Isometric Mapping Research Articles

An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy

Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used rotating machinery fault diagnosis methods are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel nonlinear dynamics technique named the multivariate generalized refined composite multi-scale sample entropy was presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping was introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine was applied to carry out the intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various faults in both the bearings and gears. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it had higher recognition accuracy compared to other multi-channel or single-channel methods.

Comparative study of different dimensionality reduction methods in hyperspectral image classification

Considering the high-dimensional characteristics of hyperspectral image (HSI) data, researchers generally adopt the dimensionality reduction (DR) methods to reduce the complexity and computing time of subsequent classification or regression tasks while preserving the intrinsic structure information of the data. At present, the research on DR of HSIs data mostly focuses on the application performance of a single method in specific tasks and few studies have been conducted on the adaptability of different DR methods to HSIs data. From the perspective of spectral domain and spatial domain, this paper makes a comparative study on the performance of various linear and nonlinear DR methods in the task of HSI classification of aerial HSI of Matiwan Village in Xiongan New Area. Specifically, it includes principal component analysis, independent component analysis, isometric mapping, Laplacian eigenmaps, autoencoder, etc. The results show that the intrinsic structure in Xiongan HSI is mainly linear structure. Compared with the nonlinear DR methods, the linear DR methods can better preserve the intrinsic structure information of data at a lower time-consuming cost. For the subsequent classification tasks, the linear DR methods have better classification performance and are more suitable for HSI data.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends.

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.

Dimensionality reduction and classification for hyperspectral image based on robust supervised ISOMAP

ABSTRACT This study contributes to classification and recognition of hyperspectral image in industrial sorting, mineral identification, geological exploration, environmental survey, and other industrial production fields. The finding eliminates the redundancy of hyperspectral data and improves the classification accuracy of hyperspectral image. Hyperspectral remote sensing data are inherently nonlinear, and traditional linear dimensionality reduction methods cannot effectively reveal the nonlinear structure contained in the data. Manifold learning is a nonlinear dimensionality reduction method which has been developed rapidly in recent years. The intrinsic structure of the data is preserved by Isometric Feature Mapping (ISOMAP), but it is sensitive to noise and is not conducive to final classification. Prior studies neglected a higher dimensional manifold real structure in hyperspectral data dimensionality reduction due to lack of consideration credibility information and class information of samples in calculating manifold distance. This study proposes a Robust Supervised ISOMAP method. Data sample credibility mode is constructed, and a data similarity measure named triple geodesic distance is defined by introducing samples’ credibility information, class information, and neighborhood information. Triple geodesic distance fitting is proven. Multidimensional scaling analysis (MDS) and generalized regression neural network are used to construct the embedded coordinates of training samples and test samples. The results show that the proposed method has higher anti-noise performance than the traditional one. Also, the runtime is feasible, and can effectively improve the classification precision of the hyperspectral images, in comparison to traditional ISOMAP methods. The conclusion is that Robust Supervised ISOMAP is an effective nonlinear dimensionality reduction method for hyperspectral remote sensing image.

Random fields representation over manifolds via isometric feature mapping‐based dimension reduction

AbstractGenerating random fields over irregular geometries (e.g., two‐dimensional (2D) manifolds embedded in the three‐dimensional (3D) Euclidean space) is a great challenge because the geometry structure is complex and the correlation function can hardly be derived, thus the traditional methods, for example, spatial discretization methods or series expansion methods, cannot be directly adopted. To solve this issue, the present paper develops a two‐stage strategy to simulate random fields over manifolds. The core idea is to map the manifolds into the 2D Euclidean space through Isometric feature mapping (Isomap), with which the geodesic distance between points in the mapped 2D Euclidean space and the original manifold space is kept as the same. Therefore, the correlation function can be readily derived and the conventional methods can be directly employed to generate random fields. To validate the proposed method, several different types of manifolds are dimensionally reduced to 2D planar domains, and the stochastic harmonic function method is used to generate the random fields. Finally, case studies on the stochastic finite element analysis of two different structures are also performed to demonstrate the applicability and efficiency of the proposed method.

Detection of microsleep states from the EEG: a comparison of feature reduction methods.

Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep. High-dimensional datasets, especially in EEG-based classification, present challenges as there are often a large number of potentially useful features for detecting the phenomenon of interest. Thus, it is often important to reduce the dimension of the original data prior to training the classifier. In this study, linear dimensionality reduction methods-principal component analysis (PCA) and probabilistic PCA (PPCA)-were compared with eight non-linear dimensionality reduction methods (kernel PCA, classical multi-dimensional scaling, isometric mapping, nearest neighbour estimation, stochastic neighbourhood embedding, autoencoder, stochastic proximity embedding, and Laplacian eigenmaps) on previously collected behavioural and EEG data from eight healthy non-sleep-deprived volunteers performing a 1D-visuomotor tracking task for 1 h. The effectiveness of the feature reduction algorithms was evaluated by visual inspection of class separation on 3D scatterplots, by trustworthiness scores, and by microsleep detection performance on a stacked-generalisation-based linear discriminant analysis (LDA) system estimating the microsleep/responsive state at 1 Hz based on the reduced features. On trustworthiness, PPCA outperformed PCA, but PCA outperformed all of the non-linear techniques. The trustworthiness score for each feature reduction method also correlated strongly with microsleep-state detection performance, providing strong validation of the ability of trustworthiness to estimate the relative effectiveness of feature reduction approaches, in terms of predicting performance, and ability to do so independently of the gold standard. Graphical abstract Proposed microsleep detection system.

Semisupervised dynamic soft sensor based on complementary ensemble empirical mode decomposition and deep learning

Noise, redundancy, and dynamic characteristics in industrial process data have been regarded as the key factors that affect the measurement accuracy of data-driven soft sensors. In this paper, a semi-supervised dynamic soft sensor is proposed to capture the dynamic characteristics of data while removing noise and redundancy within the data, thus ensuring improved accuracy. Complementary ensemble empirical mode decomposition and isometric feature mapping are combined to reduce noise and redundancy. A semi-supervised deep learning model is designed to capture the dynamic characteristics. Compared with traditional soft sensors, the effectiveness and superiority of this method are verified via an experiment using an air preheater of a power boiler. The proposed method achieves the lowest MAE of 0.1745 and the highest correlation coefficient of 0.9969. Compared to methods without data preprocessing, the MAE of the preprocessed soft sensor decreases by 22.28% on average, while the correlation coefficient increases by 0.24% on average.

A positioning method for maize seed laser-cutting slice using linear discriminant analysis based on isometric distance measurement

It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput. Each of trivial maize seeds should be recognized and positioned in a certain posture. Correlation area ratio (CAR) is defined as the metric of pixel attribute. A large template of round mask is adopted for seed morphological detection to measure the CAR values. We get the feature points extracted from the seed image through the isometric mapping operation. Iterative processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly separable. Linear discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize seed. The labeling partition operation is applied for picking out the scattered pieces to be finely clustered. Without denoising process, the feature region could be recognized with accuracies by the synthetical methods. Extensive experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.

A novel hybrid method based on KELM with SAPSO for fault diagnosis of rolling bearing under variable operating conditions

It’s a great challenge to detect fault types from the measured vibration signals automatically and efficiently under variable running condition. A novel hybrid method based on simulated annealing particle swarm optimization (SAPSO) and improved kernel-based extreme learning machine (IKELM) is proposed for diagnosing rolling bearing faults under variable conditions in the paper. Multi-domain fault features from time domain, frequency domain and time–frequency domain are extracted and combined to reconstruct a high-dimensional feature collection by applying VMD, and isometric feature mapping is adopted to lower the dimension of the high-dimensional feature collection. The parameters of KELM are optimized to adapt for the fault feature identification by using SAPSO method. The proposed SAPSO-IKELM can be applied to diagnose the fault automatically and accurately from the two rolling bearing experiments. The results indicate SAPSO-IKELM could remove dependence on artificial feature selection and reach high classification accuracy under varying conditions.

Generalized Notions of Sparsity and Restricted Isometry Property. Part II: Applications

Restricted isometry property (RIP) provides a near isometric map for sparse signals. RIP of structured random matrices has played a key role for dimensionality reduction and recovery from compressive measurements. In a companion paper, we have developed a unified theory for RIP of group structured measurement operators on generalized sparsity models. The implication of the extended result will be further discussed in this paper in terms of its pros and cons over the conventional theory. We first show that the extended RIP theory enables the optimization of sample complexity over various relaxations of the canonical sparsity model. Meanwhile, the generalized sparsity model is no longer described as a union of subspaces. Thus the sparsity level is not sub-additive. This incurs that RIP of double the sparsity level does not imply RIP on the Minkowski difference of the sparsity model with itself, which is crucial for dimensionality reduction. We show that a group structured measurement operator provides an RIP-like property with additive distortion for non-sub-additive models. This weaker result can be useful for applications like locality-sensitive hashing. Moreover, we also present that the group structured measurements with random sign enables near isometric sketching on any set similar to the Gaussian measurements. Lastly, an extension of theory to infinite dimension is derived and illustrated over selected examples given by Lebesgue measure of support and Sobolev seminorms.

