Classification Algorithm Research Articles

Deep Learning Enhanced CNN with Bio-Inspired Techniques and BCE For Effective Lung Nodules Detection & Classification For Accurate Diagnosis

Objectives: To propose a new hybrid deep learning model to boost lung nodule detection and classification in combination with a bioinspired method for efficient hyper-parameter optimization to maximize true positive and nodule discrimination rates. Methods: In order to extract the intrinsic and complex features from different lung nodules, deep learning-based enhanced CNN (ECNN) is used. Segmentation masks are generated using Mask-RCNN to isolate and capture the ROIs, which serve as input to CNN. Prior to segmentation and extraction, data cleaning, transformation, and augmentation is done. Hyper-parameter optimization is done using a bio-inspired differential evolution method, which helps to identify the learning rate and number of layers to achieve optimal performance. We utilize BCE to quantify the performance of classification tasks. A large-scale CT-DICOM image dataset with 25,1135 images is used for this research work, collected from the cancer imaging archive, which has a clinically proven record of 355 instances with detailed image analysis, tumor location, and bounding boxes with a resolution of 512x512 pixels. 175794 images are used for training, and 75341 images are used for testing and validation. The performance of the new system is assessed using MATLAB, where results are compared with existing models such as SVM-WSS, GCPSO-PNN, and 3D-DLCNN models. Findings: The newly suggested ECNN with DE Bio-inspired model boosts the performance of lung nodule detection and classification with 94.7% accuracy, 93.8% sensitivity, 94.5% specificity, 93.4% F1 score, 92.4% dice coefficient, 0.1 Log Loss and AUC-ROC with 0.93 TPR and 0.07 FPR. Novelty: The novel method presents an advanced computational model using deep learning and bio-inspired algorithms for robust classification of lung nodules, which significantly improves the early diagnosis. This CAD model overcomes the limitations of the existing approaches SVM-WSS, GCPSO-PNN, and 3D-DLCNN in terms of segmentation, classification, error detection, and hyper-parameter tuning. Keywords: Lung Nodules, Deep Learning, Disease Classification, Image Processing, ECNN- DE Classifier, CT-DICOM Dataset

Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment

Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.Each node in the original graph is smoothed together with its neighbor nodes.The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.Experimental results show that AFEF_LAA can achieve higher node classification accuracy.

Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G Networks

When it comes to clinical applications, sensor-based human activity recognition (HAR) is invaluable, and numerous machine learning algorithms have effectively used to obtain excellent presentation. Using a variety of on-body sensors, these systems attempt to ascertain the subject's status relative to their immediate surroundings. There was a time when feature extraction was done by hand, but now more and more people are using Artificial Neural Networks (ANNs). A number of innovative approaches to HAR have surfaced since the advent of deep learning. Problems arise, however, for sensor-based HAR classification algorithms in today's communication networks. Among these, you can find solutions to problems like deal with complicated and large-scale data signals, extract characteristics from complicated datasets, and meet explainability standards. For complicated 5G networks, these difficulties become even more apparent. In particular, explainability is now critical for the broad use of sensor-based HAR in 5G networks and beyond. The research suggests a classification approach based on path signatures, recurrent signature (ReS), to address these issues. This cutting-edge model employs deep-learning (DL) approaches to circumvent the tedious feature selection challenge. Furthermore, the study investigates how to improve the ReS model's classification accuracy by using graph-based optimisation methods. To test how well the suggested framework worked, to dug deep into the publicly available dataset, which included a separate set of tasks. The paper's empirical results on AReM datasets achieved an average accuracy of 96%.

Towards an open digital catalogue of sound signatures from the underwater soundscape

Passive acoustic monitoring plays a crucial role to study the underwater soundscape. Identifying the sound signatures of specific marine species and anthropogenic contributions is a key step in assessing the impact of human activities on the acoustic environment. The project COSS (Catalogue of Sound Signatures) aims to build an open library of sound signatures from the coastal seascape of European waters. In this framework, a data management workflow is developed within FAIR principles from the storage of the metadata, ensuring integrity and traceability of the recorded datasets, to the analysis and the annotation method, providing interoperability classification of sound signatures. An interactive map provides an overview of the database. Sound events are analyzed using a home-made annotation software. Labeling is based on a tree-structure ontology with respect to the biological species, human activities and geophony. This labels standardization makes it possible to extract information at several levels of precision. In particular, this library will serve as a learning and testing base for open source AI algorithms for the automatic classification of signals of anthropogenic origin. A long-term objective is to provide a means of determining the acoustic impact of human activities on the marine environment.

