.

Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada)

Abstract. For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised geographic object-based image analysis (GeOBIA) paired with the fuzzy c-means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time period, each with a frequency of 5 years. Our study site is the George River watershed (GRW), a 42 000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The time series of seven classifications showed a homogenization of subwatershed types associated with increases in vegetation productivity and in water contents in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evaporation and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 yr−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote-sensing-based approaches, particularly in areas of discontinuous and sporadic permafrost.

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

PurposeMulti-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.Design/methodology/approachThe authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.FindingsThe authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.Originality/valueMost of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

The Path of Digital Protection and Innovative Development of Rural Traditional Cultural Resources Supported by Intelligent Information

Abstract The digitized quality of rural traditional cultural resources is relatively low, and there are problems such as heterogeneity and incompleteness in the resource data, resulting in the limitation of related cultural data information mining and the inability to realize deep development and innovation. Therefore, this paper combines the fully connected neural network and fuzzy C-mean clustering algorithm to construct a cultural digital resource clustering model, and based on the clustering results combined with CR-LDA and collaborative filtering algorithm to achieve personalized recommendation of rural traditional cultural digital resources. The experimental results show that the clustering model combining a fully connected neural network and a fuzzy C-mean clustering algorithm has a better clustering effect than the other three clustering models, and it also shows good robustness and stability. In addition, although the collaborative filtering model combining the CR-LDA algorithm has a slight increase in runtime compared to the LDA model runtime, the classification accuracy is significantly improved. It thus can provide platform users with practical and reliable cultural resource recommendations. Most users indicated in the survey that they agreed with the results of the model’s personalized cultural digital resource recommendations.

Exploration and Practice of English Civics Teaching Design in Colleges and Universities Based on Big Data Analysis

Abstract This study explored the cutting-edge methodology of integrating curricular Civics into teaching specialized disciplines in higher education, an essential trend for the future development of higher education. By applying data-mining algorithms, the article constructs a big-data analysis framework from the dimensions of students’ classroom behavior and performance. The fuzzy C-mean clustering algorithm was optimized using the cuckoo algorithm to effectively classify the characteristics of students’ behavior in the Civics classroom, and the attention-based hybrid encoder-decoder model was used to accurately predict students’ grades. After establishing a complete analytical framework, an innovative college English Civics teaching model was developed following the ASSURE teaching model. It was applied and analyzed for its effect in actual teaching. The study results show that, after the implementation of the English Civics and Politics course based on the ASSURE model, the average grade of the students increased by about 0.5 points. The grades were distributed between 80 and 95 points, significantly different from the pre-test grades (P<0.05). The prediction error was controlled to be less than ±2 points, which indicates that the teaching model had a significant teaching effect. This study not only improves English learning performance but also promotes the improvement of students’ performance in Civics, showing the great potential and value of integrating Civics curriculum into professional teaching.