Class Information Research Articles

Machine Learning for Automated Oil Palm Fruit Grading: The Role of Fuzzy C-Means Segmentation and Textural Features

Oil palm fruit, a high-demand and economically valuable crop, faces grading challenges in Malaysia due to labour-intensive methods, resulting in unharvested ripe fruits and yield losses. Extensive research has been conducted on outdoor palm fruit classifiers, but most high-accuracy research in this field relies heavily on large image datasets. This creates a trade-off between employing more sophisticated deep-learning approaches that require extensive data and utilising traditional techniques that have lower data requirements. Therefore, the aim is to propose an outdoor image-based fresh fruit bunch (FFB) classification system, emphasising pre-processing technique and feature extraction techniques suitable for limited datasets. To achieve this, the FFB was localised within the original images using the YOLOv4-Tiny algorithm. Subsequently, a salient segmentation method utilising superpixel-based Fast Fuzzy C-means (FFCM) clustering is employed to remove the background from the images. Next, colour moment and opposite colour local binary pattern (OCLBP) features are extracted from the segmented images to capture important information for classification. Finally, the extracted features are fed into a Multilayer Perceptron (MLP) classifier, which enables the system to predict five classes: damaged, empty, unripe, ripe, and overripe. The developed system demonstrated a commendable performance in accurately classifying fruit bunches, achieving an accuracy of 93.68%. In conclusion, the proposed system effectively addresses the issue of unripe fruit harvesting and contributes to the advancement of state-of-the-art methods in classifying outdoor FFB images.

Deep one-class classification model assisted by radius constraint for anomaly detection of industrial control systems

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning technique, deep support vector data description (DeSVDD) has been successfully applied to ICS anomaly detection. Its advantage lies in only requiring normal data to train one-class classifier, which is suitable for actual industrial scenarios with extremely data imbalance of abundant normal data and scare abnormal data. However, this also results in its shortcoming of ignoring the valuable classification information hidden in the abnormal data. In order to overcome this issue, this paper proposes an improved DeSVDD method, called radius constraint DeSVDD (RC-DeSVDD). The proposed model constructs an anomaly detection model framework of abnormal data assisted DeSVDD, where a radius constraint is designed by considering the difference between normal and abnormal data. Further, considering the limited amount of abnormal data, a bi-directional generative adversarial network (BiGAN) is introduced to generate abnormal data. Experimental results on three benchmark datasets demonstrate the superiority of the proposed RC-DeSVDD anomaly detection method.

An Improved DETR Based on Angle Denoising and Oriented Boxes Refinement for Remote Sensing Object Detection

Remote sensing image object detection presents significant challenges, due to the difficulty in accurately predicting the rotational angles of ground-oriented objects, coupled with issues such as the false and missed detection caused by insufficient object information. Moreover, traditional convolutional neural networks are inherently limited in their capacity to capture global contextual information. To address these challenges, a DETR-based remote sensing image object detection model is designed for oriented objects. Except for the backbone, transformer encoders and decoders, scenario query guiding modules, oriented boxes refinement modules, auxiliary multiple detectors, and oriented boxes denoising modules are also designed and included in our network. The scenario query guiding module is proposed that implicitly guides the decoder to focus more on object classification information specific to that scene when inferring. The multiple deformable attention mechanism is improved to the oriented one and utilized into the oriented boxes refinement module which repeatedly corrects the oriented boxes, enhancing the network’s ability to predict the oriented boxes precisely. At the same time, the improved auxiliary multiple detectors and oriented boxes denoising module are applied only for the training process to enhance the learning ability of the encoder and decoder for oriented objects. The ablation experiments proved the effectiveness of the designed modules. The detection accuracy of our network on DOTAv1.0 (76.77%) and HRCS2016 (97.01%) is improved compared with the state-of-the-art methods, which are especially significantly higher than DETR detection algorithms.

A cross-language short text classification model based on BERT and multilayer collaborative convolutional neural network (MCNN)

This study focuses on cross-lingual short text classification tasks and aims to combine the advantages of BERT and Multi-layer Collaborative Convolutional Neural Network (MCNN) to build an efficient classification model. BERT model provides rich semantic information for text classification with its powerful language understanding and bidirectional context modeling ability, while MCNN effectively extracts local and global features in text through multi-layer convolution structure and collaborative working mechanism. In this study, the output of BERT is used as the input of MCNN, and MCNN is used to further mine the deep features in the text, so as to realize the high-precision classification of cross-lingual short text. The experimental results show that the model has achieved significant performance improvement on the dataset, which provides a new effective solution for cross-lingual short text classification tasks.

Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda

Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through imputation. Imputation methods amelia, hmisc, mice and missForest were evaluated. Simulations were conducted across various scenarios using the complete data to guide imputation for the full dataset. A random forest model was used to predict HIV status, assessing imputation precision, overall prediction accuracy, and sensitivity. While missForest produced imputed values closer to the observed ones, this did not translate into better predictive models. Hmisc and mice imputations led to higher prediction accuracy and sensitivity, with median accuracy increasing from 64% to 76% and median sensitivity rising from 0.4 to 0.75. Hmisc and amelia were the fastest imputation methods. Additionally, oversampling the minority class combined with undersampling the majority class did not improve predictions of new HIV positive cases using only the complete observations. However, increasing the minority class information through imputation enhanced sensitivity for predicting cases in this class.

Chloroplast genome of four Amorphophallus species: genomic features,comparative analysis, and phylogenetic relationships among Amorphophallus species

BackgroundThe genus Amorphophallus (Araceae) contains approximately 250 species, most of which have high ecological and economic significance. The chloroplast genome data and the comprehensive analysis of the chloroplast genome structure of Amorphophallus is limited. In this study, four chloroplast genomes of Amorphophallus were sequenced and assembled. For the first time, comparative analyses of chloroplast genomes were conducted on the 13 Amorphophallus species in conjunction with nine published sequences.ResultsThe Amorphophallus chloroplast genomes exhibited typical quadripartite structures with lengths ranging from 164,417 to 177,076 bp. These structures consisted of a large single copy (LSC, 90,705 − 98,561 bp), a small single copy (SSC, 14,172 − 21,575 bp), and a pair of inverted repeats (IRs, 26,225 − 35,204 bp). The genomes contain 108 − 113 unique genes, including 76 − 79 protein-coding genes, 28 − 29 tRNA genes, and 4 rRNA genes. The molecular structure, gene order, content, codon usage, long repeats, and simple sequence repeats (SSRs) within Amorphophallus were generally conserved. However, several variations in intron loss and gene expansion on the IR-SSC boundary regions were found among these 13 genomes. Four mutational hotspot regions, including trnM-atpE, atpB, atpB-rbcL and ycf1 were identified. They could identify and phylogeny future species in the genus Amorphophallus. Positive selection was found for rpl36, ccsA, rpl16, rps4, rps8, rps11, rps12, rps14, clpP, rps3, ycf1, rpl20, rps2, rps18, rps19, atpA, atpF, rpl14, rpoA, rpoC1, rpoC2 and rps15 based on the analyses of Ka/Ks ratios. Phylogenetic inferences based on the complete chloroplast genomes revealed a sister relationship between Amorphophallus and Caladieae. All Amorphophallus species formed a monophyletic evolutionary clade and were divided into three groups, including CA-II, SEA, and CA-I. Amorphophallus albus, A. krausei, A. kachinensis and A. konjac were clustered into the CA-II clade, A. paeoniifolius and A. titanum were clustered into the SEA clade, A. muelleri ‘zhuyajin1’, Amorphophallus sp, A. coaetaneus, A. tonkinensis and A. yunnanensis were clustered into CA- I clade.ConclusionsThe genome structure and gene content of Amorphophallus chloroplast genomes are consistent across various species. In this study, the structural variation and comparative genome of chloroplast genomes of Amorphophallus were comprehensively analyzed for the first time. The results provide important genetic information for species classification, identification, molecular breeding, and evolutionary exploration of the genus Amorphophallus.