Soft-label recover based label-specific features learning

Presently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem. In addition, missing labels are common in multi-label datasets. Recovering missing labels and constructing soft labels that reflect the mapping relationship between instances and labels is a difficult task. Most existing algorithms can only solve one of these problems. Based on this, this paper proposes a soft-label recover based label-specific features learning (SLR-LSF) to solve the above problems simultaneously. Firstly, the information entropy is used to calculate the confidence matrix between labels, and the membership degree of soft labels is obtained by combining the label density information. Secondly, the membership degree and confidence matrix are combined to construct soft labels, and this process not only solves the problem of missing labels but also obtains soft labels with richer semantic information. Finally, in the process of learning specific label features for soft labels. The local smoothness of the labels learned through stream regularization is complemented by the global label correlation, thus improving the classification performance of the algorithm. To demonstrate the effectiveness of the proposed algorithm, we conduct comprehensive experiments on several datasets.

A sound recognition algorithm to filter non construction site noise

Sixense Engineering have developed a new algorithm to correct the noise levels measured around construction sites, by identifying and filtering out sounds that are generated by activities outside the site (emergency services sirens, traffic horns, etc.). The idea is to go beyond the simple classification of audio files by applying the classification algorithm to all the noise events that contribute to the noise dose LAeq,T. In this innovative process, the noise dose is calculated from elementary LAeq,1min,. When noise events are detected (in terms of LAeq,1min), the corresponding audio recording (.wav file) are analysed by the sound recognition algorithm. When the algorithm identifies that the noise source is not construction related, with a high degree of confidence, the corresponding LAeq,1min is excluded from the noise dose calculation. In other words, the measured noise dose can be filtered by removing non construction related noise events produced by external sources like sirens, horns, motorbikes, birds, music, etc.

A unified multimodal classification framework based on deep metric learning

Multimodal classification algorithms play an essential role in multimodal machine learning, aiming to categorize distinct data points by analyzing data characteristics from multiple modalities. Extensive research has been conducted on distilling multimodal attributes and devising specialized fusion strategies for targeted classification tasks. Nevertheless, current algorithms mainly concentrate on a specific classification task and process data about the corresponding modalities. To address these limitations, we propose a unified multimodal classification framework proficient in handling diverse multimodal classification tasks and processing data from disparate modalities. UMCF is task-independent, and its unimodal feature extraction module can be adaptively substituted to accommodate data from diverse modalities. Moreover, we construct the multimodal learning scheme based on deep metric learning to mine latent characteristics within multimodal data. Specifically, we design the metric-based triplet learning to extract the intra-modal relationships within each modality and the contrastive pairwise learning to capture the inter-modal relationships across various modalities. Extensive experiments on two multimodal classification tasks, fake news detection and sentiment analysis, demonstrate that UMCF can extract multimodal data features and achieve superior classification performance than task-specific benchmarks. UMCF outperforms the best fake news detection baselines by 2.3% on average regarding F1 scores.

Advancing source reactor-type discrimination using machine learning techniques and SFCOMPO-2.0 experimental database

In recent years, nuclear forensic analysis has become crucial due to the growing global threat of nuclear terrorism and smuggling. Since 2005, extensive research has been conducted on identifying the origin of spent nuclear fuel, focusing on the source reactor-type discrimination, 235U enrichment of the fresh fuel, and the fuel exposure in the reactor (known as burnup). However, the majority of research relies on computed databases, which may lead to tracing discrepancies compared with actual situations. The present study employs the isotopic measurements from the experimental SFCOMPO-2.0 database to predict nuclear reactor types using Factor Analysis (FA) and various machine learning classification algorithms. The results reveal that FA is an effective method for dimension reduction and visualization. The FA-KNN, Random Forest (RF), and Multilayer Perceptron (MLP) algorithms are applied using a consistent dataset partition to ensure unbiased comparisons. The prediction results based on 10-fold stratified cross-validation are quite promising and the Receiver Operating Characteristic (ROC) curves for multi-class classification confirm the excellent generalization ability of models. Therefore, the application of machine learning techniques is highly effective for reactor-type forensics analysis, especially for RF and MLP.

