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CANNSkin:A Convolutional Autoencoder Neural Network-Based Model for Skin Cancer Classification 认领 引用
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作者 Abdul Jabbar Siddiqui Saheed Ademola Bello +3 位作者 Muhammad Liman Gambo Abdul Khader Jilani Saudagar Mohamad A.Alawad Amir Hussain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1142-1165,共24页
Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting ... Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting early detection,yet their performance is often limited by the severe class imbalance present in dermoscopic datasets.This paper proposes CANNSkin,a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance.The autoencoder is trained to reconstruct lesion images,and its latent embeddings are used as features for classification.To enhance minority-class representation,the Synthetic Minority Oversampling Technique(SMOTE)is applied directly to the latent vectors before classifier training.The encoder and classifier are first trained independently and later fine-tuned end-to-end.On the HAM10000 dataset,CANNSkin achieves an accuracy of 93.01%,a macro-F1 of 88.54%,and an ROC–AUC of 98.44%,demonstrating strong robustness across ten test subsets.Evaluation on the more complex ISIC 2019 dataset further confirms the model’s effectiveness,where CANNSkin achieves 94.27%accuracy,93.95%precision,94.09%recall,and 99.02%F1-score,supported by high reconstruction fidelity(PSNR 35.03 dB,SSIM 0.86).These results demonstrate the effectiveness of our proposed latent-space balancing and fine-tuned representation learning as a new benchmark method for robust and accurate skin cancer classification across heterogeneous datasets. 展开更多
关键词 Computational image processing imbalance classification medical image analysis melanoma skin cancer classification
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Enhancing multiclass brain tumor classification through automated segmentation-guided deep learning 认领 引用
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作者 Pattaramon Vuttipittayamongkol Phakorn Charoenthiphakorn +2 位作者 Yarida Fuangfoo Pornnapha Na Phirot Thanawat Sanosiang 《Medical Data Mining》 CAS 2026年第2期15-33,共19页
Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solel... Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solely on classification or treat segmentation and classification as separate tasks,limiting overall performance and interpretability.Methods:This study proposes an end-to-end automated framework that integrates optimized tumor localization with multiclass classification.An optimized segmentation model is first employed to generate tumor masks,which are then overlaid on MRI scans to produce attention-enhanced inputs.These inputs are subsequently used to train a convolutional neural network(CNN)classifier.Experiments were conducted on a public dataset comprising 4,237 MRI scans across four categories:normal,glioma,meningioma,and pituitary tumors.Results:Three widely used segmentation models were systematically evaluated,with an optimized U-Net achieving the best performance(accuracy=0.9939,Dice=0.8893).Segmentation-guided classification consistently improved performance across six CNN architectures,with the most notable gains observed in heterogeneous tumor types such as glioma and meningioma.Among the classifiers,EfficientNet-V2 achieved the highest performance,with an accuracy of 0.9835,precision of 0.9858,recall of 0.9804,and F1-score of 0.9828.The framework was further validated on an independent external dataset,demonstrating consistent performance and robustness across diverse MRI sources.Conclusion:The proposed framework demonstrates strong potential for multiclass brain tumor classification by effectively combining segmentation and classification.This segmentation-driven approach not only enhances predictive accuracy but also improves interpretability,making it more suitable for clinical applications. 展开更多
关键词 brain tumor classification MRI segmentation segmentation-guided CNN multiclass classification tumor localization medical imaging
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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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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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Application of a novel adverse event classification scale in a Latin American gastrointestinal endoscopy unit 认领 引用 被引量：1
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作者 Oscar Corsi Richard Martinez +7 位作者 Joaquín Aguirre Isidora Friedrich Victoria Galeno Vicente Jimenez Pamela Briones Luis Antonio Díaz Alberto Espino Jose Ignacio Vargas 《World Journal of Gastrointestinal Endoscopy》 2026年第1期87-94,共8页
BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is wid... BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is widely used,while the classification for AEs in gastrointestinal endoscopy(AGREE)is a recently proposed alternative aiming for broader applicability.AIM To compare the agreement and correlation between the AGREE and ASGE classification systems using real-world data from a Latin American academic endoscopy unit.METHODS A retrospective analysis of a prospective registry was conducted at a tertiary center in Chile,encompassing all endoscopy-related AEs from 2009 to 2022.Each AE was independently graded using both ASGE and AGREE classification systems by two blinded reviewers per system.Interobserver agreement was calculated using Cohen’s Kappa,and inter-scale correlation was assessed using Spearman’s rank test.RESULTS Of 176655 procedures performed,235 AEs(0.13%)were included.Most events were related to therapeutic procedures,and the most common AEs were cardiorespiratory(42.1%),bleeding(20.9%),and perforation(17.0%).The ASGE system identified 42.1%of cases as incidents and 57.9%as AEs(Kappa=0.83).AGREE classified 46.0%as non-AEs and 54.0%as AEs(Kappa=0.74).A strong correlation between both systems was observed(ρ=0.89;P<0.001).CONCLUSION The AGREE classification strongly correlates with the ASGE lexicon but excludes more cases as non-AEs and shows slightly lower interobserver agreement.These findings support AGREE as a feasible alternative for AE grading in gastrointestinal endoscopy,particularly in diverse clinical environments. 展开更多
关键词 Gastrointestinal endoscopy Adverse events classification Adverse events in gastrointestinal endoscopy American Society for Gastrointestinal Endoscopy Patient safety Latin America
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Email Classification Using Horse Herd Optimization Algorithm 认领 引用
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作者 N Jaya Lakshmi Sangeetha Viswanadham +2 位作者 Appala Srinuvasu Muttipati B Chakradhar B Kiran Kumar 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第1期69-80,共12页
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative... In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems. 展开更多
关键词 email classification optimization technique support vector machine binary classification machine learning
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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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Crack type classification of fissured sandstone using acoustic emission and Gaussian mixture modeling:Effects of fissure inclination angles 认领 引用
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作者 XUE Kang-sheng PU Hai +5 位作者 LU Yin-long CHEN Yan-long WU Yu LI Ming LIU De-jun XU Jun-ce 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第5期2166-2188,共23页
Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification fram... Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification framework based on Gaussian mixture modeling(GMM)for distinguishing acoustic emission(AE)signatures associated with different fracture modes in sandstone samples that contain prefabricated fissures at varying inclination angles.The frequency-domain characteristics of the AE signals were extracted using fast Fourier transform(FFT),while the RA-AF ratio(rise time/amplitude versus average frequency)parameter space was employed to characterize the crack mechanisms.To increase classification accuracy and model robustness,the Bayesian information criterion(BIC)was introduced to determine the optimal number of Gaussian components.Experimental results from uniaxial compression tests reveal that fissure inclination significantly affects crack evolution behavior:low-angle fissures favor shear and hybrid cracks,whereas high-angle fissures cause tensile failure.The proposed GMM-based method effectively identifies tensile,shear,and hybrid cracks with increased objectivity and accuracy,outperforming traditional empirical RA-AF thresholding techniques.This research provides a reliable and generalizable approach for AE signal classification,which presents theoretical insights and practical support for real-time monitoring,early warning,and structural health assessment in fractured rock masses. 展开更多
关键词 acoustic emission sandstone crack classification Gaussian mixture model(GMM) RA-AF analysis fissure inclination
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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
MTSCFNet:a novel framework for improving tree species classification in a subtropical forest using RGB,LiDAR-derived,and GF-2 data 认领 引用
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作者 Linlong Wang Huaiqing Zhang +5 位作者 Rurao Fu Kexin Lei Yang Liu Tingdong Yang Jing Zhang Xiaoning Ge 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期135-158,共24页
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)... Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments. 展开更多
关键词 Tree species classification Deep learning Multi-source remote sensing data Subtropical forest
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Beach morphodynamic characteristics and classifications around Hainan Island,China 认领 引用
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作者 HU Taihuan CHEN Shenliang +4 位作者 ZHONG Xiaojing JI Hongyu SANG Wenxiu LI Peng XU Wei 《Journal of Geographical Sciences》 SCIE CSCD 2026年第2期449-470,共22页
