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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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Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble 认领 引用 被引量：1
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作者 Yi-Bo Wang Jun-Yi Hang Min-Ling Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1248-1261,共14页
Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess... Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations.As a representative approach,LIFT generates label-specific features by conducting clustering analysis.However,its performance may be degraded due to the inherent instability of the single clustering algorithm.To improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble techniques.Specifically,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)algorithm.Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms. 展开更多
关键词 Clustering ensemble expectation-maximization al-gorithm label-specific features multi-label learning
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Causality-Driven Common and Label-Specific Features Learning 认领 引用
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作者 Yuting Xu Deqing Zhang +1 位作者 Huaibei Guo Mengyue Wang 《Journal on Artificial Intelligence》 2024年第1期53-69,共17页
In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the mo... In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the model are effectively improved.Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation.It is well known that the correlation between labels is asymmetric.However,existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels.Based on this,this paper proposes a Causality-driven Common and Label-specific Features Learning,named CCSF algorithm.Firstly,the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels.Then,in the optimization,both l2,1-norm and l1-norm are used to select the corresponding features,respectively.Finally,it is compared with six state-of-the-art algorithms on nine datasets.The experimental results prove the effectiveness of the algorithm in this paper. 展开更多
关键词 Label-specific features learning causal learning asymmetric label correlation common features
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Learning label-specific features for decomposition-based multi-class classification 认领 引用 被引量：1
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作者 Bin-Bin JIA Jun-Ying LIU +1 位作者 Jun-Yi HANG Min-Ling ZHANG 《Frontiers of Computer Science》 SCIE EI CSCD 2023年第6期101-110,共10页
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output codes.Existing works solve t... Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output codes.Existing works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative examples.In this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative examples.Specifically,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster centers.Experiments clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification. 展开更多
关键词 machine learning multi-class classification error-correcting output codes label-specific features
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Intelligent prediction method of rockburst driven by microseismic temporal features in deep near-vertical coal seams 认领 引用 被引量：1
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作者 Huicong Xu Kai Li +7 位作者 Xingping Lai Pengfei Shan Bo Zhang Lianpeng Dai Shangtong Yang Qifeng Guo Xun Xi Zhongming Yan 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第7期2403-2418,共16页
Rockburst has become a major hazard constraining safe production and high-quality capacity release in China's coal mines.During deep mining of near-vertical seams within the Tianshan seismic belt,the coupling of n... Rockburst has become a major hazard constraining safe production and high-quality capacity release in China's coal mines.During deep mining of near-vertical seams within the Tianshan seismic belt,the coupling of nonlinear coal-rock deformation responses with complex geological conditions markedly elevates rockburst risk.To meet the strategic demand for intelligent safe,and efficient mining in rockburst-prone seams,this study integrates geophysics,spatial statistics,big data mining,and deep learning to investigate a steeply dipping coal mine in Xinjiang,China,and systematically analyze the relationship between microseismic activity parameters and mininginduced disturbances.On this basis,a temporal fusion feature identification method for microseismic indicators is proposed.By embedding temporal constraints into a deep-learnng framework,an enhanced temporal fusion transformer(TFT)is developed to predict multiple microseismic indicators.Furthermore,an intelligent rockburst prediction and early-warning approach driven by fused microseismic parameters is established and validated in field applications.Results indicate that hazard risk in the sandwiched rock pillar and the B6 roof areas increases with working-face advance,and the localized damage in the sandwiched rock pillar is more severe than that in the B6 roof.To strengthen feature extraction,a WFTBlock is introduced by combining continuous wavelet transform,Fourier transform,and timestamp alignment to reveal the periodic evolution of spectral and phase characteristics in indicator sequences.The final multi-parameter TFT model is trained jointly with fused features and temporal inputs.Compared with the long short-term memory(LSTM)baseline,the proposed model reduces root mean square error(RMSE)by 47.9% and improves coefficient of determination(R2)by 54.5%,demonstrating substantially enhanced predictive accuracy.Overall,the proposed framework provides technical support for safe and efficient mining of steeply dipping seams and the secure development of key energy bases along the Belt and Road Initiative. 展开更多
