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Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis,treatment and prognosis 认领 引用
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作者 Yao Chen Qiang Zhang Ming-Yang Zhang 《World Journal of Gastroenterology》 SCIE CAS 2026年第5期17-35,共19页
Hepatocellular carcinoma(HCC),the predominant form of primary liver cancer,significantly threatens to global health.Despite considerable advances in diagnostic and therapeutic approaches in recent years,the prognosis ... Hepatocellular carcinoma(HCC),the predominant form of primary liver cancer,significantly threatens to global health.Despite considerable advances in diagnostic and therapeutic approaches in recent years,the prognosis for patients with HCC remains unsatisfactory.The emergence of artificial intelligence(AI),particularly deep learning technologies,offers new hope for improving the diagnosis and treatment of HCC.Researchers have extensively explored ways to integrate deep learning models into the clinical management of HCC patients,which provides a valuable foundation for developing more personalized treatment strategies.Compared with other detection methods,computed tomography(CT)has attracted significant research interest because of its comprehensive advantages,including wide availability and high resolution,making it well suited for AIpowered analysis.This review systematically integrates deep learning technologies for HCC based on CT imaging,while focusing primarily on tumor diagnosis,segmentation,treatment response prediction,and patient prognosis prediction.Moreover,we review popular deep learning networks in various fields and describe the advantages of these prevalent deep learning models for different applications.Furthermore,we discuss the outstanding challenges in applying deep learning to extract information from CT images for the diagnosis and treatment of HCC patients.These insights could provide guidance for subsequent studies. 展开更多
关键词 Hepatocellular carcinoma Computed tomography Deep learning Diagnosis Treatment Prognosis
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Terahertz video analysis for hidden object detection using deep learning techniques 认领 引用
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作者 Parama BAGCHI Olga Sergeevna SUSHKOVA +1 位作者 Alexei Alexandrovich MOROZOV Debotosh BHATTACHARJEE 《Optoelectronics Letters》 EI 2026年第4期223-228,共6页
This paper is based on automatically detecting hazardous and non-dangerous objects from terahertz images.First,we trained a neural network to automatically analyze dangerous and non-dangerous items,which can be used f... This paper is based on automatically detecting hazardous and non-dangerous objects from terahertz images.First,we trained a neural network to automatically analyze dangerous and non-dangerous items,which can be used for experiment with terahertz images generated by a prototype terahertz video system.The system comprises a terahertz video database of people hiding dangerous and non-dangerous objects under their clothing.Secondly,visual geometry group-19(VGG-19)is trained on dangerous and non-dangerous objects from our database.After training,the accuracy received was 99.6%for safe items and 85.85%for dangerous items.We tested the network with various categories of objects not included in the training set and found that most were correctly identified as dangerous and nondangerous items.Also,we have identified some of the critical issues that need to be addressed to make this technology more accessible and widely used.Our work can pave the way for future research in this field and help to address the challenges associated with terahertz imaging technologies.The paper describes some preliminary terahertz video surveillance experiments necessary for developing a natural terahertz video surveillance system. 展开更多
关键词 terahertz video database hazardous objects non dangerous objects terahertz imaging experiment terahertz images terahertz imagesfirstwe neural network deep learning
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Key techniques for precise measuring gas content in deep coal mine:In-situ pressure-and gas-preserved coring 认领 引用 被引量：4
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作者 Ju Li Jianan Li +4 位作者 Tianyu Wang Guikang Liu Zhiqiang He Cong Li Heping Xie 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2025年第4期589-607,共19页
