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Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes 认领 引用
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作者 Junmin Lyu Guangyu Xu +4 位作者 Feng Bao Yu Zhou Yuxin Liu Siyu Lu Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1235-1256,共22页
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati... Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks. 展开更多
关键词 GNN social networks nodes multi-label classification model graphic convolution neural network coupling principle
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Study on Multi-Label Classification of Medical Dispute Documents 认领 引用 被引量：2
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作者 Baili Zhang Shan Zhou +2 位作者 Le Yang Jianhua Lv Mingjun Zhong 《Computers, Materials & Continua》 SCIE EI 2020年第12期1975-1986,共12页
The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treat... The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods. 展开更多
关键词 Internet of Medical Things(IoMT) medical disputes medical dispute document(MDD) multi-label classification(MLC) key sentence extraction class imbalance
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Dual Sum-Product Networks Autoencoder for Multi-Label Classification 认领 引用
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作者 WANG Shengsheng ZHANG Hang CHEN Juan 《Journal of Shanghai Jiaotong university(Science)》 EI 2020年第5期665-673,共9页
Sum-product networks(SPNs)are an expressive deep probabilistic architecture with solid theoretical foundations,which allows tractable and exact inference.SPNs always act as black-box inference machine in many artifici... Sum-product networks(SPNs)are an expressive deep probabilistic architecture with solid theoretical foundations,which allows tractable and exact inference.SPNs always act as black-box inference machine in many artificial intelligence tasks.Due to their recursive definition,SPNs can also be naturally employed as hierarchical feature extractors.Recently,SPNs have been successfully employed as autoencoder framework in representation learning.However,SPNs autoencoder ignores the model structural duality and trains the models separately and independently.In this work,we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to compose as a dual form.This approach trains the models simultaneously,and explicitly exploits the structural duality between them to enhance the training process.Experimental results on several multilabel classification problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder architectures. 展开更多
关键词 smn-product networks(SPNs) representation learning dual learning multi-label classification
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A Multi-Label Classification Algorithm Based on Label-Specific Features 认领 引用 被引量：2
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作者 QU Huaqiao ZHANG Shichao +1 位作者 LIU Huawen ZHAO Jianmin 《Wuhan University Journal of Natural Sciences》 CAS 2011年第6期520-524,共5页
Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and... Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches. 展开更多
关键词 multi-label classification label-specific features feature＇s value density
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Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets 认领 引用 被引量：1
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作者 Shuo Xu Yuefu Zhang +1 位作者 Xin An Sainan Pi 《Journal of Data and Information Science》 CSCD 2024年第2期81-103,共23页
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t... Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution. 展开更多
关键词 Multi-label classification Real-World datasets Hierarchical structure Classification system Label correlation Machine learning
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Inverse design of nonlinear phononic crystal configurations based on multi-label classification learning neural networks 认领 引用 被引量：1
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作者 Kunqi Huang Yiran Lin +1 位作者 Yun Lai Xiaozhou Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第10期295-301,共7页
Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic feature... Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Traditional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials. 展开更多
关键词 multi-label classification learning nonlinear phononic crystals inverse design
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A Multi-Label Classification Method for Vehicle Video 认领 引用 被引量：1
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作者 Yanqiu Cao Chao Tan Genlin Ji 《Journal on Big Data》 2020年第1期19-31,共13页