关键词 near-vertical coal seams rockburst microseismic spatiotemporal features intelligent prediction
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Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features 认领 引用 被引量：1
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作者 Yanni ZHANG Xiaoyu CHAI +2 位作者 Jinpeng HU Yaxiao NIU Lizhang XU 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期499-516,I0007-I0011,共18页
Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.Thi... Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.This study aims to address this gap by focusing on rapeseed in Jiangsu Province.Multispectral and RGB images captured by unmanned aerial vehicles(UAVs)were taken during key growth stages(budding,flowering,and podding stages).Using the extracted multidimensional features,we developed biomass-yield estimation models using four machine learning techniques.Subsequently,we employed ensemble learning with multidimensional,multi-stage data and used Shapley additive explanation(SHAP)for feature contribution analysis,thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability.Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation,whereas the optimal combination for yield estimation includes three-dimensional(3D)spectral‒textural‒structural features.The synergy of these features,coupled with an ensemble learning model,significantly enhanced the accuracy of rapeseed biomass-yield estimation(biomass:coefficient of determination(R2)=0.72,relative root mean square error(rRMSE)=14.35%;yield:R2=0.68,rRMSE=13.67%).The proposed model also achieved stable prediction results across the variety‒density interaction.Overall,this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns,offering new insights for precision harvesting. 展开更多
关键词 Ensemble learning Decision-making Feature synergy Temporal fit Planting pattern
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Decoupling of Multi-View Facial Features for Cushing’s Syndrome Diagnosis 认领 引用
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作者 Changwei Song Jiaqi Qiang +5 位作者 Hongjun Liu Jianqiang Li Hui Pan Qing Zhao Jiuzuo Huang Shi Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1177-1208,共32页
Cushing’s syndrome(CS)is a rare endocrine disorder characterized by chronic hypercortisolism,and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach.However,existing diagnostic m... Cushing’s syndrome(CS)is a rare endocrine disorder characterized by chronic hypercortisolism,and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach.However,existing diagnostic models suffer from two core bottlenecks:inefficient fusion of deep semantic features and clinical prior features,and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints.To address these limitations,we propose a novel Multi-View Facial Feature Disentanglement Network(MVFFD-Net)for high-precision automatic CS diagnosis.The network takes five standard facial views(frontal,bilateral 45°oblique,and bilateral 90°lateral views)as input,with three key innovations:a bidirectional cross-attention module for synergistic fusion of deep and clinical prior features,a multi-path disentanglement architecture with multi-objective loss for separating view-invariant and view-specific features,and a graph attention fusion framework for adaptive multi-view feature integration.Unlike state-of-the-art models that mainly rely on single-view representations or generic multi-view fusion,MVFFD-Net explicitly integrates clinically guided multimodal fusion,CS-oriented feature disentanglement,and pathology-aware graph-based multi-view aggregation.Experimental results demonstrate that MVFFD-Net achieves an F1-score of 98.78%for CS diagnosis,significantly outperforming representative state-of-the-art methods in the current experimental setting.In this study,the task is defined as subject-level AI-assisted diagnosis to distinguish individuals with CS from matched controls using five standard facial views.Accordingly,the proposed framework is intended to provide non-invasive auxiliary diagnostic support rather than replace standard endocrinological evaluation and biochemical confirmation.More broadly,this clinically informed multi-view learning framework shows promising potential as a non-invasive auxiliary screening tool,though external multi-center validation remains necessary prior to large-scale clinical application. 展开更多
关键词 Cushing’s syndrome feature fusion feature disentanglement multi-view learning deep learning