Floristic patterns of the neotropical forests, savannas and scrublands with Trithrinax campestris (Arecaceae) in central Argentina

Aims: Trithrinax campestris is one of the palm species with the southernmost distribution in the Neotropics. Despite that the vegetation types in which T. campestris occurs are nowadays heavily threatened by land use and land cover changes, their floristic composition and structure are still to be documented. In order to characterize T. campestris habitats, the aim of this study was to describe the floristic composition of the vegetation types in which this palm occurs and their relationships with different environmental factors. Study area: The survey was conducted in central Argentina in an area comprising the southern extreme of the distribution of T. campestris in the following phytogeographic areas: Espinal, Lowland and Mountain Chaco. Methods: Following the Braun-Blanquet approach we collected 92 floristic relevés recording a total of 601 vascular plant species. Vegetation was classified through the ISOPAM hierarchical analysis. Bioclimatic and elevation data were related to the floristic data through the ISOMAP ordination. Remote-sensed images (Landsat TM, ETM+ and OLI) were used to characterize the fire frequency in the 92 stands. Results: Four vegetation types that differed in floristic composition and in diagnostic species were discriminated: 1.1 Celtis tala/Sida rhombifolia closed forest; 1.2 Aspidosperma quebracho-blanco/Prosopis kuntzei open forest; 2.1 Jarava pseudoichu/Vachellia caven open savanna; and 2.2 Acalypha variabilis/Nassella cordobensis scrubland. The ISOMAP ordination showed that differences in floristic composition were related to elevation, topography and climatic variables.Out of the 92 stands, only 21 showed the occurrence of fires during the period 1999–2018. Conclusions: Our results evidenced that vegetation types (forests, savannas and scrublands) comprising T. campestris developed in a wide range of environmental conditions. This is the first study that focuses on all vegetation types in which T. campestris occurs in central Argentina and it is relevant for conservation and sustainable management of the only native palm species in the flora of this part of the country. Taxonomic reference: Catálogo de las Plantas Vasculares del Cono Sur (Zuloaga et al. 2008) and its online update (http://www.darwin.edu.ar). Abbreviations: ISOMAP = isometric feature mapping; ISOPAM = isometric partitioning around medoids.

Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.

Deep reconstruction of 1D ISOMAP representations

This paper proposes a deep learning priors-based data reconstruction method of 1D isometric feature mapping (ISOMAP) representations. ISOMAP is a classical algorithm of nonlinear dimensionality reduction (NLDR) or manifold leaning (ML), which is devoted to questing for the low dimensional structure of high dimensional data. The reconstruction of ISOMAP representations, or the inverse problem of ISOMAP, reestablishes the high dimensional data from its low dimensional ISOMAP representations, and owns a bright future in data representation, generation, compression and visualization. Due to the fact that the dimension of ISOMAP representations is far less than that of the original high dimensional data, the reconstruction of ISOMAP representations is ill-posed or undetermined. Hence, the residual learning of deep convolutional neural network (CNN) is employed to boost reconstruction performance, via achieving the priors between the low-quality result of general ISOMAP reconstruction method and its residual relative to the original data. In the situation of 1D representations, it is evaluated by the experimental results that the proposed method outbalances the state-of-the-art methods, such as nearest neighbor (NN), discrete cosine transformation (DCT) and sparse representation (SR), in reconstruction performance of video data. In summary, the proposed method is suitable for low-bitrate and high-performance applications of data reconstruction.

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. In the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

Reaction coordinates by nonlinear dimensionality reduction

Deriving reaction coordinates for the characterization of chemical reactions has long been a demanding task. In our previous work [ACS Cent. Sci. 3, 407 (2017)], the reaction coordinate of a (retro-) Claisen rearrangement in aqueous solution optimized through a Bayesian measure, a linear combination of bond lengths formation and breakage, was judged to be optimal among all trails. Here, considering the nonlinearity of the transition state, we use isometric mapping and locally linear embedding to obtain one reaction coordinate which is composed of a few collective variables. With these methods, we find a more reasonable and powerful one-dimensional reaction coordinate, which can well describe the reaction progression. To explore the reaction mechanism, we analyze the contribution of intrinsic molecular properties and the solvent-solute interactions to the nonlinear reaction coordinate. Furthermore, another coordinate is identified to characterize the heterogeneity of reaction mechanisms.

Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy.