In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity sc... In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity scene,one of the major challenges is mining information comprehensively from massive features in vehicle video.This paper proposes a multi-label classification method MCM-VV(Multi-label Classification Method for Vehicle Video)for vehicle video to judge the label of road condition for unmanned system.Method MCM-VV includes a process of feature extraction and a process of multi-label classification.During feature extraction,grayscale,lane line and the edge of main object are extracted after video preprocessing.During the multi-label classification,the algorithm DR-ML-KNN(Multi-label K-nearest Neighbor Classification Algorithm based on Dimensionality Reduction)learns the training set to obtain multi-label classifier,then predicts the label of road condition according to maximum a posteriori principle,finally outputs labels and adds the new instance to training set for the optimization of classifier.Experimental results on five vehicle video datasets show that the method MCM-VV is effective and efficient.The DR-ML-KNN algorithm reduces the runtime by 50%.It also reduces the time complexity and improves the accuracy. 展开更多
关键词 Vehicle video multi-label classification dimensionality reduction
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Feature Selection for Multi-label Classification Using Neighborhood Preservation 认领 引用 被引量：13
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作者 Zhiling Cai William Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第1期320-330,共11页
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f... Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods. 展开更多
关键词 Feature selection multi-label learning neighborhood relationship preserving sample similarity
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Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification 认领 引用
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作者 Zhenwu Wang Longbing Cao 《Journal of Beijing Institute of Technology》 EI CAS 2017年第3期404-410,共7页
In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different ... In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels.The paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation criteria.The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods. 展开更多
关键词 coupled similarity multi-label categorical data correlations
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ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification 认领 引用
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作者 Khudran M.Alzhrani 《Computers, Materials & Continua》 SCIE EI 2022年第9期5459-5479,共21页
Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure.Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task... Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure.Hard parameters sharing is a multitasking approach that shares hidden layers between multiple taskspecific outputs.The output layers’weights are essential in transforming aggregated neurons outputs into tasks labels.This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks.The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance.The ensemble of a multi-output neural network that learns to classify the same multi-label classification task per output layer can outperform an individual output layer neural network.We propose an ensemble strategy of output layers components in the multi-output neural network for multi-label classification(ENSOCOM).The baseline and proposed models are selected based on the size of the hidden layer and the number of output layers to evaluate the proposed method comprehensively.The ENSOCOM method improved the performance of the neural networks on five different multi-label datasets based on several evaluation metrics.The methods presented in this work can substitute the standard labels representation and predictions generation of any neural network. 展开更多
关键词 Ensemble learning multilabel classification neural networks
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Multi-label fundus disease classification using dual-branch deep learning:an intelligent diagnosis framework inspired by traditional Chinese medicine Five Wheels theory 认领 引用
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作者 Xin He Xiaohui Li +5 位作者 Jun Peng Lei Sun Dan Shu Li Xiao Qinghua Peng Xiaoxia Xiao 《Digital Chinese Medicine》 CAS CSCD 2026年第1期80-90,共11页
Objective To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases,addressing the key limitations of insufficient complementary feature extraction and inadequate cros... Objective To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases,addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods.Methods The fundus multi-label classification dataset with 12 disease categories(FMLC-12)dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition(ODIR)and Retinal Fundus Multi-Disease Image Dataset(RFMiD),yielding 6936 fundus images across 12 retinal pathology categories,and the framework was validated on both FMLC-12 and ODIR.Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine(TCM)ophthalmology,the dualbranch multi-label network(DBMNet)was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling.The architecture employed a TransNeXt backbone within a dual-branch design:one branch processed redgreen-blue(RGB)images to capture color-dependent features,such as vascular patterns and lesion morphology,while the other processed grayscale-converted images to enhance subtle textural details and contrast variations.A feature interaction module(FIM)effectively integrated the multi-scale features from both branches.Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM.The performance of DBMNet was compared against four state-of-the-art methods,including EfficientNet Ensemble,transfer learning-based convolutional neural network(CNN),BFENet,and EyeDeep-Net,using mean average precision(mAP),F1-score,and Cohen's kappa coefficient.Results The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline,increasing from 34.41%to 44.24%,and the addition of FIM further boosted mAP to 49.85%.On FMLC-12,DBMNet achieved an mAP of 49.85%,a Cohen’s kappa coefficient of 62.14%,and an F1-score of 70.21%.Compared with BFENet(mAP:45.42%,kappa:46.64%,F1-score:71.34%),DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa,while BFENet achieved a marginally higher F1-score.On ODIR,DBMNet achieved an F1-score of 85.50%,comparable to state-of-the-art methods.Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture,significantly improving multi-label fundus disease classification.The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases,providing a promising and clinically applicable pathway for standardized,intelligent fundus disease classification. 展开更多
关键词 Multi-label classification Fundus images Deep learning Dual-branch network Traditional Chinese medicine ophthalmology Five Wheels theory
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Similarity-based multi-dimensional multi-label classification 认领 引用
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作者 Zi-Zhan GU Bin-Bin JIA Min-Ling ZHANG 《Frontiers of Computer Science》 SCIE EI CAS CSCD 2026年第2期163-173,共11页
In multi-dimensional multi-label classification(MDML),a number of heterogeneous label spaces are assumed to characterize the rich semantics of one object from different dimensions and a set of proper labels can be ass... In multi-dimensional multi-label classification(MDML),a number of heterogeneous label spaces are assumed to characterize the rich semantics of one object from different dimensions and a set of proper labels can be assigned to the object from each heterogeneous label space.In recent years,similarity-based framework has achieved a promising performance in classification tasks(e.g.,multi-class/multi-label classification),while its effectiveness has not been investigated in solving the MDML problems.Moreover,existing similarity-based approaches only utilize either instance-based or label-based information which limits their generalization ability.In this paper,we propose a novel similarity-based MDML approach,naming SIDLE which attempts to utilize both instance-based and label-based information.To extract similarity information,SIDLE first identifies k nearest neighbors in instance space and enhanced label space,respectively.Then,with these identified samples,SIDLE calculates the simple counting statistics based on their labels as well as a bias based on distance between the sample and these identified samples.Finally,the instance space is enriched with extracted similarity information to update instance space and enhanced label space.These three steps are iteratively conducted until convergence.Experiments validate the effectiveness of the proposed SIDLE approach. 展开更多
关键词 machine learning multi-dimensional multi-label classification similarity-based learning k nearest neighbor feature augmentation
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Multi-Label Machine Learning Classification of Cardiovascular Diseases 认领 引用 被引量：1
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作者 Chih-Ta Yen Jung-Ren Wong Chia-Hsang Chang 《Computers, Materials & Continua》 SCIE EI 2025年第7期347-363,共17页
In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deat... In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs. 展开更多
关键词 Photoplethysmography machine learning health management multi-label classification cardiovascu-lar disease
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Multimodal Signal Processing of ECG Signals with Time-Frequency Representations for Arrhythmia Classification 认领 引用 被引量：1
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作者 Yu Zhou Jiawei Tian Kyungtae Kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期990-1017,共28页
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin... Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification. 展开更多
关键词 Electrocardiogram arrhythmia classification multimodal time-frequency representation
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Multi-Label Movie Genre Classification with Attention Mechanism on Movie Plots 认领 引用
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作者 Faheem Shaukat Naveed Ejaz +3 位作者 Rashid Kamal Tamim Alkhalifah Sheraz Aslam Mu Mu 《Computers, Materials & Continua》 SCIE EI 2025年第6期5595-5622,共28页