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HISNET-FF:Hierarchical identification of species using a network with fused cranial and dental features 认领 引用
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作者 Zhong Cao Qiu-Le Tang +16 位作者 Wei-Qi Zeng Kun-Hui Wang Quentin Martinez Ze-Ling Zeng Si-Ning Xie Qiu-Qin Lu Shi-Yun Liu Xiao-Yun Zheng Wen-Hua Yu Jun-Jie Hu Zhong-Zheng Chen Shao-Ying Liu Song Li Fei-Yun Tu Zi-Wen Hong Ming Bai Kai He 《Zoological Research》 SCIE CSCD 2026年第2期404-413,共10页
Accurate taxonomic identification based on mammalian craniodental features remains critical for evolutionary,ecological,and paleontological research,yet conventional approaches are time-intensive and demand expert inp... Accurate taxonomic identification based on mammalian craniodental features remains critical for evolutionary,ecological,and paleontological research,yet conventional approaches are time-intensive and demand expert input.To overcome these limitations,a deep learning framework,HISNET-FF,was developed with a dual-stream architecture that integrates global cranial morphology with local diagnostic signals from teeth and auditory bullae.The model operates within a hierarchical classification pipeline,processing from genus-level discrimination to species-level resolution.Evaluation on an extensive image dataset encompassing 51 species across 18 genera of Talpidae achieved exceptional accuracy at both the genus(99.6%±0.4%)and species(96.5%±1.3%)levels.This species-level performance substantially exceeded that of single-stream models employing either flat(91.2%±2.3%)or hierarchical(93.9%±2.1%)strategies.To support endto-end automation,a YOLO-based annotation module was implemented to localize key morphological traits with 97.8%recall,97.9%precision,and 81.5%mean average precision(mAP@[.50:.95]).Incorporating this module incurred only a marginal reduction of 1.9%in identification accuracy.Thus,HISNET-FF offers a robust and accurate framework that accelerates morphology-based species identification and enables automated taxonomic classification,with strong potential for broader implementation across diverse biological research domains. 展开更多
关键词 Craniodental morphology Deep learning Feature fusion Hierarchical classification Species identification
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Multi-scale keypoints detection and motion features extraction in dairy cows using ResNet101-ASPP network 认领 引用
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作者 Saisai Wu Shuqing Han +5 位作者 Jing Zhang Guodong Cheng Yali Wang Kai Zhang Mingming Han Jianzhai Wu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2028-2040,共13页
Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,charact... Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,characterized by occlusions and large variations in object scale may result in poor detection results.Therefore,we introduce the atrous spatial pyramid pooling(ASPP) module into the shallow layers network of ResNet101,designed to improve the multi-scale feature extraction capability of the model.The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field.Furthermore,seven types of motion features,including tracking up,gait symmetry,step height balance,motion speed variability,head swing amplitude,head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints.Several of these features represent innovative extraction models and attributes,first proposed in this study.Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments.The experiments show that,in comparison with the ResNet50,MobileNet_v2_1.0,and EfficientNet-b0backbone networks,the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels.Therefore,ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module.The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels,respectively,compared to the benchmark network.The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales.In addition,the keypoints under different occlusion conditions improve considerably,especially for small-scale keypoints,demonstrating the capability of the ASPP module for multi-scale feature extraction.By analyzing the distribution of the seven features and health,mild lameness,and severe lameness in dairy cows,it is shown that all the different features play an important role in distinguishing between different levels of lameness. 展开更多
关键词 dairy cows multi-scale keypoints detection ResNet101-ASPP network motion features
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A machine learning-based depression recognition model integrating spiritexpression features from traditional Chinese medicine 认领 引用
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作者 Minghui Yao Rongrong Zhu +4 位作者 Peng Qian Huilin Liu Xirong Sun Limin Gao Fufeng Li 《Digital Chinese Medicine》 CAS CSCD 2026年第1期68-79,共12页
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ... Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis. 展开更多
关键词 Traditional Chinese medicine Spirit Expression Feature fusion Depression Recognition model
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Synthesis of gunfire shock signals with controllable SRS and temporal features 认领 引用
