Semi-supervised Model Research Articles

Optimasi Model Semi-Supervised Learning dengan SVM dan Naïve Bayes

<p><em>Determining labels in text mining is the most important thing, apart from using lexicon based, you can also use human understanding to interpret sentences into positive, negative and neutral categories. Sentiment analysis is to measure an opinion taken from tweets to be able to analyze the text. Researchers carry out a learning process and data testing. The focus of this research is to be able to determine the polarization of text into positive, negative and neutral forms using a semi-supervised machine learning model. In the previous data training process, labeling was carried out using human understanding to obtain positive, negative and neutral labels. Next, in the testing process, the data is not labeled, the role of the learning machine is so that the test data gets a label. The semi-supervised learning (SSL) technique is used to label unlabeled data with the algorithm used to process the training data using SVM and NB. The statistical evaluation used is cross validation and to measure the level of accuracy of the two algorithms using a confusion matrix. SVM received a high accuracy score in this study compared to NB, SVM got a high accuracy score in this study compared to NB, SVM got 88.97% accuracy and NB 83.02%</em></p>

Semi-supervised medical image segmentation network based on mutual learning.

Semi-supervised learning provides an effective means to address the challenge of insufficient labeled data in medical image segmentation tasks. However, when a semi-supervised segmentation model is overfitted and exhibits cognitive bias, its performance will deteriorate. Errors stemming from cognitive bias can quickly amplify and become difficult to correct during the training process of neural networks, resulting in the continuous accumulation of erroneous knowledge. To address the issue of error accumulation, a novel learning strategy is required to enhance the accuracy of medical image segmentation. This paper proposes a semi-supervised medical image segmentation network based on mutual learning (MLNet) to alleviate the issue of continuous accumulation of erroneous knowledge. The MLNet adopts a teacher-student network as the backbone framework, training student and teacher networks on labeled data and mutually updating network parameter weights, enabling the two models to learn from each other. Additionally, an image partial exchange algorithm (IPE) as an appropriate perturbation addition method is proposed to reduce the introduction of erroneous information and the disruption to the contextual information of the image. In the 10% labeled experiment on the ACDC dataset, our Dice coefficient reached 89.48%, a 9.28% improvement over the baseline model. In the 10% labeled experiment on the BraTS2019 dataset, the proposed method still performs exceptionally well, achieving 84.56%, surpassing other comparative methods. Compared with other methods, experimental results demonstrate that our approach achieves optimal performance across all metrics, proving its effectiveness and reliability.

Semi-supervised rotation measure deconvolution and its application to MeerKAT observations of galaxy clusters

Context. Faraday rotation contains information about the magnetic field structure along the line of sight and is an important instrument in the study of cosmic magnetism. Traditional Faraday spectrum deconvolution methods such as RMCLEAN face challenges in resolving complex Faraday dispersion functions and handling large datasets. Aims. We developed a deep learning deconvolution model to enhance the accuracy and efficiency of extracting Faraday rotation measures from radio astronomical data, specifically targeting data from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). Methods. We used semi-supervised learning, where the model simultaneously recreates the data and minimizes the difference between the output and the true signal of synthetic data. Performance comparisons with RMCLEAN were conducted on simulated as well as real data for the galaxy cluster Abell 3376. Results. Our semi-supervised model is able to recover the Faraday dispersion for extended rotation measure (RM) components, while accounting for bandwidth depolarization, resulting in a higher sensitivity for high-RM signals, given the spectral configuration of MGCLS. Applied to observations of Abell 3376, we find detailed magnetic field structures in the radio relics, and several active galactic nuclei. We also applied our model to MeerKAT data of Abell 85, Abell 168, Abell 194, Abell 3186, and Abell 3667. Conclusions. We have demonstrated the potential of deep learning for improving RM synthesis deconvolution, providing accurate reconstructions at a high computational efficiency. In addition to validating our data against existing polarization maps, we find new and refined features in diffuse sources imaged with MeerKAT.