Semantic information guided diffusion posterior sampling for remote sensing image fusion

The task of image fusion for optical images and SAR images is to integrate valuable information from source images. Recently, owing to powerful generation, diffusion models, e.g., diffusion denoising probabilistic model and score-based diffusion model, are flourished in image processing, and there are some effective attempts in image fusion by scholars’ progressive explorations. However, the diffusion models for image fusion suffer from inevitable SAR speckle that seriously shelters from effective information in the same location of optical image. Besides, these methods integrate pixel-level features without information for high-level tasks, e.g., target detection and image classification, which leads fused images are insufficient and their application accuracies are low, for high-level tasks. To tackle these hurdles, we propose the semantic information guided diffusion posterior sampling for image fusion. Firstly, we employ the SAR-BM3D as preprocessing to despeckle. Then, the sampling model is established with fidelity, regularization and semantic information guidance term. The first two terms are obtained by the variational diffusion method via variational inference and first-order stochastic optimization. The last term is served by cross entropy loss between annotation and classification result from FLCNet we design. Finally, the experiments validate the feasibility and superiority of the proposed method on WHU-OPT-SAR dataset and DDHRNet dataset.

Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction

The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional linear combinations while preserving the information for regression and classification. However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we propose a naive and a refined distributed estimation algorithms for fast implementation when the sample size is large. Both distributed sufficient dimension reduction estimators exhibit the same statistical efficiency as when all the data is merged together, which provides rigorous statistical guarantees for their application to large-scale datasets, while the refined method requires smaller batch sample sizes and hence is more advantageous when memory limitations exist on distributed machines. The two distributed algorithms are further adapted to principal weighted support vector machines (Shin et al., 2017) for sufficient dimension reduction in binary classification. The statistical accuracy and computational complexity of our proposed methods are examined through comprehensive simulation studies and in a real data application with more than 600000 samples.

A small‐target traffic sign detection algorithm based on partial conv and atrous spatial pyramid

AbstractTraffic sign detection is essential to an intelligent driving assistance system. The deep learning‐based algorithm proposed in this paper aims to address the issue of low detection accuracy caused by the small size and high density of traffic signs in real‐world traffic scenarios. First, to improve the feature extraction module of the backbone network and to increase the model's ability to capture contextual information, partial convolution (PConv) is introduced. Second, to prevent information loss during the downsampling process, a cross‐stage atrous spatial pyramid (ASPPFCSPC) is constructed using atrous convolution. This method combines feature map information from various scales and expands the receptive field. Lastly, the small‐target detection precision is improved by incorporating an additional small‐target detection head, which uses high‐resolution feature maps for shallow features. The detection head is decoupled to extract the location and class information of the predicted target separately, thereby enhancing the generalization ability of the proposed model. Experiments have demonstrated the superiority of the proposed algorithm, as testing on the TT100K dataset resulted in a mAP@0.5 of 91.2% and a mAP@0.5:0.95 of 71.8% using the proposed algorithm.

SAFENet: Semantic-Aware Feature Enhancement Network for unsupervised cross-domain road scene segmentation

Unsupervised cross-domain road scene segmentation has attracted substantial interest because of its capability to perform segmentation on new and unlabeled domains, thereby reducing the dependence on expensive manual annotations. This is achieved by leveraging networks trained on labeled source domains to classify images on unlabeled target domains. Conventional techniques usually use adversarial networks to align inputs from the source and the target in either of their domains. However, these approaches often fall short in effectively integrating information from both domains due to Alignment in each space usually leads to bias problems during feature learning. To overcome these limitations and enhance cross-domain interaction while mitigating overfitting to the source domain, we introduce a novel framework called Semantic-Aware Feature Enhancement Network (SAFENet) for Unsupervised Cross-domain Road Scene Segmentation. SAFENet incorporates the Semantic-Aware Enhancement (SAE) module to amplify the importance of class information in segmentation tasks and uses the semantic space as a new domain to guide the alignment of the source and target domains. Additionally, we integrate Adaptive Instance Normalization with Momentum (AdaIN-M) techniques, which convert the source domain image style to the target domain image style, thereby reducing the adverse effects of source domain overfitting on target domain segmentation performance. Moreover, SAFENet employs a Knowledge Transfer (KT) module to optimize network architecture, enhancing computational efficiency during testing while maintaining the robust inference capabilities developed during training. To further improve the segmentation performance, we further employ Curriculum Learning, a self-training mechanism that uses pseudo-labels derived from the target domain to iteratively refine the network. Comprehensive experiments on three well-known datasets, “Synthia→Cityscapes” and “GTA5→Cityscapes”, demonstrate the superior performance of our method. In-depth examinations and ablation studies verify the efficacy of each module within the proposed method.

