Confusion Problems Research Articles

Clinical characteristics and treatment management of combined large cell neuroendocrine carcinoma, a subtype of large cell neuroendocrine carcinoma.

Combined large cell neuroendocrine carcinoma (CLCNEC) is a rare neuroendocrine carcinoma, accounting for approximately 10% of large cell neuroendocrine carcinoma (LCNEC). Mainly composed of coexisting adenocarcinoma components, with strong invasiveness and poor prognosis. The treatment regimen for CLCNEC mainly refers to complete surgical resection as the first choice in the early stage, while patients with stage II or higher require adjuvant treatment. At present, research on CLCNEC is mostly small sample and retrospective, and there is no consensus on whether molecular typing and treatment should be carried out. There is considerable controversy over whether it should be managed as small-cell lung cancer (SCLC) or non-small-cell lung cancer (NSCLC). Therefore, in order to solve the problem of confusion in the selection of treatment regimens for CLCNEC, while also considering the therapeutic effects, this article summarizes and analyzes previous studies, fully seeks evidence, and boldly proposes new therapeutic insights: the etoposide-platinum (EP) regimen serves as the basis for adjuvant therapy; In addition, SCLC/NSCLC-CLCNEC can be distinguished based on presence of RB1 and TP53 co-mutation, and targeted therapy or NSCLC type chemotherapy including platinum + gemcitabine or taxanes (NSCLC-GEM/TAX) can be used in combination or sequentially for NSCLC-CLCNEC.

AMVFNet: Attentive Multi-View Fusion Network for 3D Object Detection

Pillar-based method is significant in the field of LiDAR-based 3D object detection which could directly make use of efficient 2D backbone and save computational resources during reference. Existing methods usually sequentially project the original point clouds into the cylindrical view or the bird-eye view for feature extraction. However, the former suffers from obscured problems and the scales of instances vary greatly with distance, while the latter leads to considerable confusion problems due to the loss of semantic information caused by the sparsity of the projected point cloud. In this paper, we present a novel and efficient two-stage point-pillar hybrid architecture named Attentive Multi-View Fusion Network (AMVFNet), in which we abstract features from all cylindrical view, bird-eye view, and raw point clouds. Rather than designing more complex modules to solve the problems inherent in the single-view approach, our multi-view fusion architecture effectively combines the strengths of multiple perspectives to improve performance at a more fundamental level. Besides, to compensate for quantization distortion caused by projection operations, we propose attentive feature enhancement layers to further improve the capability of contextual information capturing. Extensive experiments on the KITTI detection benchmark illustrate that our proposed AMVFNet achieves competitive performance compared with other SOTA 3D object detectors.

Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation

This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms, historical attention information, dynamically obtain information related to the generated words, and constraint mechanisms were introduced to embed semantic information, represent polysemy, and annotate semantic roles of verbs. This article used the Workshop on MT (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, and OpenSubtitles corpus, and enhanced the data in the corpora. The improved NMT model was compared with traditional NMT models, Rule-Based MT (RBMT), and Statistical MT (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in five corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in five corpora was 0.90. The improved NMT model in this article can effectively improve the accuracy of verb recognition in MT, providing new methods for verb recognition in MT.

Effective multispike learning in a spiking neural network with a new temporal feedback backpropagation for breast cancer detection

