Correlation Of Features Research Articles

Online Physical Education Teaching Resources Integration Method Based on Multi-Objective Optimization Algorithm

In order to improve the sharing level of online physical education teaching resources, the integration design of online physical education teaching resources is carried out by using MOOC resource information fusion technology and closed frequent item fusion calculation method, and the intelligent scheduling and management ability of online physical education teaching resources is improved. An online physical education teaching resource integration model based on big data information fusion and cluster scheduling is proposed. The factors of online physical education teaching demand scale are initialized, the structure of common factors of online physical education teaching resources is determined by factor analysis method, the orthogonal rotation of indicators by maximum variance method is adopted, the cloud computing model is adopted to analyze the integration structure of online physical education teaching resources, and the feature quantity of cross frequent items rules of online physical education teaching resources is extracted. The parallel K-means clustering model of online physical education resources integration is constructed, the fuzzy correlation degree features of online physical education resources are calculated, the extracted correlation features of online physical education resources are processed by C-means clustering method for big data fusion, the multi-objective optimization technology is adopted for online physical education resources integration and adaptive scheduling, and the adaptive scheduling ability of online physical education resources is improved. The SPSS statistical software is used for empirical analysis, and the results show that the integration degree of online physical education teaching resources by this method is high, which promotes the development of online physical education teaching.

Spatial Signatures of Electron Correlation in Least-Squares Tensor Hypercontraction.

Least-squares tensor hypercontraction (LS-THC) has received some attention in recent years as an approach to reduce the significant computational costs of wave function-based methods in quantum chemistry. However, previous work has demonstrated that LS-THC factorization performs disproportionately worse in the description of wave function components (e.g., cluster amplitudes T̂2) than Hamiltonian components (e.g., electron repulsion integrals (pq|rs)). This work develops novel theoretical methods to study the source of these errors in the context of the real-space T̂2 kernel, and reports, for the first time, the existence of a "correlation feature" in the errors of the LS-THC representation of the "exchange-like" correlation energy EX and T̂2 that is remarkably consistent across ten molecular species, three correlated wave functions, and four basis sets. This correlation feature portends the existence of a "pair point kernel" missing in the usual LS-THC representation of the wave function, which critically depends upon pairs of grid points situated close to atoms and with interpair distances between one and two Bohr radii. These findings point the way for future LS-THC developments to address these shortcomings.

TMVF: Trusted Multi-View Fish Behavior Recognition with correlative feature and adaptive evidence fusion

TMVF: Trusted Multi-View Fish Behavior Recognition with correlative feature and adaptive evidence fusion

MACHINE LEARNING-DRIVEN STRATEGIES FOR CUSTOMER RETENTION AND FINANCIAL IMPROVEMENT

In the telecom sector, which generates vast amounts of data daily due to its extensive client base, retaining present customers is more cost-effective than attaining new ones. Business analysts and CRM specialists must comprehend the reasons behind customer churn and identify behavioral patterns within client data. This study develops a churn forecast model employing clustering and classification algorithms to recognize churn consumers and highlight the factors influencing customer churn in the telecom industry. Feature selection is done using information gain and correlation feature ranking filters. The Random Forest (RF) algorithm achieved superior performance, correctly classifying 88.63% of churned customer data. An essential CRM function is to formulate effective retention strategies to prevent customer departure. Post-classification, the proposed model clusters the churned customer data and provides group-based retention strategies using cosine similarity among the groups. The performance of the model is assessed through metrics like precision, accuracy, recall, f-score, and ROC-AUC. The findings indicate that the RF algorithm enhances churn classification and customer profiling through k-means clustering, and the classification algorithm helps identify the factors driving customer churn through generated rules.