Climate change and anthropogenic activities have profoundly affected coastal systems,making geomorphological research a critical focus for coastal protection and sustainable development.In this study,a comprehensive c... Climate change and anthropogenic activities have profoundly affected coastal systems,making geomorphological research a critical focus for coastal protection and sustainable development.In this study,a comprehensive classification of beach states around Hainan Island is conducted for the first time by utilizing theΩ-RTR model and geological control modes.Six distinct classic beach states ranging from dissipative to reflective are identified:barred dissipative beaches or no-barred dissipative beaches(BD or NBD),barred beaches(B),low-tide terrace or low-tide bar with rip(LTTR or LTBR),and reflective state(R).Among these,the BD and B types are predominant on Hainan Island.Notably,the beach states are subject to multiple factors,such as hydrodynamic forcings,geomorphic features and underlying substrates,and exhibit remarkable spatiotemporal variability.During extreme events,hydrodynamic forcings impact beach states more substantially than geological and geomorphic features do,leading to a more homogeneous distribution of beach states.Under normal circumstance,beach states are predominantly controlled by geological and geomorphic features.Coastal geological and geomorphic features have a pronounced influence on beach morphology and stability.For example,hard substrates underpin wide and stable dissipative beaches,whereas softer substrates lead to narrower,erosion-prone beaches.Three geological control modes are identified,namely,gently sloping hard substrates with dissipative beaches,moderately sloping hard substrates with seasonally variable reflective beaches,and steeply sloping soft substrates with dynamic sandbar-dominated beaches.These findings highlight the necessity of integrating geological settings in tandem with hydrodynamic forcings into coastal management practices.A dual-mode strategy is proposed:maintaining geomorphic self-organization on hard-substrate coasts under normal conditions and implementing hybrid engineering–ecological measures(e.g.,artificial sand replenishment and vegetation restoration)on erosion-prone soft substrates. 展开更多
关键词 beach morphodynamic classification Ω-RTR model underlying substrate geological control extreme climate coastal management
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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
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作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
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Enhancing Surface Water Classification:Integrating Time Series Features and Automated Sampling on Google Earth Engine 认领 引用
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作者 FU Yi YAO Yunlong +3 位作者 WANG Lei SHAN Yuanqi LI Weineng LIU Yuna 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第2期337-350,I0007,共14页
Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus pr... Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data. 展开更多
关键词 surface water mapping machine learning classification performance Sentinel-2 Google Earth Engine(GEE) Songnen Plain,China
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Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning 认领 引用
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作者 Dingran Song Feng Dai +2 位作者 Yi Liu Hao Tan Mingdong Wei 《Engineering》 SCIE EI CSCD 2026年第1期267-283,共17页
Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe ... Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe noise interference,and data scarcity.To address these issues,this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework(LRE-UDAF)for cross-domain MS signal classification.The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation(UDA)module utilizing a bi-classifier disparity metric and entropy regularization.The feature extractor derives high-level representations from the preprocessed signals,which are subsequently fed into two classifiers to predict class probability.Through three-stage adversarial learning,the feature extractor and classifiers progressively align the distributions of the source and target domains,facilitating knowledge transfer from the labeled source to the unlabeled target domain.Source-domain experiments reveal that the feature extractor achieves high effectiveness,with a classification accuracy of up to 97.7%.Moreover,LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness.Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals.Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules.This framework presents an efficient solution for resource-constrained,noise-prone,and data-scarce environments in deep underground engineering,offering significant promise for practical implementations in early disaster warning. 展开更多
关键词 Deep learning Microseismic classification Lightweight design Noise robustness Unsupervised domain adaptation
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Multimodal Graph-Enhanced Vision Transformer for Interpretable Skin Lesion Classification 认领 引用
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作者 Faten SAlamri Noor Ayesha +2 位作者 Afia Zafar Adil Ali Saleem Amjad R.Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1123-1141,共19页
The use of automated skin lesion classification is still a disadvantage,since there is a great visual similarity between benign and malignant lesions.The majority of deep learning methods utilize dermoscopic images on... The use of automated skin lesion classification is still a disadvantage,since there is a great visual similarity between benign and malignant lesions.The majority of deep learning methods utilize dermoscopic images only,without taking into account clinical metadata employed by dermatologists on a regular basis.The following paper proposes a vision-graph multimodal framework that links Image encoding to graph neural networks based on metadata representation through the fusion of learnable attention.The framework focuses on three limitations,which are underutilization of clinical context,absence of interpretability,and suboptimal incorporation of modalities.Gradientweighted Class Activation Mapping++(Grad-CAM++)is used to obtain dual explainability of visual attention,and SHapley Additive exPlanations(SHAP)to obtain feature importance.Examining the HAM10000 and Derm7pt datasets,statistically significant advances(p<0.001)of 89.3%and 92.1%accuracy are obtained,which is 4.1%and 2.7%higher than baselines that can only use images.Focusing on weight analysis will provide metadata with 37.7%averaged variance with an error of 8.4%,which confirms the clinical importance of multimodal modeling.The study of ablation shows that graph-based metadata encoding is 1.4%better than standard multilayer perceptron encoding(p=0.003). 展开更多