LightGBM integration with modified data balancing and whale optimization algorithm for rock mass classification

The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, this study introduces an innovative predictive model combining the improved EWOA (IEWOA) and the light gradient boosting machine (LightGBM). The proposed IEWOA algorithm incorporates a novel parameter l for more effective position updates during the exploration stage and utilizes sine functions during the exploitation stage to optimize the search process. Additionally, the model integrates a minority class technique enhanced with a random walk strategy (MCT-RW) to extend the boundaries of minority classes, such as Classes II, IV, and V. This approach significantly improves the recall and F1-score for these rock mass classes. The proposed methodology was rigorously evaluated against other predictive algorithms, demonstrating superior performance with an accuracy of 94.74%. This innovative model not only enhances the accuracy of rock mass classification but also contributes significantly to the intelligent and efficient construction of TBM tunnels, providing a robust solution to one of the key challenges in underground engineering.

A fine-grained image classification algorithm based on self-supervised learning and multi-feature fusion of blood cells

Leukemia is a prevalent and widespread blood disease, and its early diagnosis is crucial for effective patient treatment. Diagnosing leukemia types heavily relies on pathologists’ morphological examination of blood cell images. However, this process is tedious and time-consuming, and the diagnostic results are subjective, leading to potential misdiagnosis and underdiagnosis. This paper proposes a blood cell image classification method that combines MAE with an enhanced Vision Transformer to tackle these challenges. Initially, pre-training occurs on two datasets, TMAMD and Red4, using the MAE self-supervised learning algorithm. Subsequently, the pre-training weights are transferred to our improved model.This paper introduces feature fusion of the outputs from each layer of the Transformer encoder to maximize the utilization of features extracted from lower layers, such as color, contour, and texture of blood cells, along with deeper semantic features. Furthermore, the dynamic margins for the subcenter Arcface Loss function are employed to enhance the model’s fine-grained feature representation by achieving inter-class dispersion and intra-class aggregation. Models trained using our method achieved state-of-the-art results on both the TMAMD dataset and Red4 dataset, with classification accuracies of 93.51% and 81.41%, respectively. This achievement is expected to be a valuable reference for physicians in their clinical diagnoses.

An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.

A Comprehensive Compilation of Spectral Libraries for Petroleum Hydrocarbons (PHC) Encompassing VNIR-SWIR-TIR Ranges

Remote detection and mapping of surface materials using optical sensors relies predominantly on analyzing multispectral and hyperspectral imagery employing classification algorithms. The classification process involves comparing the spectra of individual pixels within the image to spectra from reference databases, commonly referred to as spectral libraries. Here, we introduce a comprehensive compilation of spectral libraries specifically tailored for petroleum hydrocarbons (PHC), meticulously crafted under controlled laboratory conditions. This compilation includes reference spectral libraries for various PHC forms, including crude oils, mineral substrate-PHC mixtures (comprising crude oils and fuels), oil-film on water, and oil-water emulsions. Data collection was conducted within the visible, near, and shortwave IR (VNIR-SWIR – 0.35–2.5 µm) spectra and thermal IR (TIR – 3–15 µm) range. The openly accessible spectral libraries presented herein support the scientific community and industry in characterizing field samples or spectral data from onshore and offshore sites. Furthermore, these libraries are instrumental in developing and applying classification algorithms designed for processing spectral images captured by cameras coupled to multiple platforms (e.g., tripods, drones, airborne, orbital satellites).

MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed

Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these algorithms suffer in terms of generalization, precision, and reliability. The use of the distinctive characteristics of bibliographic metadata can prove effective in achieving better performance for this challenging task. In this research paper, we introduce an approach for biomedical relation classification using the qualifiers of co-occurring Medical Subject Headings (MeSH). First of all, we introduce MeSH2Matrix, our dataset consisting of 46,469 biomedical relations curated from PubMed publications using our approach. Our dataset includes a matrix that maps associations between the qualifiers of subject MeSH keywords and those of object MeSH keywords. It also specifies the corresponding Wikidata relation type and the superclass of semantic relations for each relation. Using MeSH2Matrix, we build and train three machine learning models (Support Vector Machine [SVM], a dense model [D-Model], and a convolutional neural network [C-Net]) to evaluate the efficiency of our approach for biomedical relation classification. Our best model achieves an accuracy of 70.78% for 195 classes and 83.09% for five superclasses. Finally, we provide confusion matrix and extensive feature analyses to better examine the relationship between the MeSH qualifiers and the biomedical relations being classified. Our results will hopefully shed light on developing better algorithms for biomedical ontology classification based on the MeSH keywords of PubMed publications. For reproducibility purposes, MeSH2Matrix, as well as all our source codes, are made publicly accessible at https://github.com/SisonkeBiotik-Africa/MeSH2Matrix.

B-117 Rapid Classification of Cleanliness Grades of Vaginal Discharge through Computational Analysis of SERS Spectra via Variational Auto-Encoder Model And Long Short-Term Memory Networks

Abstract Background Classification of cleanliness grades of vaginal discharge plays a pivotal role in clinical laboratories, serving as indicators for the presence of various vaginal infections such as bacterial vaginosis and candidal vulvovaginitis, etc. The conventional method for assessing vaginal discharge cleanliness involves manual microscopy, a technique susceptible to variations based on the examiner's expertise and subjective judgment, hindering the attainment of swift and accurate diagnoses. Methods This study introduces a novel approach by integrating Surface-Enhanced Raman Spectroscopy (SERS) with a deep learning algorithm for the rapid classification of vaginal discharge cleanliness grades. The proposed classification method combines the Variational Auto-Encoder (VAE) and Long Short-Term Memory (LSTM) to process SERS spectra. The quality of the training data is assessed using the signal-to-noise ratio (SNR) as a standard indicator. Results The application of the VAE-LSTM algorithm significantly enhances classification performance, providing swift and accurate results while reducing the required time. Comparative analyses with various classical machine learning algorithms and evaluation metrics reveal the superior predictive capability and time efficiency of the VAE-LSTM algorithm, demonstrating 100% accuracy and a fitting time of 2 seconds. Blind testing of SERS spectra collected from unknown patients further validates the reliability of the proposed method. Conclusions This study presents a promising and practical approach for the rapid classification of vaginal discharge cleanliness grades. The integration of the SERS technique with the VAE-LSTM algorithm not only ensures accuracy and efficiency in clinical diagnosis but also lays the foundation for advancements in non-invasive diagnostic technologies in gynecological healthcare.

B-107 Advancing Parasitology Laboratory Quality and Processes Through Artificial Intelligence (AI) and Automation