Neural Networks Learn Specified Information for Imbalanced Data Classification

Neural Networks Learn Specified Information for Imbalanced Data Classification

Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides

AVPs, or antiviral peptides, are short chains of amino acids capable of inhibiting viral replication, preventing viral entry, or disrupting viral membranes. They represent a promising area of research for developing new antiviral therapies due to their potential to target a broad spectrum of viruses, incorporating those resistant to traditional antiviral drugs. However, traditional experimental methods for identifying AVPs are often costly and labour-intensive. Thus far, multiple computational methods have been introduced for the in silico identification of AVPs, but these methods still have certain shortcomings. In this study, we propose a novel stacked ensemble learning framework, termed Stack-AVP, for fast and accurate AVP identification. In Stack-AVP, we investigated heterogeneous prediction models, which were trained with 12 commonly used machine learning algorithms coupled with a wide range of multiple feature encoding schemes. Subsequently, these prediction models were adopted to generate multi-view features providing class information and probability information. Finally, we applied our feature selection method to determine the best feature subset for the construction of the final stacked model. Comparative assessments on the independent test dataset revealed that Stack-AVP surpassed the performance of current state-of-the-art methods, with an accuracy of 0.930, MCC of 0.860, and AUC of 0.975. Furthermore, it was found that our multi-view features exhibited a crucial mechanism to improve the prediction performance of AVPs. To facilitate experimental scientists in performing high-throughput identification of AVPs, the prediction sever Stack-AVP is publicly accessible at https://pmlabqsar.pythonanywhere.com/Stack-AVP.

Chimeric U-Net – Modifying the standard U-Net towards explainability

Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net, encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under:https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch

How does lead selection impact diagnosis through AI? Implications for continuous monitoring systems using convolutional neural networks

Abstract Background and Objectives Recently, there has been a surge in non-invasive diagnosing methods leveraging the dipolar 12-lead ECG information using Convolutional Neural Networks (CNNs). However, the traditional ECG contains 12 projections of the cardiac dipole in a three-dimensional space can result in redundancy. Furthermore, in continuous monitoring systems such as Holter and wearables, not all 12 leads are consistently available. This study aims to quantify redundancy and determine the best lead configuration for improved classification results using a CNN. It evaluates the level of redundancy in ECG leads and its impact on CNN classification. Methods The degree of redundancy was quantified by means of lead entropies (H) as depicted in Figure 1.A. The standard 12-lead ECG was chosen as the baseline input. From it, various subsets of leads were selected (6, 3, 1 leads) with the objective of attaining maximal orthogonality between leads while minimizing redundancy (Figure 1.B). A 2D-CNN model was trained for multiclass classification: Hypertension (HYP), ST-T interval Changes (STTC), Conduction Disturbances (CD), Myocardial Infarction (MI) and Normal (NORM). The designed inputs were employed to evaluate how redundancy impacts the performance of the CNN (see Figure 1.C). Results Reducing redundancy in input leads led to notable improvements in model prediction for all pathological conditions in terms of Area Under the Curve (AUC). Reducing to 6 and 8 leads were the most suitable input for the CNN (Table 1) increasing by 1.18% in HYP, 0.23% in STTC, 0.52% in CD, and 0.33% in MI. However, a significant reduction in redundancy may result in the loss of essential clinical information for classification, as observed in scenarios involving only 3 or 1 leads. Conclusions When diagnosing pathologies with systems featuring fewer leads, its combinations should aim for maximum orthogonality to reduce redundancy while preserving unique information. By strategically selecting a set of leads with reduced input redundancy, the effectiveness of the standard 12-lead ECG can be improved, thereby enhancing the utility of such continuous monitoring systems for automated diagnosis and also decreasing training and prediction times.

Clinical Characteristics of Patients with Takayasu Arteritis Undergoing Open or Endovascular Operations in China.