Automated and accurate movie genre classification is crucial for content organization,recommendation systems,and audience targeting in the film industry.Although most existing approaches focus on audiovisual features ... Automated and accurate movie genre classification is crucial for content organization,recommendation systems,and audience targeting in the film industry.Although most existing approaches focus on audiovisual features such as trailers and posters,the text-based classification remains underexplored despite its accessibility and semantic richness.This paper introduces the Genre Attention Model(GAM),a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots formulti-label genre classification.In order to assess its effectiveness,we assessmultiple transformer-based models,including Bidirectional Encoder Representations fromTransformers(BERT),ALite BERT(ALBERT),Distilled BERT(DistilBERT),Robustly Optimized BERT Pretraining Approach(RoBERTa),Efficiently Learning an Encoder that Classifies Token Replacements Accurately(ELECTRA),eXtreme Learning Network(XLNet)and Decodingenhanced BERT with Disentangled Attention(DeBERTa).Experimental results demonstrate the superior performance of DeBERTa-based GAM,which employs a two-tier hierarchical attention mechanism:word-level attention highlights key terms,while sentence-level attention captures critical narrative segments,ensuring a refined and interpretable representation of movie plots.Evaluated on three benchmark datasets Trailers12K,Large Movie Trailer Dataset-9(LMTD-9),and MovieLens37K.GAM achieves micro-average precision scores of 83.63%,83.32%,and 83.34%,respectively,surpassing state-of-the-artmodels.Additionally,GAMis computationally efficient,requiring just 6.10Giga Floating Point Operations Per Second(GFLOPS),making it a scalable and cost-effective solution.These results highlight the growing potential of text-based deep learning models in genre classification and GAM’s effectiveness in improving predictive accuracy while maintaining computational efficiency.With its robust performance,GAM offers a versatile and scalable framework for content recommendation,film indexing,and media analytics,providing an interpretable alternative to traditional audiovisual-based classification techniques. 展开更多
关键词 Multi-label classification artificial intelligence movie genre classification hierarchical attention mechanisms natural language processing content recommendation text-based genre classification explainable AI(Artificial Intelligence) transformer models BERT
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Review of the classification and related terminology of fine-grained sedimentary rocks 认领 引用 被引量：1
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作者 ZHU Rukai SUN Longde +2 位作者 ZOU Caineng CHEN Yang MIAO Xue 《Petroleum Exploration and Development》 SCIE 2026年第1期61-78,共18页
Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a class... Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area. 展开更多
关键词 fine-grained sedimentary rock shale mudstone clay shale oil shale gas lamellation shale microfacies classification scheme fine-grained sedimentology
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Robust Multi-Label Cartoon Character Classification on the Novel Kral Sakir Dataset Using Deep Learning Techniques 认领 引用
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作者 Candan Tumer Erdal Guvenoglu Volkan Tunali 《Computers, Materials & Continua》 SCIE EI 2025年第12期5135-5158,共24页
Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil... Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems. 展开更多
关键词 Cartoon character recognition multi-label classification deep learning transfer learning predictive modelling artificial intelligence-enhanced(AI-Enhanced)systems Kral Sakir dataset
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A robust phylogenomic framework supports a revised intrafamilial classification of Urticaceae 认领 引用
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作者 Xiao-Gang Fu Jie Liu +8 位作者 Richard I.Milne Alex K.Monro Shui-Yin Liu Qin Tian Gregory W.Stull Amos Kipkoech Ting-Shuang Yi De-Zhu Li Zeng-Yuan Wu 《Plant Diversity》 SCIE CAS CSCD 2026年第2期289-306,共18页
Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic s... Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic sequences and taxa within the nettle family(Urticaceae)are still lacking.Here,we reconstructed the phylogeny of Urticaceae(345 species across 89% of accepted genera)using concatenated and coalescent analyses from plastome and nuclear ribosomal DNA sequences.Different plastid datasets and tree inference methods yielded a consistent phylogenetic backbone,with 98% of nodes achieving>90% bootstrap support—a significant improvement compared to 54% of nodes in the latest published phylogenetic study of Urticaceae.Plastid and nuclear phylogenetic relationships were largely congruent,with several exceptions that warrant further study.In the context of the updated phylogenetic relationships,we propose dividing the family into seven tribes that correspond to seven major clades