Colon Cancer Detection Using Exhaustive Correlation Feature Selection Based Genetic Decision Support Regression

Colon Cancer Detection Using Exhaustive Correlation Feature Selection Based Genetic Decision Support Regression

Marine economic network in China’s coastal regions: static characteristics and resilience under attacks

ABSTRACT China’s marine economy exhibits clear complex network characteristics. Given the pressure from internal and external uncertainties, understanding the characteristics of a marine economic complex network and enhancing its resilience are crucial for promoting high-quality development. After marine economic networks are constructed for three coastal regions (the Bohai Rim, Yangtze River Delta, and Pearl River Delta), four key dimensions are analysed to delineate the static features. Subsequently, two types of attacks are simulated to assess connectivity and integrity. The results show a low degree of interdependence in the three major coastal economic circles, leading to slow network recovery. The Bohai Rim demonstrates the highest transmissibility, while the Yangtze River Delta shows a superior correlation feature. Both the Pearl and Yangtze River Deltas exhibit high levels of hierarchy, characterized by strong core groups. Compared with random attacks, targeted attacks are far more damaging to the resilience of China’s marine economic network. Fortunately, since 2012, the number of nodes that must be attacked to completely disrupt the network has increased, indicating a steady improvement in core network resilience. These conclusions offer valuable insights into ensuring the stable, efficient, and rapid development of China’s marine economy.

GeometryFormer: Semi-Convolutional Transformer Integrated with Geometric Perception for Depth Completion in Autonomous Driving Scenes.

Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved. On the one hand, for the poor performance of ViT in details, this paper proposes a semi-convolutional vision transformer to optimize local continuity and designs a geometric perception module to learn the positional correlation and geometric features of sparse points in three-dimensional space to perceive the geometric structures in depth maps for optimizing the recovery of edges and transparent areas. On the other hand, previous methods implement single-stage fusion to directly concatenate or add the outputs of ViT and convolution, resulting in incomplete fusion of the two, especially in complex outdoor scenes, which will generate lots of outliers and ripples. This paper proposes a novel double-stage fusion strategy, applying learnable confidence after self-attention to flexibly learn the weight of local features. Our network achieves state-of-the-art (SoTA) performance with the NYU-Depth-v2 Dataset and the KITTI Depth Completion Dataset. It is worth mentioning that the root mean square error (RMSE) of our model on the NYU-Depth-v2 Dataset is 87.9 mm, which is currently the best among all algorithms. At the end of the article, we also verified the generalization ability in real road scenes.

TimbreSense: Timbre Abnormality Detection for Bel Canto with Smart Devices

With the rise of mobile devices, bel canto practitioners increasingly utilize smart devices as auxiliary tools for improving their singing skills. However, they frequently encounter timbre abnormalities during practice, which, if left unaddressed, can potentially harm their vocal organs. Existing singing assessment systems primarily focus on pitch and melody and lack real-time detection of bel canto timbre abnormalities. Moreover, the diverse vocal habits and timbre compositions among individuals present significant challenges in cross-user recognition of such abnormalities. To address these limitations, we propose TimbreSense, a novel bel canto timbre abnormality detection system. TimbreSense enables real-time detection of the five major timbre abnormalities commonly observed in bel canto singing. We introduce an effective feature extraction pipeline that captures the acoustic characteristics of bel canto singing. By applying temporal average pooling to the Short-Time Fourier Transform (STFT) spectrogram, we reduce redundancy while preserving essential frequency-domain information. Our system leverages a transformer model with self-attention mechanisms to extract correlation and semantic features of overtones in the frequency domain. Additionally, we employ a few-shot learning approach involving pre-training, meta-learning, and fine-tuning to enhance the system’s cross-domain recognition performance while minimizing user usage costs. Experimental results demonstrate the system’s strong cross-user domain recognition performance and real-time capabilities.