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作者 Yinzhong YAN Jianbin RUAN Yulong LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期447-462,共16页
Aircraft-mounted weapons systems generate intense shock and vibration during combat missions,creating a highly complex,repetitive,and nonstationary environment.Avionic devices and components are susceptible to damage ... Aircraft-mounted weapons systems generate intense shock and vibration during combat missions,creating a highly complex,repetitive,and nonstationary environment.Avionic devices and components are susceptible to damage in severe gunfire shock environments and must undergo shock testing.In the absence of measured data,gunfire shock signals synthesized from Shock Response Spectrum(SRS) should serve as input excitation.However,the synthesis of gunfire shock signals presents several challenges,primarily due to the transient and repetitive nature of gunfire shock itself,as well as the inherent non-linearity in the SRS method.This paper presents a novel method for synthesizing gunfire shock signals that match SRS specifications while maintaining realistic temporal characteristics.The proposed method utilizes a shock-waveform dictionary technique to generate single-shot shock signals with controllable features including initial rise time,effective duration,and repetition intervals.These single-shot signals are then duplicated and concatenated to create multi-shot sequences,with low-frequency compensation applied to meet SRS requirements.The method's effectiveness is demonstrated through a case study simulating the M61A1 aircraft cannon firing at 4 000 rounds per minute,achieving an average error of only 0.35 d B compared to SRS specifications.Further validation across two additional cases with varied single-shot durations and repetition intervals underscores the method's generalizability.The proposed synthesis method provides a practical solution for laboratory testing of avionic equipment under gunfire shock conditions when measured data is unavailable. 展开更多
关键词 Gunfire shock environment Shock response spectrum Shock testing Shock-waveform Temporal features
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Long-range masked autoencoder for pre-extraction of trajectory features in within-visual-range maneuver recognition 认领 引用
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作者 Feilong Jiang Hutao Cui +2 位作者 Yuqing Li Minqiang Xu Rixin Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第1期301-315,共15页
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,... In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems. 展开更多
关键词 Within-visual-range maneuver recognition Trajectory feature pre-extraction Long-range masked autoencoder Kalman filter constraints Intelligent air combat
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Sporadic Alzheimer’s disease with bipolar-like features:a case report and a brief review of the current research status 认领 引用
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作者 Lingzhuo KONG Yan YANG +1 位作者 Weihua ZHOU Shaohua HU 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第4期416-425,共10页
Alzheimer's disease(AD)is among the main causes of cognitive impairment,memory loss,and dementia,particularly in old adults.It has been listed as one of the most expensive,lethal,and burdening diseases of the 21 s... Alzheimer's disease(AD)is among the main causes of cognitive impairment,memory loss,and dementia,particularly in old adults.It has been listed as one of the most expensive,lethal,and burdening diseases of the 21 st century and develops with the process of aging worldwide(Scheltens et al.,2021).Currently,it is widely acknowledged that the typical pathogenesis of AD involves the deposition of amyloid-β(Aβ)and Tau proteins in the cerebral parenchyma and vasculature,intraneuronal neurofibrillary tangles,and the gradual degeneration of synapses(Scheltens et al.,2016;Rostagno,2022).According to several hypotheses,abnormalities and dysfunctions in vascular structure,mitochondrial metabolism,oxidative stress,glucose utilization,and neuroinflammation are considered fundamental for AD pathology(Scheltens et al.,2016). 展开更多
关键词 bipolar features tau proteins amyloid cerebral parenchyma neurofibrillary tangles alzheimers disease ad Alzheimers disease sporadic Alzheimers disease
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Trends,clinicopathological features,and prognosis of 687 cases of early-onset gastric cancer:A single-center,retrospective study 认领 引用
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作者 Hui-Qing Zhang Wei-Lei Wu +2 位作者 Jia-Xin Huang Shu-Ping Xiong Xiao-Dan Chen 《World Journal of Gastrointestinal Oncology》 SCIE 2026年第7期265-275,共11页