Effects of viscosity and induced magnetic fields on weakly nonlinear wave transmission in a viscoelastic tube using physics-informed neural networks

This study presents an advanced deep learning methodology that utilizes physics-informed neural networks (PINNs), to analyze the transmission of weakly nonlinear waves in a prestressed viscoelastic arterial tube. Using the long wave approximation, a mathematical model is constructed to replicate the propagation of weakly nonlinear waves in a viscoelastic arterial tube filled with viscous nanofluid, taking into account the influence of an induced magnetic field. The perturbed Burger, perturbed Korteweg–de Vries, and perturbed Korteweg–de Vries-Burgers equations are formulated based on the combined effects of nanofluid viscosity and the applied magnetic field using the reductive perturbation technique. Semi-supervised physics-informed neural network models are utilized to solve these perturbed evolutionary equations, trained on a limited dataset within their rectangular domain of definition. Gaussian process-based Bayesian optimization is used to determine the hyperparameters of the neural network, ensuring optimal model is performance. The effectiveness of the optimal models is evaluated by calculating the residual losses associated with the perturbed partial differential equations (PDEs). Visual representations of weakly non-linear wave propagation, considering nanofluid viscosity and induced magnetic fields, enhance the comprehension of dissipative effects in the cardiovascular system. These insights aid in obtaining precise measurements of pulse wave velocity for cardiovascular health monitoring. Consequently, the application of PINN proves to be a valuable tool for solving real-world PDEs and highlights its importance in advancing medical machine learning fields.

Cross-domain fine grained strip steel defect detection method based on semi-supervised learning and Multi-head Self Attention coordination

The identification of steel strip defects plays a pivotal role in assessing steel quality and advancing production technology. However, the majority of intelligent defect recognition algorithms for steel strips, based on deep learning, primarily focus on supervised learning. These methods depend on a multitude of training samples, incurring additional manual labelling costs, and exhibit low recognition efficiency. In contrast to supervised learning, we integrate the fine-grained characteristics of strip defects. We propose a cross-domain, fine-grained strip defect detection method based on semi-supervised learning and Multi-head self-attention coordination, along with an improvement strategy, resulting in a novel network structure: Multi-head Self Attention and Semi-supervised collaborative detection network (MSD Net). This method initiates the cross-domain migration of defect samples through Cycle Generative Adversarial Networks (Cycle GAN), creating new semi-supervised training samples from source domain and target domain data to enhance data distribution diversity. The detection model is then constructed leveraging the advantages of Multi-head Self Attention (MSA) in augmenting the global receptive field of feature extraction. The proposed semi-supervised learning method employs a pseudo-label allocation strategy to guide the model in fully utilizing the distribution fitting of unlabeled samples. This allows the deep neural network to learn a more comprehensive multivariate data distribution within the training set, thereby enhancing the generalization ability of the semi-supervised model. Experimental results on the benchmark dataset for steel strip defect detection demonstrate that the cross-domain semi-supervised method achieves a test accuracy of 96.1 % on mAP@0.5, surpassing the supervised baseline model by 4.8 %. Our method also outperforms the baseline supervised model in the accuracy of small target recognition on PASCAL VOC 2007 datasets. Additionally, we have implemented a strip defect detection system based on edge computing for real-time deployment of the proposed algorithm. Testing in an actual industrial setting further validates the efficacy of our proposed method in practical applications. Our work encourages further exploration, the task of public datasets can be obtained at https://github.com/songzhiweiknight/NEU-DET-Datasets.git.

Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization

Data-driven fault classification identifies and categorizes faults or anomalies in industrial systems, to ensure efficient and safe operations. This paper addresses the challenges of semi-supervised learning for dynamic fault classification in industrial processes. The key problems involve utilizing three unique characteristics of typical industrial process dataset: 1) the presence of unlabeled samples due to high labeling costs and required expert knowledge, 2) the local manifold structure arising from strong variable coupling, and 3) time correlation inherent in time-series sensing data of dynamic processes. To tackle these challenges, a semi-supervised fault classification model, SSDCGAN, is proposed, based on deep convolutional Generative Adversarial Networks. The model captures dynamic temporal information through a moving window technique and leverages manifold regularization to maintain classifier consistency along the manifold’s tangent direction. Evaluations on the Tennessee Eastman (TE) benchmark demonstrate that SSDCGAN enhances fault classification accuracy, outperforming current methods.