or subclades,including a newly established tribe,Sarcochlamydeae stat.nov.Our phylogenetic analysis indicates that Debregeasia and Phenax are non-monophyletic.By combing morphological,molecular and distributional evidence,we describe a new genus Chiajuia gen.nov.Additionally,we propose synonymizing the following genera:Cypholophus(to Boehmeria),Haroldiella(to Pilea),Hemistylus,Neodistemon,Rousselia(all to Pouzolzia),Hesperocnide(to Urtica),and Pellionia(to Elatostema),while recognizing Elatostematoides,Gonostegia,Leptocnide,Margarocarpus,Scepocarpus,and Sceptrocnide as distinct genera.This robust phylogenomic framework and revised classification lays a foundation for future studies on the evolution and ecology of Urticaceae.The approach applied here may also serve as an important reference for other large plant families in angiosperms. 展开更多
关键词 Chiajuia Intrafamilial classification Phylogenomics Plastome Sarcochlamydeae Urticaceae
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DCETEN:a lightweight ECG automatic classification network based on Transformer model 认领 引用
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作者 Fan Jiang Jiayi Xiao +1 位作者 Lei Liu Chaowei Wang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第5期789-801,共13页
Currently,Cardiovascular Disease(CVD)remains a significant contributor to premature mortality and escalating health care expenses.Early and accurate detection is crucial for the successful treatment,intervention,and m... Currently,Cardiovascular Disease(CVD)remains a significant contributor to premature mortality and escalating health care expenses.Early and accurate detection is crucial for the successful treatment,intervention,and monitoring of heart health.Electrocardiograms(ECGs)are essential for diagnosing and monitoring cardiovascular diseases.However,the increasing demand for ECG signal detection,coupled with a shortage of specialized ECG doctors,has made automatic classification and diagnosis of ECG signals a prominent research area.Traditional ECG signal classification models often involve numerous parameters,rendering them unsuitable for resource-limited IoT devices in smart healthcare scenarios.In response,this paper proposes a novel lightweight ECG signal classification network based on the Transformer model,named DCETEN.Specifically,we introduce a lightweight Efficient Channel Attention(ECA)module,integrating it with Depthwise Separable Convolution(DSC)to design a One-dimensional Convolutional Neural Network(1D-CNN)that enhances feature extraction capabilities.Additionally,we fuse hand-crafted RR interval features and features learned by the Transformer to comprehensively capture the ECG signal characteristics.Finally,to make the proposed method suitable for resource-constrained IoT-based edge devices,we employ pruning techniques to reduce the number of model parameters.We validated the proposed model on the MIT-BIH Arrhythmia Database,achieving 99.84%accuracy and a 99.67%F1 score with low computational and memory requirements,making it suitable for deployment in smart healthcare settings with prevalent resource limitations. 展开更多
关键词 Electrocardiogram Arrhythmia classification Transformer model Lightweight Weight pruning
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A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification 认领 引用
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作者 Hamzeh Asgharnezhad Pegah Tabarisaadi +2 位作者 Abbas Khosravi Roohallah Alizadehsani U.Rajendra Acharya 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1849-1874,共26页
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and... Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and limited uncertainty awareness.This study presents a comprehensive evaluation of deep learning-based skin lesion classification with transfer learning and UQ on the HAM10000 dataset.We benchmark several pre-trained feature extractors(including Contrastive Language-Image Pre-training(CLIP)variants,ResNet50,DenseNet121,VGG16,EfficientNet-V2-Large,and ConvNeXt Large)combined with traditional classifiers such as SVM,XGBoost,and logistic regression.Multiple PCA settings(64,128,256,512)are explored,with LAION CLIP ViT-H/14 and ViT-L/14 at PCA-256 achieving the strongest baseline results.In the UQ phase,Monte Carlo Dropout(MCD),Ensemble,and Ensemble Monte Carlo Dropout(EMCD)are applied and evaluated using uncertainty-aware metrics(UAcc,USen,USpe,UPre).Ensemble methods with PCA-256 provide the best balance between accuracy and reliability.Further improvements are obtained through feature fusion of top-performing extractors at PCA-256.Finally,we propose a feature-fusion-based model trained with a Predictive Entropy(PE)loss function,which outperforms all prior configurations across both standard and uncertainty-aware evaluations,advancing trustworthy deep learning-based skin cancer diagnosis. 展开更多
关键词 Deep learning Machine learning Classification Uncertainty quantification Skin cancer
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