The operation rate for different involved arteries and the manifestation of vessel involvement of patients with Takayasu arteritis undergoing open or endovascular operations remain unclear. We aimed to investigate the clinical characteristics, vessel involvement, angiographic classification and operations information in a large cohort of patients with Takayasu arteritis undergoing open or endovascular operations at a single centre in China. From January 2017 to October 2022, a total of 153 consecutive patients undergoing open or endovascular operations were recruited from the Department of Vascular Surgery of Beijing Hospital. The demographic characteristics, clinical presentations, pattern of vascular involvement and operation information were collected and analysed. The majority of patients were female (128/153, 83.7%). The most common vascular finding was hypertension (66.7%). The subclavian (74.2%), carotid (70.1%) and renal (68.9%) arteries were the most commonly involved arteries. Type V (40.5%) was the most common angiographic classification pattern. A total of 296 open or endovascular operations were performed, including 73 percutaneous transluminal angioplasties (PTAs), 50 stent placements and 173 bypass graft operations. Patients with renal (83.3%) or carotid (65.2%) artery involvement had markedly higher rates of undergoing operations. The subclavian and carotid arteries, as well as the type V (40.5%) pattern, exhibited the highest frequency of involvement among patients with Takayasu arteritis who underwent open or endovascular operations. Variations in angiographic features can result in differences in clinical manifestations and significantly impact the possibility and modality of operations.

Is metadata of articles about COVID-19 enough for multilabel topic classification task?

The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.

Operational Google-Earth-Engine Workflow to Monitor Irrigated Areas in a Semi-Arid Climate

Abstract. Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.

Adaptive Taxonomy Learning and Historical Patterns Modeling for Patent Classification

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Existing methods for automated patent classification primarily focus on analyzing the text descriptions of patents. However, apart from the textual information, each patent is also associated with some assignees, and the knowledge of their previously applied patents can often be valuable for accurate classification. Furthermore, the hierarchical taxonomy defined by the IPC system provides crucial contextual information and enables models to leverage the correlations between IPC codes for improved classification accuracy. However, existing methods fail to incorporate the above aspects and lead to reduced performance. To address these limitations, we propose an integrated framework that comprehensively considers patent-related information for patent classification. To be specific, we first present an IPC codes correlations learning module to capture both horizontal and vertical information within the IPC codes. This module effectively captures the correlations by adaptively exchanging and aggregating messages among IPC codes at the same level (horizontal information) and from both parent and children codes (vertical information), which allows for a comprehensive integration of knowledge and relationships within the IPC hierarchical taxonomy. Additionally, we design a historical application patterns learning component to incorporate previous patents of the corresponding assignee by aggregating high-order temporal information via a dual-channel graph neural network. Finally, our approach combines the contextual information from patent texts, which encompasses the semantics of IPC codes, with assignees’ sequential preferences to make predictions. Experimental evaluations on real-world datasets demonstrate the superiority of our proposed approach over existing methods. Moreover, we present the model’s ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.

Post-disaster investigations of damage feature of buildings and structures due to several local strong winds in China during 2021–2024

Local strong winds such as tornadoes and downbursts are rare in terms of measured wind speed data due to their rapid formation and strong destructive power. Post-disaster investigations have become an important approach for rating the strength of these two kind of winds and analyzing structural failure mechanism. From 2021 to 2024, several tornadoes and downbursts occurred in China. This study delineated changes in the intensity levels of wind disasters along their paths based on on-site disaster investigations and established standards for the level of structural damage based on the extent of functional loss and repair. This study focused on the damage feature to light steel structures, masonry structures, steel-concrete structures, and pole structures, and discussed the causes of the damage from the perspectives of construction and force transmission mechanisms. In addition, this study estimated the equivalent wind speeds based on the damage feature of pole structures and metal roof claddings. To reproduce the damage process for a group of high-rise buildings, the computational fluid dynamic (CFD) technology is used to simulate the airflow distribution inside buildings under two conditions of open and closed interior doors. The investigation results can provide useful information for the wind intensity classification and rating standards in China. Meanwhile, compared with the interior doors being open or closed, closing indoor doors during strong winds can significantly reduce indoor wind speeds, which is beneficial for ensuring safety.

ASCL: Accelerating semi‐supervised learning via contrastive learning

SummarySSL (semi‐supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo‐labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo‐labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug‐and‐play method named Accelerating semi‐supervised learning via contrastive learning (ASCL). This method combines contrastive learning with uncertainty‐based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo‐labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi‐supervised learning. Uncertainty‐based selection provides a robust mechanism for acquiring pseudo‐labels. The combination of the contrastive learning module and the uncertainty‐based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state‐of‐the‐art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR‐10 dataset, the application of ASCL to Pseudo‐label, UDA (unsupervised data augmentation for consistency training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%.