BACKGROUND Early-onset gastric cancer(EOGC)presents distinct clinicopathological features compared with late-onset gastric cancer.However,data on temporal trends and prognosis of EOGC in certain regions remain limited... BACKGROUND Early-onset gastric cancer(EOGC)presents distinct clinicopathological features compared with late-onset gastric cancer.However,data on temporal trends and prognosis of EOGC in certain regions remain limited.AIM To investigate the temporal trends,clinicopathological features,and prognosis of EOGC in a regional cancer center over a 10-year period.METHODS This single-center,retrospective study included newly diagnosed patients with EOGC(age≤45 years and histopathologically confirmed gastric adenocarcinoma)admitted to Jiangxi Cancer Hospital between January 1,2013,and December 31,2022.Clinicopathological data were collected from medical records.Temporal trends were analyzed using the Cochran-Armitage trendχ2 test.Overall survival(OS)was estimated using the Kaplan-Meier survival curve,and survival differences were compared using the log-rank test.RESULTS A total of 687 patients were included,with a female predominance(53.28%).The most common primary location was the gastric body(67.83%),and diffuse-type histology predominated(90.15%);35.92%of the patients tested positive for Helicobacter pylori.Common metastatic sites included lymph nodes(14.70%),ovaries(11.20%),and peritoneum(8.44%).From 2013 to 2022,the proportion of EOGC among all gastric cancers declined significantly from 12.05%to 6.53%(P=0.000).However,within the EOGC population,the proportion of patients≤35 years increased(P=0.042).The proportion of overweight patients increased significantly(P=0.010).The median OS of the entire cohort was 47.0 months,with 5-year and 10-year survival rates of 43.8%and 21.8%,respectively.Survival improved significantly over the 10-year period(P=0.000),with median OS extending from 20-27 months in earlier years to 61-74 months in later years.Higher body mass index and radical gastrectomy were associated with better survival(both P<0.01).CONCLUSION EOGC accounts for a decreasing proportion of all gastric cancers but presents at a younger age.The disease is characterized by female predominance,diffuse histology,and high propensity for peritoneal and ovarian metastasis.OS has improved markedly over time,likely due to increased rates of radical surgery,better nutritional status,and advances in systemic therapies. 展开更多
关键词 Early-onset gastric cancer Clinicopathological features Overall survival Prognosis Trend
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Fine-Tune Transfer Learning Model for Deepfake Audio Detection Using Hybrid Features and Data Augmentation 认领 引用
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作者 Rashid Jahangir Nazik Alturki Muhammad Zubair Khan 《Computers, Materials & Continua》 SCIE EI 2026年第9期1316-1334,共19页
Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication.Its realism has raised serious concerns in different applications such ... Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication.Its realism has raised serious concerns in different applications such as digital forensics,cybersecurity,media authentication and voice-based security systems.However,deepfake audio detection still remains difficult.Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely.Variations in speakers,recording conditions and background noise make the task more complex.In addition,dataset imbalance and low diversity in training samples could lead to low robustness in the model.To overcome these limitations,the present study aims to propose a framework of transfer learning-based methods based on a combination of fine-tuned pre-trained models,as well as systematic data augmentation.Augmentation methods are introduced to increase the variability and mimic real acoustic conditions.This approach supports the learning of more stable and generalizable representations for both genuine and manipulated speech.The framework employs three DL models:ResNet50 to capture global spectro-temporal structures,VGGish to extract mid-level semantic audio embeddings and YAMNet to identify fine-grained temporal irregularities associated with synthetic speech artifacts.Features from these models are fused through concatenation to construct a unified hybrid feature space.A feature selection stage then reduces redundancy before classification using a lightweight model.Experimental results demonstrate the superiority of the proposed hybrid approach and achieved an accuracy of 99.7%.This performance significantly outperformed individual baseline models and achieved strong generalization across diverse acoustic conditions. 展开更多
关键词 Deepfake audio detection transfer learning hybrid feature fusion data augmentation audio forensics synthetic speech detection
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Tongue age:a new dimension for disease risk assessment based on tongue-coating microbiome and image features 认领 引用
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作者 Yanfei Liu Yiwen Li +2 位作者 Jianqing Ju Yanwu Xu Yue Liu 《Acupuncture and Herbal Medicine》 CAS CSCD 2026年第1期6-9,共4页
Cardiovascular disease(CVD)is characterized by high incidence,disability burden,and mortality.Early warning and precise prognosis assessment are crucial for reducing disease burden[1].According to Traditional Chinese ... Cardiovascular disease(CVD)is characterized by high incidence,disability burden,and mortality.Early warning and precise prognosis assessment are crucial for reducing disease burden[1].According to Traditional Chinese Medicine(TCM),tongue appearance changes can reflect organ functions such as blood circulation and nutrient metabolism.The tongue surface provides an accessible window for host physiology. 展开更多