Estimation of Arterial Path Flow Considering Flow Distribution Consistency: A Data-Driven Semi-Supervised Method

To implement fine-grained progression signal control on arterial, it is essential to have access to the time-varying distribution of the origin–destination (OD) flow of the arterial. However, due to the sparsity of automatic vehicle identification (AVI) devices and the low penetration of connected vehicles (CVs), it is difficult to directly obtain the distribution pattern of arterial OD flow (i.e., path flow). To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. Firstly, this paper proposes a semi-supervised arterial path flow estimation model based on multi-knowledge graphs. It utilizes graph neural networks to combine some arterial AVI OD flow observation information with CV trajectory information to infer the path flow of AVI unobserved OD pairs. Further, to ensure that the estimation results of the multi-knowledge graph path flow estimation model are consistent with the distribution of path flow in real situations, we introduce a generative adversarial network (GAN) architecture to correct the estimation results. The proposed model is extensively tested based on a real signalized arterial. The results show that the proposed model is still able to achieve reliable estimation results under low connected vehicle penetration and with less observed label data.

Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.

Automated segmentation software in optical coherence tomography (OCT) devices is usually developed for and primarily tested on common diseases. Therefore segmentation accuracy of automated software can be limited in eyes with rare pathologies. We sought to develop a semisupervised deep learning segmentation model that segments 10 retinal layers and four retinal features in eyes with Macular Telangiectasia Type II (MacTel) using a small labeled dataset by leveraging unlabeled images. We compared our model against popular supervised and semisupervised models, as well as conducted ablation studies on the model itself. Our model significantly outperformed all other models in terms of intersection over union on the 10 retinal layers and two retinal features in the test dataset. For the remaining two features, the pre-retinal space above the internal limiting membrane and the background below the retinal pigment epithelium, all of the models performed similarly. Furthermore, we showed that using more unlabeled images improved the performance of our semisupervised model. Our model improves segmentation performance over supervised models by leveraging unlabeled data. This approach has the potential to improve segmentation performance for other diseases, where labeled data is limited but unlabeled data abundant. Improving automated segmentation of MacTel pathology on OCT imaging by leveraging unlabeled data may enable more accurate assessment of disease progression, and this approach may be useful for improving feature identification and location on OCT in other rare diseases as well.

Super-Jump: State Intent Inference and Lineage Tracking through Semi-Supervised Jump Diffusion Modeling in Normal Hematopoiesis and Acute Myeloid Leukemia

Super-Jump: State Intent Inference and Lineage Tracking through Semi-Supervised Jump Diffusion Modeling in Normal Hematopoiesis and Acute Myeloid Leukemia

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy.

The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored. We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches. We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains. Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort. This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.

S2Match: Self-paced sampling for data-limited semi-supervised learning

Data-limited semi-supervised learning tends to be severely degraded by miscalibration (i.e., misalignment between confidence and correctness of predicted pseudo labels) and stuck at poor local minima while learning from the same set of over-confident yet incorrect pseudo labels repeatedly. We design a simple and effective self-paced sampling technique that can greatly alleviate the impact of miscalibration and learn more accurate semi-supervised models from limited training data. Instead of employing static or dynamic confidence thresholds which is sensitive to miscalibration, the proposed self-paced sampling follows a simple linear policy to select pseudo labels which eases repeated learning from the same set of falsely predicted pseudo labels at the early training stage and lowers the chance of being stuck at local minima effectively. Despite its simplicity, extensive evaluations over multiple data-limited semi-supervised tasks show the proposed self-paced sampling outperforms the state-of-the-art consistently by large margins.

Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation

Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.

A novel self-training framework for semi-supervised soft sensor modeling based on indeterminate variational autoencoder

In modern industrial processes, the high acquisition cost of labeled data can lead to a large number of unlabeled samples, which greatly impacts the accuracy of traditional soft sensor models. To this end, this paper proposes a novel semi-supervised soft sensor framework that can fully utilize the unlabeled data to expand the original labeled data, and ultimately improve the prediction accuracy. Specifically, an indeterminate variational autoencoder (IVAE) is first proposed to obtain pseudo-labels and their uncertainties for unlabeled data. On this basis, the IVAE-based self-training (ST-IVAE) framework is further naturally proposed to expand the original small labeled dataset through continuous circulation. Among them, a variance-based oversampling (VOS) strategy is introduced to better utilize the pseudo-label uncertainty. By determining similar sample sets through the comparison of Kullback-Leibler (KL) divergence obtained by the proposed IVAE model, each sample can be independently modeled for prediction. The effectiveness of the proposed semi-supervised framework is verified on two real industrial processes. Comparable results illustrate that the ST-IVAE framework can still predict well even in the presence of missing input data compared to state-of-the-art methodologies in addressing semi-supervised soft sensing challenges.