关键词 Skin lesion classification vision transformer graph neural network multimodal learning explainable AI medical image analysis
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Pre-Trained RS-VGG19 Model for Remote Sensing Natural Scene Image Classification Using Spark Distributed Framework 认领 引用
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作者 LI Zhaofei ZHANG Yijie +2 位作者 ZHAO Na LIU Guoquan ZHENG Ruiyu 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期843-857,I0003,共15页
Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by ... Remote sensing image classification using deep learning methods faces challenges such as high complexity,significant computational demands,and inefficiency on resource-constrained devices,while also being affected by issues like class similarity and spatial distribution.Current convolutional neural networks rely on stacking small convolutional kernels for feature learning,which results in relatively low classification accuracy,while their dependence on centralized learning architectures with high-performance GPUs/CPUs incurs substantial training costs.Therefore,this paper proposes a distributed rapid classification method for high-similarity natural scene remote sensing images using an improved VGG19 model(RS-VGG19)that combines residual connections and attention mechanisms.By introducing residual connections,the method improves training convergence speed and high-level feature learning ability,effectively preventing gradient vanishing during training.Embedding the SENet visual attention module in the tenth convolution layer allows the model to more specifically extract similar and significant features in remote sensing images.By employing a combination of cross-entropy and center loss functions,the model is able to learn features with reduced intra-class variance and increased inter-class variance,further enhancing classification accuracy.The distributed inference framework Spark is employed for decentralized model training,storing large-scale remote sensing images in the distributed file system HDFS,and accessing the pre-trained RS-VGG19 model in Docker containers on cluster nodes for distributed inference and classification using PySpark.Experimental results show that on two commonly used high-similarity remote sensing image datasets,NWPU-RESISC45 and UCMerced Land-Use,the RS-VGG19 model improves classification accuracy by 6.57%and 8.76%respectively compared to the original VGG19 model,and significantly enhances accuracy compared to other related classification models.This demonstrates the superior performance of the proposed structure and loss function fusion strategy in remote sensing image classification tasks.On the large-scale remote sensing image inference dataset NWPU-RESISC45,while maintaining classification accuracy,the distributed inference framework achieved a speedup of 11.9 when using six nodes,an improvement of 98.33%over theoretical linear speedup(6.00),reducing dependency on high-end hardware resources and significantly improving the classification speed of high-similarity natural scene remote sensing images. 展开更多
关键词 remote sensing image VGG19 residual connection SENet visual attention Spark Docker distributed classification
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Seismic event classification in North China based on machine learning 认领 引用
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作者 Yixiao Zhang Tingting Wang +1 位作者 Ruifeng Liu Zibo Wang 《Earthquake Science》 CAS CSCD 2026年第3期257-272,共16页
Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and e... Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and explosions are highly similar.This study compared feature-based machine learning(ML)and image-based deep learning(DL)methods in event-and stationlevel classification frameworks.The dataset consisted of 1,847 events and more than 43,000 vertical-component waveforms with two input types,40-dimensional feature vectors for ML and spectrogram images for DL.The results showed that the eventlevel models consistently outperformed the station-level models,achieving over 98%accuracy;the station-level models performed well above 94%.On the test set,the ML and DL models exhibited comparable performance;however,the ML models demonstrated better generalization and lower computational demands.In contrast,DL models required fewer manual interventions.The misclassification analysis revealed distinct error patterns across the model types,indicating potential complementarity.These findings highlight the importance of model choice based on the input type,data granularity,and generalization needs.Although DL models are well suited to automated processing,ML approaches provide more robust and efficient solutions for real-world deployment. 展开更多
关键词 seismic event classification machine learning XGBoost EfficientNet time-frequency analysis
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