关键词 host physiology tongue coating microbiome image features traditional chinese medicine tcm tongue appearance changes precise prognosis assessment tongue surface reducing disease burden according cardiovascular disease cvd
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Multimodal deep learning with time-frequency health features for battery SOH and RUL prediction 认领 引用 被引量：3
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作者 Rongzheng Wang Le Chen +8 位作者 Jiahao Xu Fei Yuan Junjie Han Zongrun Li Zekun Li Yiwei Zhang Peiyan Li Lipeng Zhang Zhouguang Lu 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第2期303-314,I0009,共12页
This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-... This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction. 展开更多
关键词 State of health Remaining useful life Feature selection Electrochemical impedance spectroscopy Machine learning
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Joint Label-Specific Features and Correlation Information for Multi-Label Learning 认领 引用 被引量：7
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作者 Xiu-Yi Jia Sai-Sai Zhu Wei-Wei Li 《Journal of Computer Science & Technology》 SCIE EI CSCD 2020年第2期247-258,共12页
Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each othe... Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each other,and the correlation information has shown promising strength in improving multi-label learning.In this study,we propose a novel multilabel learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process.Firstly,we learn a sparse weight parameter vector for each label based on the linear regression model,and the label-specific features can be extracted according to the corresponding weight parameters.Secondly,we constrain label correlations directly on the output of labels,not on the corresponding parameter vectors which conflicts with the label-specific feature learning.Specifically,for any two related labels,their corresponding models should have similar outputs rather than similar parameter vectors.Thirdly,we also exploit the sample correlations through sparse reconstruction.The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods.The proposed method ranks in the 1st place at 66.7%case and achieves optimal average rank in terms of all evaluation measures. 展开更多
关键词 multi-label learning label-specific feature sparse reconstruction label correlation sample correlation
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Clinical features and prognosis of orbital inflammatory myofibroblastic tumor 认领 引用
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作者 Jing Li Liang-Yuan Xu +9 位作者 Nan Wang Rui Liu Shan-Feng Zhao Ting-Ting Ren Qi-Han Guo Bin Zhang Hong Zhang Hai-Han Yan Yu-Fei Zhang Jian-Min Ma 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第1期105-114,共10页
AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital ... AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis. 展开更多
关键词 inflammatory myofibroblastic tumor orbital disease clinical features prognosis
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Recognition of Pedestrians’Street-Crossing Intentions Based on Skeleton Features 认领 引用
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作者 LU Jushou CHEN Hao +2 位作者 BAI Yuchuan HU Chuan ZHANG Xi 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期305-318,共14页
An integrated method is proposed to solve the problem of frequent conflicts between autonomous vehicles and pedestrians in the street crossing scene.The method involves pedestrian detection,tracking,and intention reco... An integrated method is proposed to solve the problem of frequent conflicts between autonomous vehicles and pedestrians in the street crossing scene.The method involves pedestrian detection,tracking,and intention recognition.First,an enhanced YOLOv8 is introduced by combining the C2f CA module to achieve accurate pedestrian detection,tracking and pose estimation.Second,a variety of intention recognition features are proposed to characterize the position and pose of pedestrians in spatial and time domains.Finally,by taking the feature data as input for the base learners,the intention classification model is proposed based on the Stacking model with SVM,KNN,and random forest as the base learners and XGBoost as the meta learner.The experimental results show that the enhanced YOLOv8 improves the detection accuracy by 5.4%compared with the original model,and the intention recognition based on the Stacking model can achieve 94.0%accuracy on the JAAD dataset,which is improved by more than 3.4%compared with the existing intention recognition models.Furthermore,when different parts of a pedestrian are occluded,the accuracy of the Stacking model still reaches 65.8%—73.3%,which verifies the robustness of the proposed model.The proposed model provides reliable inputs for decision planning of autonomous vehicles,which is conducive to improving the safety of self-driving. 展开更多
关键词 object detection intention recognition Stacking model skeleton features
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