The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with either sequential feature selection (SFS) or dimensionality reduction methods, for classifying soil with laser-induced breakdown spectroscopy (LIBS) had been explored in this study. Each algorithm combination was compared to assess their classification performance. After eliminating the irrelevant features of the data using sequential feature selection (SFS), the performances were all improved for the studied four classification models, and the best accuracy reached 97.88% by SFS-SVM. The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition (TSVD), and isometric mapping (Isomap), respectively. The classification accuracy improved for all combination models with dimensionality reduction, and impressive accuracies of 98.12% from TSVD-SVM and 98.24% from VAE-SVM were obtained. These results demonstrate an effective way to reduce uncorrelated features, high dimensionality, and redundant information in the LIBS dataset. In addition, coupling classification models with feature selection and dimensionality reduction techniques could significantly optimize the classification performance of LIBS.

Flow Regimes Identification-based Multidomain Features for Gas–Liquid Two-Phase Flow in Horizontal Pipe

Accurate flow regimes identification is of great significance for the deep understanding of the flow structure and ensuring the safe and economic operation of actual production in the multiphase flow process. For the flow regimes identification of the gas-liquid two-phase flow in the horizontal pipe, a multidomain features processing scheme is proposed. The water holdup data are acquired by conductance rings sensor, and processed by the improved empirical wavelet transform (EWT) to obtain the Fourier spectrum in the frequency domain and decomposition components in the multiscale domain. The obtained multidomain feature information is quantified to get the high-dimensional feature vectors of different flow regimes. To make the features more representative and realize the visualization of the structural relationship between the flow regime samples, the isometric feature mapping (Isomap) method in manifold learning is used to synthesize the feature structure relationships and map them to low-dimensional space. The obtained low-dimensional feature vectors of training samples are input into the support vector machine (SVM) to acquire the flow regimes identification model. Compared with other flow regimes identification schemes that feature reduction uses different manifold learning methods, or feature extraction is not in multidomain, or data preprocessing uses traditional wavelet transform, the proposed scheme can better retain the global structural relationship of the flow regimes, has the characteristics of strong stability, and the identification rate of the gas-liquid two-phase flow regimes in the horizontal pipe is over 95%.

Mode-specific dynamics in multichannel reaction NH+ + H2.

The vibrational- and rotational-mode specificity of the multichannel NH+ + H2 reaction is studied on a recently constructed ab initio-based global potential energy surface using an initial state selected quasi-classical trajectory method, and the trajectories are analyzed using an isometric feature mapping and k-means approach. All excitation modes promote two reactions (R1: NH'+ + H2 → NH+ + HH' and R4: NH'+ + H2 → NH2+ + H') where both NH and HH bonds are broken, but reduce the reactivity of the proton-transfer reaction R2 (NH'+ + H2 → N + H'H2+) at low collision energies. For the hydrogen-transfer reaction R3 (NH'+ + H2 → HNH'+ + H), the rotational excitation of NH+ enhances the reactivity remarkably, while its vibrational excitation has an inhibiting effect on the reaction. The trajectory analyses show that the vibrational and rotational excitations of NH+ make R3 tend to go over a submerged saddle point instead of extracting hydrogen atoms directly. On the other hand, the motions of the H2 reactant facilitate the enhancement of the reactivity but they do not affect the mechanism of R3. In addition, the results suggest that the coupling of the isometric feature mapping and the k-means approach in the trajectory analysis is an appropriate tool for reaction-dynamics studies.

Automated Sustainable Multi-Object Segmentation and Recognition via Modified Sampling Consensus and Kernel Sliding Perceptron

Object recognition in depth images is challenging and persistent task in machine vision, robotics, and automation of sustainability. Object recognition tasks are a challenging part of various multimedia technologies for video surveillance, human–computer interaction, robotic navigation, drone targeting, tourist guidance, and medical diagnostics. However, the symmetry that exists in real-world objects plays a significant role in perception and recognition of objects in both humans and machines. With advances in depth sensor technology, numerous researchers have recently proposed RGB-D object recognition techniques. In this paper, we introduce a sustainable object recognition framework that is consistent despite any change in the environment, and can recognize and analyze RGB-D objects in complex indoor scenarios. Firstly, after acquiring a depth image, the point cloud and the depth maps are extracted to obtain the planes. Then, the plane fitting model and the proposed modified maximum likelihood estimation sampling consensus (MMLESAC) are applied as a segmentation process. Then, depth kernel descriptors (DKDES) over segmented objects are computed for single and multiple object scenarios separately. These DKDES are subsequently carried forward to isometric mapping (IsoMap) for feature space reduction. Finally, the reduced feature vector is forwarded to a kernel sliding perceptron (KSP) for the recognition of objects. Three datasets are used to evaluate four different experiments by employing a cross-validation scheme to validate the proposed model. The experimental results over RGB-D object, RGB-D scene, and NYUDv1 datasets demonstrate overall accuracies of 92.2%, 88.5%, and 90.5% respectively. These results outperform existing state-of-the-art methods and verify the suitability of the method.

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms.

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.