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape.

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants- k cat and K M -for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Tri-training algorithm based nuclear power systems semi-supervised fault diagnosis under multiple restricted data conditions

An improved tri-training semi-supervised classification method was proposed as a solution to the restricted data conditions of supervised classification models relying on labeled data, semi-supervised models not considering imbalanced proportion of faults data and the presence of faults data with similar feature or varying severity in existing data-driven nuclear power system fault diagnosis studies. Based on the structure of three identical sub-classifiers in the original tri-training algorithm, data update paths have been added, the updated data selection has been strengthened, and the initial information and permissions of sub-models have been differentiated. The novel method only requires the dataset to meet the Smoothness Assumption, and through multiple strict data update paths, it improves the utilization of unlabeled training data while reducing Pseudo-labeling process errors. The difference in permissions and initial information between sub-classifiers is utilized to improve the training rigor for fault types with smaller sample sizes, in order to alleviate the problem of imbalanced data. Based on a marine nuclear power system secondary circuit model and a widely recognized nuclear power plant dataset, multiple nuclear power system common restricted fault diagnosis datasets faced in ships and power plants were obtained to verify the novel method's advantages and generalization. The conclusion is that under the same conditions, compared with the original tri-training method and other semi-supervised learning methods, the Accuracy, AUC, Precision Rate and Recall Rate of the novel method have improved by about 21 %, 10 %, 20 %, and 21 %, respectively, and can reduce about 25 % of misjudgments between similar feature faults.

Advancing MRI segmentation with CLIP-driven semi-supervised learning and semantic alignment

Precise segmentation and reconstruction of multi-structures within MRI are crucial for clinical applications such as surgical navigation. However, medical image segmentation faces several challenges. Although semi-supervised methods can reduce the annotation workload, they often suffer from limited robustness. To address this issue, this study proposes a novel CLIP-driven semi-supervised model, that includes two branches and a module. In the image branch, copy-paste is used as data augmentation method to enhance consistency learning. In the text branch, patient-level information is encoded via CLIP to drive the image branch. Notably, a novel cross-modal fusion module is designed to enhance the alignment and representation of text and image. Additionally, a semantic spatial alignment module is introduced to register segmentation results from different axial MRIs into a unified space. Three multi-modal datasets (one private and two public) were constructed to demonstrate the model’s performance. Compared to previous state-of-the-art methods, this model shows a significant advantage with both 5% and 10% labeled data. This study constructs a robust semi-supervised medical segmentation model, particularly effective in addressing label inconsistency and abnormal organ deformations. It also tackles the axial non-orthogonality challenges inherent in MRI, providing a consistent view of multi-structures.

Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models

Smart buildings optimize energy consumption and occupant comfort through Heating, Ventilation and Air Conditioning, and lighting management. Nevertheless, large venues require data fusion techniques to improve analysis and forecasting. This study aims to evaluate the effectiveness of using different feature fusion techniques, environmental sensors, and semi-supervised learning to estimate indoor occupancy in a 230 m2 office. Using five Internet of Things devices measuring air temperature, relative humidity, and barometric pressure, data was collected for 99 days with 6800 entries (on average) and only 14% labeled. Eight feature selection methods were evaluated along with three supervised and two semi-supervised classification methods. Results indicate that the Chi-squared-based approach for feature fusion outperformed others. Similarly, the semi-supervised Self-Training model achieved better performance than the supervised methods. This research shows that combining semi-supervised learning and data fusion allows for estimating the occupancy level in large indoor spaces with high accuracy and low labeling costs. Highlights This study pioneers in exploring semi-supervised learning and distinct feature fusion methods for estimating indoor occupancy levels in a 230 m 2 open office using only Internet of Things (IoT) environmental sensors (air temperature, relative humidity, and barometric pressure). A comprehensive comparison of statistical methods, feature selection, and dimensionality reduction techniques are conducted to determine their ability to generate robust feature fusion sets. The feature fusion selected through the Chi-squared test stood out with a high accuracy F1-score (average of 0.95) and an average accuracy of 0.99. The Self-Training model reached the best performance from semi-supervised learning, with an average F1-Score of 0.90 and an average accuracy of 0.97, based on a dataset with a large proportion of unlabelled data (16,847 entries) and only 9367 labels. For supervised learning, Random Forest achieved a high accuracy (average of 0.98) and F1-score (average of 0.93) across various feature sets.