Abstract Background This research emphasizes the profound impact of intestinal parasitic infections on developing countries, particularly in regions such as sub-Saharan Africa, South and Central America, China, and East Asia. With over 1.5 billion people globally affected and 450 million facing serious illness and a mortality rate of 155,000 cases per year, the socio-economic hindrances posed by these infections necessitate innovative approaches for control and eradication. The integration of artificial intelligence (AI) and machine learning (ML) into parasitology, exemplified by the “Automated Diagnosis of Intestinal Parasites” (ADIP), from brazilian Portuguese “Diagnóstico Automatizado de Parasitas Intestinais” (DAPI) system, showcases a paradigm shift. This advanced system, combining simple and complex decision-making mechanisms, demonstrates promising results, achieving high agreement compared to TF-Test (three fecal test). Methods Data were extracted through reports from the laboratory information system (LIS) from January-December 2023. TF-test was replaced by ADIP system in October 2023. Data were analyzed using Microsoft Excel software, with a bibliographic survey in national and international repositories. Results The parasitology laboratory of AFIP implemented artificial intelligence (AI) with ADIP equipment associated with system LIS automation, automating the analysis of approximately 38,000 monthly stool samples. The ADIP classification, image analysis and subsequent algorithm allowed a reduced examination time, since approximately 90% of samples could be released automatically, optimizing the whole process. In addition, after a three-month period using only ADIP, positivity results increased from 7,63% (TF-test) to 9,53%. Our database is periodically reviewed by specialized professionals who validate positive results, ensuring the security of information obtained by artificial intelligence, bringing efficiency, safety, and quality to the laboratory processes. Conclusions Artificial intelligence (AI) in parasite identification and the automated results system promises a bright future. Collaboration between the scientific community and data scientists is crucial to enhance laboratory processes. Through these tools, it is possible to focus on continuous improvements, increased efficiency, and, most importantly, without sacrificing accuracy and result quality. With new technologies associated with the parasitology field, detailed records can be ensured, previously challenging to obtain, showcasing each step from result acquisition to issued report. Process improvement in parasitology brings optimizations, suggesting the potential for team reduction without compromising quality and improving the takt time.

B-123 Prediction of Opioid Use Duration in Post-Surgical Orthopedic Patients Using CYP2D6 Inhibitor Index and Routine Laboratory Markers

Abstract Background Hydrocodone, the most commonly prescribed opioid in the U.S., is typically used for pain management following surgery and is a known potential drug of abuse with non-negligible risk of addiction. Significant variability in patients’ response to hydrocodone therapy have been reported, owing to both iatrogenic and endogenous sources. The objective of this study is to evaluate the feasibility of predicting post-discharge hydrocodone use in patients who have undergone orthopedic surgery using patient demographics, genotyping, concurrently prescribed medications, and routinely collected laboratory test results. Methods Targeted genotyping was performed on each patient with genes known to affect hydrocodone pharmacokinetics or pharmacodynamics, including CYP2D6, CYP3A4, CYP3A5, CYP2B6, COMT, OPRM1, and ABCB1. An inhibition index for CYP2D6 was calculated by ranking concurrently prescribed medications as weak, moderate, or strong inhibitors of enzyme activity. Pain index, average hydrocodone dose per day during hospital stay, and CYP2D6 estimated activity scores based on allele functionality were included as predictors. In addition, a retrospective chart review of electronic hospital records was performed where basic metabolic panel and complete blood count results were screened within 10 days of each patient's surgery date. Two types of classification and decision tree algorithms were tuned and validated as prediction models using 5-fold cross validation, including a ‘Fast and Frugal Decision Tree’ (FFTree) and an extreme gradient boosting machine (xgBoost). Clinical predictive features were ranked according to the Shapley Additive Explanations (SHAP) values using the xgBoost model. Results Out of 79 total patients with hydrocodone use duration information, 25 were categorized as “Short” and 54 categorized as “Long” using a 1-week threshold. FFTree model selected the top three features of CYP2D6 inhibition index, blood sodium level, and blood creatinine level and obtained a sensitivity and specificity of 0.80 and 0.76, respectively. The xgBoost model, using the same three features, achieved a sensitivity and specificity of 0.872 and 0.628, respectively, an AUROC of 0.814, PRAUC of 0.912, net benefit of 0.359, and was well-calibrated with a Brier score of 0.161. Consistent with previously published results, the predictive capacity of all genotypes analyzed were unremarkable and consistently ranked as the least important features by the xgBoost model SHAP values, due to their low predictive value. Conclusions Both the FFTree and xgBoost models were able to adequately classify the patients according to a 1-week stratification criteria with good to excellent classification performance. Patients with larger CYP2D6 inhibition index were more likely to be classified as “Long” duration. Sodium and creatinine level, which may reflect hydration status and renal function, and therefore hydromorphone elimination rate, were key predictors of post-discharge hydrocodone use duration when combined with the CYP2D6 inhibition index. Further refinement of the proposed models and validation of model robustness and stability using external cohorts is necessary. These results may have implications for providers when prescribing hydrocodone for pain management, where the ability to risk-stratify patients according to their predicted probability of developing opioid use disorder is of clinical importance.

