Feature Extraction Algorithm Research Articles

Overview of the whalse algorithm for feature selection and extraction in machine learning

Feature selection and extraction play a key role in machine learning, helping to improve model performance and reduce data complexity. In the context of feature selection and extraction in machine learning, the Whales approach, also known as the Whale Optimization Algorithm (WOA), is often used. The unique properties of the Whales algorithm and its optimization capabilities can be useful in solving problems of feature selection and extraction. This article contains both a review of similar methods and an analysis of the Whales algorithm itself and the main stages of its application. The paper also examines the relationship between the Whales algorithm and traditional methods of feature selection and extraction and identifies future directions for research.

The Establishment of Chinese Culture Teaching Resources by Knowledge Graph Applied to Chinese International Education

Abstract With the development of Chinese international education, the construction of Chinese culture teaching resources has become a priority. This study uses knowledge mapping technology to optimize Chinese culture teaching resources in Chinese international education and improve the usability and efficiency of teaching resources. The study adopts a top-down approach to construct a knowledge graph of Chinese culture teaching resources, covering named entity identification, relationship extraction and knowledge meta-extraction. BERT and BiLSTM techniques are used for feature extraction, text encoding, and conditional random field algorithm for knowledge meta-extraction and relationship mining. The experimental results show that the information utilization rate of intelligent retrieval of teaching resources using this knowledge graph is 82.67% and shows good adaptability in applications between different geographical regions. In addition, through comparative experiments, the proposed method in this study performs better than the traditional method in improving the utilization efficiency of teaching resources. This study proves the effectiveness and practicality of knowledge mapping in constructing Chinese culture teaching resources in Chinese international education.

Research on Key Technologies and Eco-Innovation Strategies for Digital Transformation of Art Industry

Abstract The development of digital technology has brought unprecedented opportunities and challenges for the art industry, and seeking transformation and upgrading has become the preferred choice for the development of the art industry in the wave of digitalization that keeps rushing forward. This paper constructs an art design system using image processing technology to provide strong technical support for the core design of the art industry. The method of sparse feature expression and visual communication is used to extract the edge features of artworks, and the chunked feature matching technology is used for spatial sampling of artworks to realize the three-dimensional display of artworks based on virtual reality technology. The application effect of the image processing algorithm used in the art design system of this paper is significantly better than that of the original processing technology and digital signal-based processing technology. At the same time, the feature extraction and matching algorithms in the three-dimensional display of this paper are also significantly better than other algorithms, and the matching and fusion of artworks can realize the effect of high similarity with the original works. Based on the Kano model of display demand research, this paper finally calculated that the SI value is located between 0.1943~0.8120, the DSI value is located between -0.7053~-0.1004, and according to the calculation of the various demand items affecting user satisfaction, which provides a directional guideline for the upgrading and transformation of the display of works of art and the art industry.

Application of “Artificial Intelligence + Education” Innovation Model in Higher Education Management and Student Training

Abstract The rapid development of modern science and technology promotes technological innovation in education, and this paper constructs the higher education innovation model of “Artificial Intelligence+Education” to realize the goal of responding to the construction of education informatization in higher education. The intelligent education platform is created using the theoretical foundation of three-layer education to manage data integratedly. The intelligent mode includes face recognition and voice recognition, which are realized using LBP feature extraction and neural network algorithms. Their effects are verified. The model is better than the traditional education model in all dimensions of the impact of student training. The P is less than 0.05, which is a significant difference, and the effect of education management is improved by 0.02~0.09. This paper’s “Artificial Intelligence + Education” innovation model has certain advantages in higher education management and student training, and provides a practical path to the construction of informationization and intelligent development of education. It provides a pragmatic path for constructing information and the thoughtful development of education.