This paper presents an effective learning multi-spike deep spiking neural network with temporal feedback backpropagation for breast cancer detection using contrast-enhanced MRI images. The learning in the spiking network is a new universal temporal feedback called Temporal_Feedback_SpikeProp (TF_SpikeProp) and Temporal_Feedback_ReSuMe (TF_ReSuMe). Thus, it can be implemented on all kinds of algorithms such as MultiSpikeProp and MultiReSuMe algorithms and is compatible with all temporal codings. The presented spiking network is a functional network with high accuracy and convergence in low epochs. The new algorithm explores the influence of all presynaptic neurons on the output error and reduces the role of inactive neurons. Therefore, the error propagation of the spiking network is such that it affects not only the spike time of each neuron in the output spike time, but also the refractory time and the presynaptic spikes of the neuron during the spiking time of the output neuron. The spiking network can correct and adjust the weights, spike delay, spike threshold, and also the time constant of the spike collection kernel. In order to diagnose cancer tissue, time–frequency features such as STFT and packet wavelet transform (WPT) have been used along with texture recognition features such as co-occurrence matrix. The presented network achieved an accuracy of 98.3% in 23 epochs in SpikeProp algorithm, 97.4% in ReSuMe with Duke MRI dataset, 98.64% on MNIST, and 96.1% on Iris. The results have shown that the training algorithm used in this study can achieve high accuracy in low epochs compared to traditional algorithms, while solving the challenge of getting stuck in local minima and poor convergence, as well as reducing the problem of time confusion of output spikes to an acceptable level and providing a fully functional network in practice and reality.

A Few-Shot Object Detection Method for Endangered Species

Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples, and building such endangered species datasets is usually costly. Aiming at the problems faced by endangered species detection, such as low accuracy and easy loss of location information, an efficient endangered species detection method with fewer samples is proposed to extend the few-shot object detection technique to the field of endangered species detection, which requires only a small number of training samples to obtain excellent detection results. First, SE-Res2Net is proposed to optimize the feature extraction capability. Secondly, an RPN network with multiple attention mechanism is proposed. Finally, for the classification confusion problem, a weighted prototype-based comparison branch is introduced to construct weighted category prototype vectors, which effectively improves the performance of the original classifier. Under the setting of 30 samples in the endangered species dataset, the average detection accuracy value of the method, mAP50, reaches 76.54%, which is 7.98% higher than that of the pre-improved FSCE method. This paper also compares the algorithm on the PASCOL VOC dataset, which is optimal and has good generalization ability compared to the other five algorithms.

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.

Developing An ITS Based Solution Proposal for Confusion Problems in Traffic During the Transition of Emergency Vehicles

Artan nüfus ve araç sayısı şehiriçi yollarda tıkanıklık ve karmaşaya neden olabilmektedir. Özellikle trafiğin kesişim bölgesi olan kavşak kesimlerinde bu sorun daha belirgin olmaktadır. Kavşaklarda yaşanan karmaşa acil durum müdahale araçlarının olay yerlerine hızlı erişimini olumsuz etkilemektedir. Bu araçların müdahalede gecikmesi nedeniyle kayıplar daha da artmaktadır. Çalışmada, dijital sistemler, sensörler ve lazer ışıklandırma yardımıyla kavşak yaklaşımlarında acil durum araçlarının kullanabileceği bir sanal şerit oluşumunu sağlayan akıllı bir sistem kavramsal olarak tasarlanmıştır. Bu sistem yardımıyla lazer ışık kümesi ile belirlenen yol kesimi üzerinde bir sanal şerit oluşturularak araçların hızlı hareketlerine yönelik tüm adımlar tasarlanmış ve tasarım beş kişilik bir uzman ekip tarafından değerlendirilmiştir. İlk olarak SWOT analizi ile belirlenen faktörler uzman görüşü sayesinde ikili olarak karşılaştırılmış ve AHP metodu ile ağırlıklandırılmıştır. Uygulanan A’WOT tekniği sonuçlarından sistemin araçların hızlı hareketini sağlaması en güçlü yön, sürücülerin sanal şeridi kullanma olasılıkları en zayıf yön ve Türkiye’deki sürücülerin bu tür yenilikleri kabul etme eğiliminin düşük olması da sistemin önünde en önemli tehdit olarak belirlenmiştir. Son yıllarda acil müdahale gerektiren olayların sayısının ve sıklığının artması, sistemlerin uygulanmasında önemli fırsatlar oluştuğunu net olarak göstermekte ve çalışmanın amacını desteklemektedir.