An Intelligent Approach of Underwater Image Co-Enhancement Using Correlation Feature Matching with Enhanced Meta-Heuristic Optimization-Aided Transformer UNet

In marine ecosystems, the geological formation, and aquatic life are analyzed by underwater imaging. The visibility of the aquatic environment is enhanced by the underwater image improvement approaches. Because of the dispersal of the particles, attenuation of light, and distortion of color, the capturing of marine images is a challenging task. The enhanced images with the absence of water turbidity and light attenuation provide an excellent view of the underwater sceneries. The dispersion and incorporation of light underwater cause color distortion effects. Without disturbing the accuracy of the underwater image, the process of obtaining an accurate color representation becomes a complicated task. Overall excellence of the enhanced images may affected by the noise or the artifacts by traditional approaches which tends to affect the image quality. Also, the preservation of important details in the image is a rigorous task while removing the distortion. To combat such issues, an adaptive deep learning-based underwater image co-enhancement is suggested. In the first phase, the images are gathered from the standard dataset. Further, the image is fed into the correlation feature matching module. Here, the feature matching is executed via the Adaptive Trans-Unet (ATUNet), and the correlation feature matching performance of the ATUNet is strengthened by tuning the parameter using the Updated Random Parameter-based Red Kite Optimization Algorithm (URP-RKOA). The usefulness of the developed model is defined using the experimental study. Throughout the analysis, the numerical findings of the developed model attain the values of 155.01, 26.2, and 0.78 in terms of MSE, PSNR, and SSIM. The underwater image co-enhancement achieved better performance rather than existing approaches.

Code and Data Repository for Feature Selection and Grouping Effect Analysis for Credit Evaluation via Regularized Diagonal Distance Metric Learning

This repository provides an implementation of the regularized diagonal Distance Metric Learning (DML) model, which improves distance metrics, selects features, and conducts grouping effect analysis by rescaling the features. One characteristic of the proposed model is that it does not pursue linear separability, which is highly unrealistic in financial data. Another characteristic of the proposed model is that it considers correlated features when conducting feature selection, and thus, does not neglect important credit risk sources when used for credit evaluation. The implementation of the solver based on the Alternating Direction Method of Multipliers(ADMM) makes it suitable for large-scale financial applications. The repository also provides the scripts, data, and experimental results reported in the paper. This repository includes four folders, src, scripts, data, and results.

Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods. Our study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments. Retrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 264 healthy control surgical patients. Through correlation screening, chi-square tests, and feature importance ranking, 47 variables were narrowed down to 5 potential predictive factors based on the main cohort of 528 patients. Nine machine learning models, including k-nearest neighbors, support vector machine, random forest, decision tree, gradient boosting machine, adaptive boosting, naive bayes, general linear model, and linear discriminant analysis, were developed and validated to predict postoperative pneumonia. Model performance was evaluated using the area under the receiver operating curve, sensitivity, specificity, accuracy, precision, recall, and F1 score. A distribution plot of feature importance and feature interaction was obtained to interpret the machine learning models. Among 17,190 surgical patients, 264 (1.54%) experienced postoperative pneumonia, which resulted in adverse outcomes such as prolonged hospital stay, increased ICU admission rates, and mortality. We successfully established nine machine learning models for predicting postoperative pneumonia in surgical patients, with the general linear model demonstrating the best overall performance. The AUC of the general linear model on the testing set was 0.877, with an accuracy of 0.82, specificity of 0.89, sensitivity of 0.74, precision of 0.88, and F1 score of 0.80. Our study revealed that the duration of bed rest, unplanned re-operation, end-tidal CO2, postoperative albumin, and chest X-ray film were significant predictors of postoperative pneumonia. Our study firstly demonstrated that the general linear model based on 5 common variables might predict postoperative pneumonia in the general surgical population.

Score matching for differential abundance testing of compositional high-throughput sequencing data.