EMG Physical Action Detection using Recurrence Plot Approach

Electromyography (EMG) is used to identify neuromuscular illnesses, motor issues, nerve damage, and degenerative ailments. EMG signals are difficult to accurately classify because of their complexity, nonlinearity, and time-variable nature; therefore, proper feature extraction and classification algorithms must be used. The proposed study uses the recurrence plot (RP) approach combined with deep learning (DL) techniques for the purpose of normal and aggressive EMG physical action (PA) classification. The transfer learning (TL) approach Inception-ResNet-v2 is utilized for feature extraction here. The Inception-ResNet-v2 model’s results demonstrate the excellent generalizability of the proposed method. Using only a few of the eight leads in the original EMG data, the model demonstrated the best accuracy (0.9841) and F1 score (0.99). These results show that the 2D RP-based technique has a significant clinical potential for classifying PA while requiring fewer EMG leads.

Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers

Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers

Feature Selection for Unbalanced Distribution Hybrid Data Based on ${k}$-Nearest Neighborhood Rough Set

Neighborhood rough sets are now widely used to process numerical data. Nevertheless, most of the existing neighborhood rough sets are not able to distinguish class mixture samples well when dealing with classification problems. That is, it cannot effectively classify categories when dealing with data with an unbalanced distribution. Because of this, in this aiticle, we propose a new feature selection method that takes into consideration both heterogeneous data and feature interaction. The proposed model well integrates the ascendancy of δ-neighborhood and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor. Such heterogeneous data can be handled better than existing neighborhood models. We utilize information entropy theories such as mutual information and conditional mutual information, and employ an iterative strategy to define the importance of each feature in decision-making. Furthermore, we design a feature extraction algorithm based on above idea. Experimental results displays that the raised algorithm has superior effect than some existing algorithms, particularly the δ-neighborhood rough set model and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighborhood rough set model.

Unbiased multivariate multiscale sample entropy

The development of multi-channel data acquisition techniques has provided richer prior information for studying the nonlinear dynamic characteristics of complex systems. However, conventional nonlinear feature extraction algorithms prove unsuitable in the context of multi-channel data. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although the MMSE algorithm generalized the multiscale sample entropy algorithm, presenting a novel method for multidimensional data analysis, it remains deficient in theoretical underpinning and suffers from shortcomings, such as missing cross-channel correlation information and having biased estimation results. In this paper, unbiased multivariate multiscale sample entropy algorithm (UMMSE) is proposed. UMMSE increases the embedding dimension from &lt;i&gt;M&lt;/i&gt; to &lt;i&gt;M&lt;/i&gt; + &lt;i&gt;p&lt;/i&gt;. This increasing strategy facilitates the reconstruction of a deterministic phase space. By virtue of theoretical scrutiny grounded in probability theory and subsequent experimental validation, this paper illustrates the algorithm's effectiveness in extracting inter-channel correlation information through the integration of cross-channel conditional probabilities. The computation of similarities between sample points across different channels is recognized as a potential source of bias and instability in algorithms.Through simulation experiments, this study delineates the parameter selection range for the UMMSE algorithm. Subsequently, diverse simulation signals are employed to showcase the UMMSE algorithm’s efficacy in extracting both within-channel and cross-channel correlation information. Ultimately, this paper demonstrates that the new algorithm has the lowest computational cost compared with traditional MMSE algorithms.

A Study of the Aesthetic Application of Machine Learning to Five Education in Classical Music Interpretation

Abstract With the application of machine learning to the teaching of classical music in the context of five education as the research objective, this study first tested the feature extraction performance and classification of classical music with specific classical music data. Secondly, it explored the changes in the three elements of song aesthetics before and after machine learning five-education music teaching in two academic periods and analyzed the teaching effect it had on the task completion time and rhythmic accuracy of the songs. The use of audio analysis software in terms of task completion time, rhythmic accuracy, and pitch accuracy dimensions produces quantitative teaching effect ratings. The results show that the method in this paper obtains ideal classical music classification and feature extraction results and the ratings of the music feature extraction algorithms are in line with the real level of teachers and students. The experimental group and the control group, after teaching music using machine learning, took a similar time to learn the task, but the difference in rhythmic accuracy was large. The highest IOI value of the experimental group is only 0.07439, and the overall average of the group’s satisfaction with the teaching effect has increased by 0.842. This paper’s method is conducive to the development and innovation of music teaching.