Investigating Semi-supervised Time Series Anomaly Detection Using AW-SNBAE Model

In time series anomaly detection, most of the existing algorithms have difficulty in balancing the precision and dependence on labeled data. To solve this problem, a semi-supervised model called Adaptive Wavelet Sparse N-Beats AutoEncoder (AW-SNBAE) is proposed. This model has two main innovations. Firstly, an adaptive wavelet transform module is designed, which adaptively learns the wavelet function based on the global information of the time series, removes noise, and captures features of short-term and long-term changes. Secondly, the proposed Sparse N-Beats Auto Encoder (SNBAE) uses N-Beats as a base, and then, combines residual enhancement and sparse regularization strategies to construct the encoder and the decoder, respectively. Experimental results show that the AW-SNBAE model improves F1 scores by 8% in univariate time series anomaly detection and by 2% on average in multivariate detection. These results demonstrate that the model is able to exhibit excellent detection capability despite limited labeled data.

Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series

Detecting anomalies in multivariate time series poses a significant challenge across various domains. The infrequent occurrence of anomalies in real-world data, as well as the lack of a large number of annotated samples, makes it a complex task for classification algorithms. Deep Neural Network approaches, based on Long Short-Term Memory (LSTMs), Autoencoders, and Variational Autoencoders (VAEs), among others, prove effective with handling imbalanced data. However, the same does not follow when such algorithms are applied on multivariate time-series, as their performance degrades significantly. Our main hypothesis is that the above is due to anomalies stemming from a small subset of the feature set. To mitigate the above issues in the multivariate setting, we propose forming an ensemble of base models by combining different feature selection and transformation techniques. The proposed processing pipeline includes applying a Feature Bagging techniques on multiple individual models, which considers separate feature subsets for each specific model. These subsets are then partitioned and transformed using multiple nested rotations derived from Principal Component Analysis (PCA). This approach aims to identify anomalies that arise from only a small portion of the feature set while also introduces diversity by transforming the subspaces. Each model provides an anomaly score, which are then aggregated, via an unsupervised decision fusion model. A semi-supervised fusion model was also explored, in which a Logistic Regressor was applied on the individual model outputs. The proposed methodology is evaluated on the Skoltech Anomaly Benchmark (SKAB), containing multivariate time series related to water flow in a closed circuit, as well as the Server Machine Dataset (SMD), which was collected from a large Internet company. The experimental results reveal that the proposed ensemble technique surpasses state-of-the-art algorithms. The unsupervised approach demonstrated a performance improvement of 2% for SKAB and 3% for SMD, compared to the baseline models. In the semi-supervised approach, the proposed method achieved a minimum of 10% improvement in terms of anomaly detection accuracy.

Semi-supervised action recognition with dynamic temporal information fusion

The most advanced semi-supervised models available are based on images for innovation, and the use of semi-supervised learning models augmented with temporal data for video-level action recognition still suffers from severe model mismatches, and the models are not sufficiently capable of capturing both local and global information about the action. Secondly the use of constant-threshold pseudo-labeling leads to low utilization of unlabeled data for difficult actions in the early stages of training, poor pseudo-labeling quality and affects recognition accuracy. To make the semi-supervised framework FixMatch more suitable for action recognition, we propose Time-Mixer and Dynamic Threshold, respectively. Time-Mixer explores complementary information between time sequences through the fusion of two-channel temporal context information. Dynamic Threshold utilizes a new core mapping function (Normal Distribution Function) to enhance pseudo-labeling quality. Extensive experiments were conducted on three action recognition datasets (Kinetics-400, UCF-101, and HMDB-51). Comprehensive experiments show that the performance of the semi-supervised model in action recognition improves considerably after using dynamic thresholding and temporal context information fusion, with a 14.4% improvement over the baseline and a 1.8% improvement over the TG (with a labeling rate of 10%) in UCF101, whereas an overall good performance is obtained for DTIF.