The class of a-b power interaction models, proposed by Yu et al. (2024), provides a general framework for modeling sparse compositional count data with pairwise feature interactions. This class includes many distributions as special cases and enables modeling of zero counts through power transformations, making it particularly suitable for modern high-throughput sequencing data with excess zeros, including single-cell RNA-Seq and microbial amplicon data. Here, we present an extension of this class of models that allows inclusion of covariate information, thus enabling accurate characterization of covariate dependencies in heterogeneous populations. Combining this model with a tailored differential abundance (DA) test leads to a novel DA testing scheme, cosmoDA, that can reduce false positive detection rate caused by correlated features. cosmoDA uses penalized generalized score matching for parsimonious model fitting. We show on simulated benchmarks that cosmoDA can accurately estimate feature interactions in the presence of population heterogeneity and significantly reduces the false discovery rate when testing for differential abundance of correlated features. Using single-cell and amplicon data, we illustrate cosmoDA's ability to estimate data-adaptive Box-Cox-type data transformations and assess the impact of zero replacement and power transformations on downstream differential abundance results. cosmoDA is available at https://github.com/bio-datascience/cosmoDA.

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

Time-Lag Transiograms and Their Implications for Landscape Change Characterization

Markov chain transition probability matrices (TPMs) have traditionally been used to characterize land use and land cover (LULC) changes and species succession. However, previous studies relied solely on TPMs or transition area matrices to describe overall class area/proportion changes, overlooking important time correlation features. This study introduces the concept of idealized time-lag transiograms and demonstrates how they can be computed from temporal TPMs, using illustrative examples. The primary objective is to explore the potential value and utility of idealized time-lag transiograms in revealing additional characteristics of landscape change. Specifically, we focus on computing idealized time-lag transiograms with a fixed starting point and highlighting their fundamental features, such as sills, practical correlation ranges, and curve shapes, along with peak positions and peak height ratios of peaked cross-transiograms. These features are identified and discussed in terms of their potential implications for characterizing LULC changes. While idealized time-lag transiograms with a fixed starting point may not precisely predict future LULC changes due to the assumptions of the Markov property and time homogeneity (i.e., stationarity), they provide new insights into future LULC dynamics, revealing aspects that traditional Markov chain analysis has overlooked.

Electronic Coherences in Molecules: The Projected Nuclear Quantum Momentum as a Hidden Agent.

Electronic coherences are key to understanding and controlling photoinduced molecular transformations. We identify a crucial quantum-mechanical feature of electron-nuclear correlation, the projected nuclear quantum momenta, essential to capture the correct coherence behavior. For simulations, we show that, unlike traditional trajectory-based schemes, exact-factorization-based methods approximate these correlation terms and correctly capture electronic coherences in a range of situations, including their spatial dependence, an important aspect that influences subsequent electron dynamics and that is becoming accessible in more experiments.

Conceptual metaphor quantum correlation and radial basis extreme learning for predicting chronic kidney disease

Chronic kidney disease is a progressive condition that often remains unnoticed until substantial kidney damage has occurred, leading to severe complications like nerve damage, pregnancy issues, and potentially fatal kidney failure. Early diagnosis of chronic kidney disease is challenging due to a lack of distinct indicators. To enhance detection, a new machine learning technique called conceptual metaphor quantum mutual correlation and radial basis extreme learning is developed for chronic kidney disease diagnosis. This approach starts by extracting critical terms from unstructured datasets. It then applies quantum mutual information-based feature selection to identify highly correlated feature subsets from both unstructured and structured data. Finally, radial basis extreme learning is used for classification, overcoming imbalanced data and computational issues typical in extreme learning machines. The proposed method demonstrates remarkable accuracy (98 %), precision (88 %), and recall (92 %), surpassing traditional models, and holds significant potential for improving chronic kidney disease detection and treatment.