Fast flow field prediction of three-dimensional hypersonic vehicles using an improved Gaussian process regression algorithm

Conducting large-scale numerical computations to obtain flow field during the hypersonic vehicle engineering design phase can be excessively costly. Although deep learning algorithms enable rapid flow field prediction with high-precision, they require a significant investment in training samples, contradicting the motivation of reducing the cost of acquiring flow field. The combination of feature extraction algorithms and regression algorithms can also achieve high-precision prediction of flow fields, which is more suitable to tackle three-dimensional flow prediction with a small dataset. In this study, we propose a reduced-order model (ROM) for the three-dimensional hypersonic vehicle flow prediction utilizing proper orthogonal decomposition to extract representative features and Gaussian process regression with improved automatic kernel construction (AKC-GPR) to perform a nonlinear mapping of physical features for prediction. The selection of variables is based on sensitivity analysis and modal assurance criterion. The underlying relationship is unveiled between flow field variables and inflow conditions. The ROM exhibits high predictive accuracy, with mean absolute percentage error (MAPE) of total field less than 3.5%, when varying altitudes and Mach numbers. During angle of attack variations, the ROM only effectively reconstructs flow distribution by interpolation with a MAPE of 7.02%. The excellent small-sample fitting capability of our improved AKC-GPR algorithm is demonstrated by comparing with original AKC-GPRs with a maximum reduction in a MAPE of 35.28%. These promising findings suggest that the proposed ROM can serve as an effective approach for rapid and accurate vehicle flow predicting, enabling its application in engineering design analysis.

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

Abstract With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.

An In-Situ Visual Analytics Framework for Deep Neural Networks.

The past decade has witnessed the superior power of deep neural networks (DNNs) in applications across various domains. However, training a high-quality DNN remains a non-trivial task due to its massive number of parameters. Visualization has shown great potential in addressing this situation, as evidenced by numerous recent visualization works that aid in DNN training and interpretation. These works commonly employ a strategy of logging training-related data and conducting post-hoc analysis. Based on the results of offline analysis, the model can be further trained or fine-tuned. This strategy, however, does not cope with the increasing complexity of DNNs, because (1) the time-series data collected over the training are usually too large to be stored entirely; (2) the huge I/O overhead significantly impacts the training efficiency; (3) post-hoc analysis does not allow rapid human-interventions (e.g., stop training with improper hyper-parameter settings to save computational resources). To address these challenges, we propose an in-situ visualization and analysis framework for the training of DNNs. Specifically, we employ feature extraction algorithms to reduce the size of training-related data in-situ and use the reduced data for real-time visual analytics. The states of model training are disclosed to model designers in real-time, enabling human interventions on demand to steer the training. Through concrete case studies, we demonstrate how our in-situ framework helps deep learning experts optimize DNNs and improve their analysis efficiency.

FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection.

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

Interior Space Design Method Considering Image Feature Extraction Algorithms

Interior Space Design Method Considering Image Feature Extraction Algorithms

Fluid classification through well logging is conducted using the extreme gradient boosting model based on the adaptive piecewise flatness-based fast transform feature extraction algorithm

In recent years, fluid prediction through well logging has assumed a pivotal role in the realm of oil and gas exploration. Seeking to enhance prediction accuracy, this paper introduces an adaptive piecewise flatness-based fast transform (APFFT) algorithm in conjunction with the XGBoost (extreme gradient boosting) method for logging fluid prediction. Initially, the APFFT technology is employed to extract frequency-domain features from the logging data. This algorithm dynamically determines the optimal frequency interval, transforming raw logging curves into frequency domain data. This adaptive process enhances the preservation of frequency domain information reflective of fluid characteristics, simultaneously minimizing the impact of noise and non-fluid compositions. Subsequently, the acquired frequency domain features are utilized as inputs to construct an XGBoost model for fluid prediction. To validate the efficacy of this proposed approach, real logging data were collected, and an extensive experimental evaluation was conducted. The experimental findings underscore the substantial advantages of the APFFT-XGBoost method over traditional machine learning models such as XGBoost, random forest, K-nearest neighbor algorithm, support vector machine, and backpropagation neural network in logging fluid prediction. The proposed method demonstrates the ability to accurately capture fluid features, leading to improved prediction accuracy and stability.

GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules

Data about vehicle trajectories assumes a crucial role in applications such as intelligent connected vehicles. However, missing values resulting from sensors and other factors frequently affect real trajectory data. Currently, it is challenging to utilize trajectory completion methods to generate accurate real-time results at an affordable computing cost. This paper proposes GNN-RM, a trajectory completion algorithm based on graph neural networks and regeneration modules, encompassing feature extraction, subgraph construction, spatial interaction graph, and trajectory regeneration modules. The feature extraction algorithm extracts influential data as feature vectors based on certain conditions and organizes these feature vectors into different subgraphs according to categories. The spatial interaction graph constructed through graph neural networks extracts spatial interaction features between vehicles and the environment, while the regeneration modules constructed by multi-head attention mechanisms extract temporal features of vehicles, thereby completing the missing trajectories. The experimental results demonstrate that GNN-RM can achieve higher trajectory completion accuracy with fewer input parameters than multiple baseline models.

Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

Research on the Generative Mechanism of Intelligent Reconstruction for the Integration of Traditional Paper-cutting Art Symbols and Modern Pattern Designs

Abstract In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

Image feature extraction algorithm based on visual information

Abstract Vision is the main sensory organ for human beings to contact and understand the objective world. The results of various statistical data show that more than 60% of all ways for human beings to obtain external information are through the visual system. Vision is of great significance for human beings to obtain all kinds of information needed for survival, which is the most important sense of human beings. The rapid growth of computer technology, image processing, pattern recognition, and other disciplines have been widely applied. Traditional image processing algorithms have some limitations when dealing with complex images. To solve these problems, some scholars have proposed various new methods. Most of these methods are based on statistical models or artificial neural networks. Although they meet the requirements of modern computer vision systems for feature extraction algorithms with high accuracy, high speed, and low complexity, these algorithms still have many shortcomings. For example, many researchers have used different methods for feature extraction and segmentation to get better segmentation results. Scale-invariant feature transform (SIFT) is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. A sparse coding algorithm is an unsupervised learning method, which is used to find a set of “super complete” basis vectors to represent sample data more efficiently. Therefore, combining SIFT and sparse coding, this article proposed an image feature extraction algorithm based on visual information to extract image features. The results showed that the feature extraction time of X algorithm for different targets was within 0.5 s when the other conditions were the same. The feature matching time was within 1 s, and the correct matching rate was more than 90%. The feature extraction time of Y algorithm for different targets was within 2 s. The feature matching time was within 3 s, and the correct matching rate was between 80 and 90%, indicating that the recognition effect of X algorithm was better than that of Y algorithm. It indicates the positive relationship between visual information and image feature extraction algorithm.

Four-axis dual-band error avoidance attitude measurement algorithm with adaptive active differential interference feature extraction

Infrared radiation attitude measurement technology is a new, non-inertial, satellite independent and fast-responding attitude parameters testing method. However, the random occurrence of infrared radiation during missile flight, such as clouds or solar radiation, can cause significant interference in infrared radiation attitude measurement. To enhance the anti-interference capability of infrared radiation attitude measurement, the adaptive active differential (AAD) interference feature extraction algorithm and the four-axis error avoidance (FEA) attitude calculation algorithm with dual-band infrared radiation (DIR) are proposed. Initially, the long-wave and mid-wave infrared radiation laws of clouds and solar radiation are analyzed to identify their different characteristics in both wavebands. The interference type encountered by the rotating missile during flight is extracted by the designed AAD algorithm. Subsequently, based on the results of AAD algorithm, the adjacent axis long-wave or mid-wave infrared sensor data with less or no interference influence is selected. Finally, the attitude angle information is obtained by using two-axis or four-axis attitude calculation algorithm. The results of the semi-physical experiments indicate that the four-axis dual-band infrared attitude measurement device using the FEA algorithm can achieve RMSE about 1 even under the interference of solar or clouds cover.