Multi-task global optimization-based method for vascular landmark detection

Vascular landmark detection plays an important role in medical analysis and clinical treatment. However, due to the complex topology and similar local appearance around landmarks, the popular heatmap regression based methods always suffer from the landmark confusion problem. Vascular landmarks are connected by vascular segments and have special spatial correlations, which can be utilized for performance improvement. In this paper, we propose a multi-task global optimization-based framework for accurate and automatic vascular landmark detection. A multi-task deep learning network is exploited to accomplish landmark heatmap regression, vascular semantic segmentation, and orientation field regression simultaneously. The two auxiliary objectives are highly correlated with the heatmap regression task and help the network incorporate the structural prior knowledge. During inference, instead of performing a max-voting strategy, we propose a global optimization-based post-processing method for final landmark decision. The spatial relationships between neighboring landmarks are utilized explicitly to tackle the landmark confusion problem. We evaluated our method on a cerebral MRA dataset with 564 volumes, a cerebral CTA dataset with 510 volumes, and an aorta CTA dataset with 50 volumes. The experiments demonstrate that the proposed method is effective for vascular landmark localization and achieves state-of-the-art performance.

DNA barcoding, identification, and validation of the pufferfish (Order: Tetraodontiformes) in China coastal waters.

The order Tetraodontiformes are one of the most unique groups of teleostean fish, exhibiting highly derived and greatly diversified phenotypes. It is a difficult task for both professionals and nonprofessionals to accurately identify these species only according to morphological characteristics. DNA barcoding can identify species at the molecular level to overcome the limitations of morphological classification. In this study, we collected 616 specimens of pufferfish from the coastal waters of China. According to the morphological characteristics, they were preliminarily identified as 50 species. Further analysis using DNA barcodes identified these specimens as 46 species, belonging to 23 genera, 6 families. According to the species classification results of DNA barcoding, the three species identified by morphology as Takifugu pseudommus, Takifugu chinensis, and Takifugu rubripes should be the same species. Similarly, Lagocephalus wheeleri is the synonym of Lagocephalus spadiceus. Another important discovery of DNA barcoding analysis is that there are closer interspecific genetic distances within the genus Takifugu. If T. rubripes, T. pseudommus, and T. chinensis are taken as one species, the average interspecific to intraspecific genetic distance ratio of Takifugu is only 6.21 times, which does not reach the DNA barcoding threshold of more than 10 times proposed previously. Although the interspecific genetic distance in the genus Takifugu is relatively small, each species can be clustered into independent clades in the NJ tree. In conclusion, this study not only found that there are synonymous phenomena in the order Tetraodontiformes but also provided molecular evidence for the valid species names of Takifugu rubripes and Lagocephalus Spadiceus. The results can provide reliable DNA barcoding information for the identification of pufferfish species, help solve the problem of classification confusion in this order, and provide technical support for the identification of the original components of related commodities on the aquatic product market.

Hierarchy of identities In modern humanitarian discourse

The article examines the problems of terminological confusion in matters of identity and their impact on the social and political atmosphere in societies. The purpose of the article is the study of the modern discourse on the problem of identity, the analysis of filling the concept of identity with content, the construction of a semantic "chain" in the hierarchy of identities. The methods used in the article are the historical method, the systemic method, and discourse analysis. In the results and discussion, positions of the relationship of different types of identity are given. Thus, analyzing ethnic and national identity, where it is proven that ethnic identity has a socio-cultural nature, and a national-political-legal one. The regional and civilizational subsections emphasize that the concepts of regional and civilizational identity are a pair of bipolar categories that show a person's place in the time-space cultural and political continuum. When considering civic and political identity, their similarities and differences are emphasized, and various degrees of maturity of civic identity are described: normal, deformed, and deficient. The conclusions emphasize the importance of the right identity policy, which the state should conduct. The following chain in the hierarchy of identities is proposed: the first is individual, the second is regional, the third is ethnic, the fourth is national, and the fifth is civilizational. The relationship of identities is an ensemble in which each identity can be defined depending on the orientation of the individual.

ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging.

By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, existing algorithms cannot adapt well to computing scenarios with limited computing power, such as portable sleep detection and consumer-level sleep disorder screening. In addition, existing algorithms still have the problem of N1 confusion. To address these issues, we propose an efficient sleep sequence network (ESSN) with an ingenious structure to achieve efficient automatic sleep staging at a low computational cost. A novel N1 structure loss is introduced based on the prior knowledge of N1 transition probability to alleviate the N1 stage confusion problem. On the SHHS dataset containing 5,793 subjects, the overall accuracy, macro F1, and Cohen's kappa of ESSN are 88.0%, 81.2%, and 0.831, respectively. When the input length is 200, the parameters and floating-point operations of ESSN are 0.27M and 0.35G, respectively. With a lead in accuracy, ESSN inference is twice as fast as L-SeqSleepNet on the same device. Therefore, our proposed model exhibits solid competitive advantages comparing to other state-of-the-art automatic sleep staging methods.

Relational Part-Aware Learning for Complex Composite Object Detection in High-Resolution Remote Sensing Images.

In high-resolution remote sensing images (RSIs), complex composite object detection (e.g., coal-fired power plant detection and harbor detection) is challenging due to multiple discrete parts with variable layouts leading to complex weak inter-relationship and blurred boundaries, instead of a clearly defined single object. To address this issue, this article proposes an end-to-end framework, i.e., relational part-aware network (REPAN), to explore the semantic correlation and extract discriminative features among multiple parts. Specifically, we first design a part region proposal network (P-RPN) to locate discriminative yet subtle regions. With butterfly units (BFUs) embedded, feature-scale confusion problems stemming from aliasing effects can be largely alleviated. Second, a feature relation Transformer (FRT) plumbs the depths of the spatial relationships by part-and-global joint learning, exploring correlations between various parts to enhance significant part representation. Finally, a contextual detector (CD) classifies and detects parts and the whole composite object through multirelation-aware features, where part information guides to locate the whole object. We collect three remote sensing object detection datasets with four categories to evaluate our method. Consistently surpassing the performance of state-of-the-art methods, the results of extensive experiments underscore the effectiveness and superiority of our proposed method.

Adaptive Feature Learning for Unbiased Scene Graph Generation.

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships in the scene. Recently, tremendous progress has been made in exploring better context relationship representations. Previous work mainly focuses on contextual information aggregation and uses de-biasing strategies on samples to eliminate the preference for head predicates. However, there remain challenges caused by indeterminate feature training. Overlooking the label confusion problem in feature training easily results in a messy feature distribution among the confused categories, thereby affecting the prediction of predicates. To alleviate the aforementioned problem, in this paper, we focus on enhancing predicate representation learning. Firstly, we propose a novel Adaptive Message Passing (AMP) network to dynamically conduct information propagation among neighbors. AMP provides discriminating representations for neighbor nodes under the view of de-noising and adaptive aggregation. Furthermore, we construct a feature-assisted training paradigm alongside the predicate classification branch, guiding predicate feature learning to the corresponding feature space. Moreover, to alleviate biased prediction caused by the long-tailed class distribution and the interference of confused labels, we design a Bi-level Curriculum learning scheme (BiC). The BiC separately considers the training from the feature learning and de-biasing levels, preserving discriminating representations of different predicates while resisting biased predictions. Results on multiple SGG datasets show that our proposed method AMP-BiC has superior comprehensive performance, demonstrating its effectiveness.