Decoding covert visual attention to motion direction using graph theory features of EEG signals and quadratic discriminant analysis

Visual attention is a type of selective attention that plays an important role in prioritizing and processing the information received from the visual scenes around us. The brain is an intricate network composed of numerous regions, each with distinct functions. As different tasks are performed, brain regions regularly synchronize and correlate with each other through intricate networks of connections. The aim of this study is to decode two states of attention by examining the interactions and connections between different brain regions using graph theory. This is demonstrated by EEG recordings from 15 participants who performed visual attention task. Pearson's correlation coefficient and coherence have been used to measure the functional connections between brain regions. In fact, each of these two criteria is regarded as an individual feature, and we perform decoding using each criterion separately. With an optimal selection of 40 connectivity features, the QDA classifier attained accuracies of 79.83% and 83.28% using correlation and coherence features, respectively. The results of attention decoding using the coherence criterion are more promising, indicating the superior effectiveness of coherence-based methods. Therefore, this study employed graph theory to analyse a neural network derived from coherence measurements. The study focused on three graph-theoretical metrics: degree centrality, efficiency, and betweenness centrality. The QDA classifier, using an optimal set of 40 features that includes degree, betweenness, and channel efficiency, achieved an accuracy of 86.46%. In comparison, the QDA classifier with 40 features based solely on degree centrality reached an accuracy of 89.96%. Finally, the results of this research indicate that analysing brain connections and brain network graphs can effectively decode different covert visual attention states.

Correlation analysis and feature extraction using impedance spectroscopy over aging of lithium ion batteries

To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method.

Temporal–spatial correlation and graph attention-guided network for micro-expression recognition in English learning livestreams

Micro-expressions, fleeting facial movements lasting 1/25 to 1/3 of a second, offer crucial insights into genuine emotions, particularly valuable in online education settings. The rapid growth of English learning livestreams has heightened the need for accurate, real-time micro-expression recognition to enhance learner engagement and instructional effectiveness. However, existing methods need help with the subtle nature of these expressions, especially in dynamic, low-resolution streaming environments. This paper presents TSG–MER–ELL, a novel end-to-end network for micro-expression recognition in English learning livestreams, integrating temporal–spatial correlation and graph attention mechanisms. The framework addresses the unique challenges of real-time emotion analysis in online language education, where subtle facial cues are crucial in understanding learner engagement and comprehension. The temporal–spatial correlation module employs action units with spatio-temporal graph convolution to aggregate features from diverse facial regions, while transformer encoders construct long-range correlations. The graph attention module builds upon local facial areas to guide self-attention computations, yielding precise local correlation features. These global and local features are fused for the final micro-expression classification. We introduce an adaptive loss function that balances accuracy, efficiency, and relevance to linguistic context. Extensive experiments on SMIC, CASME II, and SAMM datasets, adapted for English learning scenarios, demonstrate TSG–MER–ELL’s superior performance over ten state-of-the-art baselines. The TSG–MER–ELL framework achieves top UF1 and UAR scores across all datasets, significantly improving recognition speed and accuracy. Ablation studies and visualizations of temporal–spatial features and graph attention weights provide insights into the framework’s effectiveness in capturing subtle emotional cues. TSG–MER–ELL’s robust performance in varied online learning conditions highlights its potential to enhance engagement, personalize instruction, and improve overall outcomes in virtual English language education.

Classifying School Scope Using Deep Neural Networks Based on Students' Surrounding Living Environments

This research investigates school scope classification using Deep Neural Networks (DNN), focusing on students living environments and educational opportunities. By addressing the interplay of socioeconomic and educational factors, the study aims to develop an analytical framework for understanding how environmental contexts shape academic trajectories. The research provides a nuanced understanding of the importance of features in educational classification by developing DNN models based on Spearman's Rank Correlation Coefficient (SRCC). The methodology employs machine learning techniques, integrating data wrangling, exploratory analysis, and multiple DNN models with K-fold cross-validation. The study analyzes 677 student records from two schools. The research examined multiple model configurations. Results show that the 'All Data' model achieved 83.08% accuracy, the 'Top 5' model 81.54%, and the 'Non-Top 5' model 79.23%. The SRCC-based approach revealed that while top correlated features are important, additional variables significantly contribute to model performance. The study highlights the profound impact of family background, social environment, and educational contexts on school selection. Furthermore, it demonstrates DNN's capability to uncover intricate, non-linear relationships, offering actionable insights for policymakers to leverage machine learning's potential in developing targeted educational strategies.