A defect classification algorithm for gas tungsten arc welding process based on unsupervised learning and few-shot learning strategy

Welding defect prediction is the foundation for ensuring welding quality in gas tungsten arc welding (GTAW). In the prediction process, method based on molten pool vision is the most effective. Since the classification of molten pool defects relies on a substantial volume of labeled data, it is challenging for the models to be applied industrially. This paper presents an algorithm, FS-Classifier, that can achieve high prediction accuracy based on a limited amount of labeled data. The FS-Classifier comprises two stages: Firstly, an unsupervised training approach named RaP is designed to pre-train the feature extractor using extensive unlabeled daily datasets. The RaP consists of a rotation angle prediction task and a position prediction task, which ensure that the network focuses on salient features and precise elements, respectively. Secondly, the support vectors constructed from limited labeled data are used for the feature classifier. The input data is classified to certain class by computing its distances to support vector. The model achieves an accuracy of 94.5 % on the private dataset and 92.8 % on the public dataset for the six classes of defects using 5 % of labeled data volume. In addition, comparative experiments show that our method only requires 5 % of labeled data to achieve accuracy comparable to traditional supervised learning methods. The proposed algorithm addresses the issue of relying on a substantial amount of labeled data in welding process defect classification.

Application of Improved Classification Algorithm with Binary Tree Support Vector Machine in Power Grid Prediction

Abstract To further improve the accuracy of the power grid prediction, the paper proposes an improved binary tree classification algorithm with the SVM and predicts the real time power grid. Based on the basic principles of SVM, the common multi-classifier classification algorithm and its characteristics are summarized. Incorporating the advantages of existing classification algorithms, an improved binary tree classification algorithm was used, addressing the limitations of conventional methods. From the simulation results, it can be seen that the improved binary tree algorithm can shorten the test time from the original 0.285 seconds to 0.267 seconds, and the measured category can also be increased from the original 94.42 to 95.27. This shows that the improved binary tree can not only improve the practicability of the support vector machine algorithm, but also better improve the ability of real-time prediction of power grid.

The Application of Deep Learning in Sports Training

In response to the problems of overfitting, susceptibility to interference information, and insufficient feature expression ability in existing deep learning methods for sports action recognition, the author proposes a deep learning sports action recognition method that integrates attention mechanism. This method proposes a video data augmentation algorithm in data preprocessing to reduce the risk of model overfitting. Then, during the video frame sampling process, the existing sampling algorithms are improved to effectively suppress the influence of interference information. In the special section, the network residual consolidation is proposed to improve the feature extraction capacity of the structure. The Long Term Time Transform (LSTM) network is used to solve the problem of the global correlation of the spatial correlation, and the classification algorithm is achieved by Softmax. and the classification algorithm is proposed. The experimental results show that the recognition rates of this method on UCFYouTube, KTH, and HMDB-51 data are 96.73%, 98.07%, and 64.82%, respectively.

Regulation of tapioca starch 3D printability by yeast protein: Rheological, textural evaluation, and machine learning prediction

This article investigated the effects of yeast protein (YP) on gel rheology, texture, and 3D printability of tapioca starch and the feasibility of Principal component analysis (PCA) and support vector machine (SVM) algorithms for classification and prediction of printability. The results indicated that increasing YP content enhanced the viscosity, storage and loss moduli, and hardness, thereby improving extrudability and supportability of 3D printing. The addition of 15% YP exhibited the best 3D printing performance, but excessively high YP addition hindered ink extrusion. PCA analysis based on rheological and texture indices categorized the ink's 3D printing performance into four classes: poor support and low printing accuracy; good support but low printing accuracy; good support and high printing accuracy; and non-smooth extrusion. Furthermore, SVM algorithm used texture data to predict printability classification, with the highest prediction accuracy (91.67%) achieved at polynomial kernel among four different kernel functions. These results confirm that YP can serve as a potential ink for 3D printing and underscore SVM's efficacy in predicting ink's 3D printing performance.