企劃論文] 영창서관 도서목록 수록 현대소설의 양상

This article explored the publishing trend of the Yeongchang Bookstore based on the New Book List in 1930s. Among them, the purpose was to examine the characteristic aspects of works classified as ‘literary novels’. The New Book List can be seen as a selection of bestsellers or steady sellers who gained popularity among novels published by the Yeongchang Bookstore. The Yeongchang Bookstore is a publisher closely related to sixpence novels. The reason why actual works have not been confirmed among the publications of the Yeongchang Bookstore is related to the background of many unknown publications. In addition, various issues related to the publishing environment, such as authors, copyrights, and censorship, affected the spread and distribution of sixpence popular novels. It is also necessary to consider the relevance of the literary situation of the time, when the artistic identity of the sixpence popular novel was relatively not recognized. And the problem of confusion in the notation of author names is also revealed. This can be said to be a problem caused by the coexistence of modernity and premodernity in the creation and distribution process of sixpence popular novels. The copyright reached them, but the subject of protection was not the actual author. Most of the damage caused by copyright issues was concentrated on sixpence popular novels.

Progressively Multi-Scale Feature Fusion for Image Inpainting

The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient’s tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network’s attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively.

Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions.

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

Circulant Singular Spectrum Analysis (CiSSA) performs well in the decomposition and extraction of the periodic components of nonstationary signals. However, the decomposition of signals in complex environments suffers from spectral aliasing and difficulties in extracting feature information. Therefore, based on CiSSA, an improved CiSSA and multipoint optimal minimum entropy deconvolution adjustment (ICiSSA-MOMEDA) is proposed and applied to the early faint fault diagnosis of axlebox bearings of urban rail train wheelsets. First, the optimal embedding dimension was computed adaptively and accurately using an improved Cao’s method. Then, the initial components obtained from the decomposition were reorganized using the K-ARs method. ICiSSA effectively solves the problems of spectrum confusion and fault-information dispersion. Finally, ICiSSA was combined with MOMEDA to improve its ability to detect weak fault information. The superiority of ICiSSA-MOMEDA was verified based on the analysis of the actual bearing data and comparison with other methods.

CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.

CrossTLNet: A Multitask-Learning-Empowered Neural Network with Temporal Convolutional Network–Long Short-Term Memory for Automatic Modulation Classification

Amidst the evolving landscape of non-cooperative communication, automatic modulation classification (AMC) stands as an essential pillar, enabling adaptive and reliable signal processing. Due to the advancement of deep learning (DL) technology, neural networks have found application in AMC. However, the previous DL models face the inter-class confusion problem in high-order modulations. To address this issue, we propose a multitask-learning-empowered hybrid neural network, named CrossTLNet. Specifically, after the signal enters the model, it is first transformed into two task components: in-phase/quadrature (I/Q) form and amplitude/phase (A/P) form. For each task, we design a method that combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network to effectively capture long-term dependency features in high-order modulations. To enable interaction between these two different dimensional features, we innovatively introduce a cross-attention method, thereby further enhancing the model’s ability to distinguish signal features. Moreover, we also design a simple and efficient knowledge distillation method to reduce the size of CrossTLNet, making it easier to deploy in real-time or resource-limited scenarios. The experimental results indicate that the suggested method exhibits exceptional performance in AMC on public benchmarks, especially in high-order modulations.

Trend recurrence analysis and time series classification via trend fuzzy granular recurrence plot method

The recurrence plot (RP) method proposed by Eckmann et al. is a powerful tool for visualizing the recurrent states of a system and is successfully applied in time series classification. However, the traditional RP method is easily affected by noise, and cannot effectively express trend differences between time series especially opposite trends. To solve these problems, this paper proposes a method of trend fuzzy granular recurrence plot (TFGRP). The TFGRP method is implemented at the level of granular time series, which integrates linear fuzzy information granulation into time series recursive analysis. It can not only reduce the effect of noise on the signal but also address the trend confusion problem. Based on the TFGRP method, we combined the support vector machine (SVM) to propose a time series classification model named as TFGRP-SVM. The experimental results demonstrate that the TFGRP method can reduce the influence of noise on data and the TFGRP-SVM model has superior classification performance.