Gas content serves as a critical indicator for assessing the resource potential of deep coal mines and forecasting coal mine gas outburst risks.However,existing sampling technologies face challenges in maintaining the... Gas content serves as a critical indicator for assessing the resource potential of deep coal mines and forecasting coal mine gas outburst risks.However,existing sampling technologies face challenges in maintaining the integrity of gas content within samples and are often constrained by estimation errors inherent in empirical formulas,which results in inaccurate gas content measurements.This study introduces a lightweight,in-situ pressure-and gas-preserved corer designed to collect coal samples under the pressure conditions at the sampling point,effectively preventing gas loss during transfer and significantly improving measurement accuracy.Additionally,a gas migration model for deep coal mines was developed to elucidate gas migration characteristics under pressure-preserved coring conditions.The model offers valuable insights for optimizing coring parameters,demonstrating that both minimizing the coring hole diameter and reducing the pressure difference between the coring-point pressure and the original pore pressure can effectively improve the precision of gas content measurements.Coring tests conducted at an experimental base validated the performance of the corer and its effectiveness in sample collection.Furthermore,successful horizontal coring tests conducted in an underground coal mine roadway demonstrated that the measured gas content using pressure-preserved coring was 34%higher than that obtained through open sampling methods. 展开更多
关键词 Pressure-and gas-preserved coring Deep coal mines coring Gas migration model In-situ gas content
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Chinese DeepSeek: Performance of Various Oversampling Techniques on Public Perceptions Using Natural Language Processing 认领 引用 被引量：2
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作者 Anees Ara Muhammad Mujahid +2 位作者 Amal Al-Rasheed Shaha Al-Otaibi Tanzila Saba 《Computers, Materials & Continua》 SCIE EI 2025年第8期2717-2731,共15页
DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without... DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step,demonstrates remarkable reasoning capabilities of performing a wide range of tasks.DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries.Users possess divergent viewpoints regarding advanced models like DeepSeek,posting both their merits and shortcomings across several social media platforms.This research presents a new framework for predicting public sentiment to evaluate perceptions of DeepSeek.To transform the unstructured data into a suitable manner,we initially collect DeepSeek-related tweets from Twitter and subsequently implement various preprocessing methods.Subsequently,we annotated the tweets utilizing the Valence Aware Dictionary and sentiment Reasoning(VADER)methodology and the lexicon-driven TextBlob.Next,we classified the attitudes obtained from the purified data utilizing the proposed hybrid model.The proposed hybrid model consists of long-term,shortterm memory(LSTM)and bidirectional gated recurrent units(BiGRU).To strengthen it,we include multi-head attention,regularizer activation,and dropout units to enhance performance.Topic modeling employing KMeans clustering and Latent Dirichlet Allocation(LDA),was utilized to analyze public behavior concerning DeepSeek.The perceptions demonstrate that 82.5%of the people are positive,15.2%negative,and 2.3%neutral using TextBlob,and 82.8%positive,16.1%negative,and 1.2%neutral using the VADER analysis.The slight difference in results ensures that both analyses concur with their overall perceptions and may have distinct views of language peculiarities.The results indicate that the proposed model surpassed previous state-of-the-art approaches. 展开更多
关键词 DeepSeek prediction natural language processing deep learning analysis TextBlob imbalance data
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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：3
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作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
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Effect of dynamic disturbance frequency on brittle failure of granite in deep excavation 认领 引用 被引量：2
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作者 Ben-Guo He Hanyi Liu +1 位作者 Xia-Ting Feng Hongyuan Fu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1002-1015,共14页
Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blas... Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blasting,blasting,tunnel boring machine(TBM)vibration,rockburst wave,earthquakes.Specifically,these dynamic sources are characterized by a wide range of wave frequencies f,resulting in differences in failure modes.A series of true-triaxial compression tests were conducted on granite to simulate the excavation-induced stress path in three-dimensional(3D)stresses.Subsequently,a dynamic disturbance with various frequencies was applied to a cuboid specimen,to reveal the behavior associated with brittle failure.The dynamic disturbance with frequencies f of 5 Hz,10 Hz,and 40 Hz generates less disturbed energy components in the granite together with higher peak strength.However,dynamic disturbances with f of 20 Hz and 30 Hz resulted in a lower peak strength;the peak strength of the rock increases sp albeit it decreases at first,then increases.This U-shaped phenomenon relates to the natural frequency of the granite under such stress conditions.Different rock lithologies consisting of diverse mineral composition,respond differently to each sensitive resonance frequency.Interestingly,the weak disturbance stress with a high frequency f and low amplitude A increases the ratio of crack damage to peak strength(scd/sp)in the granite.This leads to the inhibition of the expansion of the granite during the dynamic disturbance process.Multiple penetrating tensileeshear cracks appear in the s3-direction as the disturbance frequency f increases. 展开更多
关键词 True-triaxial compression Disturbance frequency Brittle failure Characteristic strength Deep excavation
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Enhancing Deepfake Detection:Proactive Forensics Techniques Using Digital Watermarking 认领 引用
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作者 Zhimao Lai Saad Arif +2 位作者 Cong Feng Guangjun Liao Chuntao Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期73-102,共30页
With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed... With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics. 展开更多
关键词 Deepfake proactive forensics digital watermarking traceability detection techniques
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
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作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
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Optimization of multi-cluster fracturing in deep reservoirs based on stress field reconstruction effect 认领 引用 被引量：1
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作者 Jinbo Li Siwei Meng +3 位作者 He Liu Suling Wang Kangxing Dong Qiuyu Lu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期227-245,共19页
Since rock plasticity under in-situ conditions poses challenges during fracturing stimulation,extensive research is necessary on deep gas and oil reserves,which will be the primary area of future development.This pape... Since rock plasticity under in-situ conditions poses challenges during fracturing stimulation,extensive research is necessary on deep gas and oil reserves,which will be the primary area of future development.This paper created a competitive,multi-cluster fracture propagation model that considered elastoplastic rock deformation and nonlinear fracture characteristics in deep reservoirs.It also proposed an optimal fracture design of“dense fracture distribution,non-uniform perforation and alternating staged fracturing”based on stress field reconstruction.The findings indicated that suitably reducing the spacing between clusters and increasing the number of perforated clusters minimized local in-situ stress variations through stress interference among fractures.This mitigated the limiting effect of plastic deformation on the propagation of hydraulic fractures,demonstrating a viable approach for enhancing the expansion of fractures in deep reservoirs.The elastoplastic fracture propagation mechanism was examined to elucidate the advantages of close-cutting fracturing technology.The impact of various fracture techniques was analyzed using stress field reconstruction.Alternate fracturing displayed a high degree of stress reconstruction with an extensive propagation range,which facilitated the propagation of multiple fracture clusters in the subsequent fracturing section.The findings offer a theoretical basis for fracture design of deep reservoirs. 展开更多
关键词 Deep reservoir Stress field reconstruction Stage multi-cluster fracturing Elastoplastic crack Competitive propagation
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Landslide susceptibility assessment integrating deep transfer learning and physical models in the Baihetan reservoir area,China 认领 引用 被引量：1
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作者 Ming Peng Yue Wang +5 位作者 Chenyi Ma Haojie Wang Shaoqiang Meng Zhenming Shi Weijiang Chu Jianrong Xu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4382-4401,共20页
Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effe... Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effective regional disaster prevention and mitigation.However,the complexity,model uninterpretability,and data scarcity related to reservoir landslides,particularly when adapting models across diverse geographic regions,present significant challenges.This study proposes an interpretable Deep Transfer Learning model coupled with multi-source data and Physical methods(DTLP).The model is trained on multi-source data from the Three Gorges Reservoir Area(TGRA)and Lower Jinsha River Basin(LJRB),tested in Baihetan Reservoir Area(BHT),addressing the issues of limited data and cross-regional generalization.The physical method captures the effect of dynamic water level changes on slope stability.SHAP values are used to interpret the model,providing clear insights into its internal mechanisms.Results demonstrate that DTLP outperforms TrAdaBoost in data-scarce regions,achieving higher accuracy(AUC=0.953,Accuracy=0.941)with better feature generalization and susceptibility zone identification.Incorporating dynamic water level changes into the physical model enhances identification of high-susceptibility areas and reduces misclassifications.SHAP analysis indicates that elevation,lithology,and distance to river significantly influence the model decisions.Using TGRA as the source domain further validates the superiority of DTLP framework.However,due to the initial discrepancies between TGRA and the target domain,the transferability is constrained to some extent,resulting in models trained on LJRB data outperforming those trained on TGRA data. 展开更多
关键词 Landslide susceptibility assessment Deep transfer learning Reservoir landslides Infiniteslope model SHAP values
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基于DeepSeek智能算法的财务概念框架演进研究——数据资产确认、计量与报告的三维重构 认领 引用 被引量：4
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作者 赵雪艳 孟令云 耿华 《会计之友》 北大核心 2026年第3期114-121,共8页
基于DeepSeek智能算法,探讨了数据资产在财务会计概念框架中的确认、计量与报告问题,提出了“三维重构”理论。文章创新性地引入DeepSeek技术构建“场景—时间—质量”标准,重新定义了数据资产的确认逻辑、计量模式和报告体系,认为数据... 基于DeepSeek智能算法,探讨了数据资产在财务会计概念框架中的确认、计量与报告问题,提出了“三维重构”理论。文章创新性地引入DeepSeek技术构建“场景—时间—质量”标准,重新定义了数据资产的确认逻辑、计量模式和报告体系,认为数据资产的价值实现依赖于算法中介的有效性,会计确认标准应从“控制观”转向“治理观”,财务报告周期需与算法迭代周期同步化,会计信息质量特征体系应纳入算法伦理维度。建议数据资产要素尽快融入相应的财务概念框架体系,相关会计理论需要接入DeepSeek算法构建数据资产的多维度计量,政府也需要加强DeepSeek等智能技术算法的伦理监管。 展开更多
关键词 Deep Seek 新质生产力 数据资产 智能算法 财务概念框架
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Research on Low Visibility Forecast Model of Sea Fog in Beibu Gulf Based on Attention Mechanism-Embedded LSTM Deep Learning 认领 引用 被引量：1
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作者 ZHENG Feng-qin LI Jie +1 位作者 JIN Long LU Qian-qian 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第2期176-185,共10页
To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em... To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em-bedded long short-term memory(ATT-LSTM)deep learning model for sea fog visibility hazard prediction.This archi-tecture seamlessly incorporates ATT into the conventional LSTM neural network framework.This integration enables the model to adaptively assign weights to the input features,thereby distinguishing between salient and non-salient variables.This targeted allocation enhances the contribution of considerable factors within the LSTM forecasting algorithm,opti-mizes input data,and assigns varying levels of attention to each variable.Consequently,the model substantially mitigates prediction errors in multivariate scenarios.An empirical analysis employing an independent dataset encompassing 303 foggy days over a biennial period confirmed the superior performance of the proposed ATT-LSTM model.Comparative evaluations with LSTM,logistic classification regression,and support vector machine classification regression models revealed that the ATT-LSTM model achieved a recall rate of 37%,a precision rate of 48%,an accuracy rate of 91%,and a threat score(TS)of 0.26.Among the assessed methodologies,the ATT-LSTM model outperformed the others in terms of recall,accuracy,and TS metrics.These findings confirm that the ATT-LSTM model offers a potent and innovative deep learning approach for enhancing the accuracy of low-visibility sea fog hazard predictions. 展开更多
关键词 deep learning low visibility attention mechanism prediction model low-probability event
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A Comparative Study of Data Representation Techniques for Deep Learning-Based Classification of Promoter and Histone-Associated DNA Regions 认领 引用
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作者 Sarab Almuhaideb Najwa Altwaijry +2 位作者 Isra Al-Turaiki Ahmad Raza Khan Hamza Ali Rizvi 《Computers, Materials & Continua》 SCIE EI 2025年第11期3095-3128,共34页
Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid(DNA)sequence,making DNA sequence classification an integral step in performing bioinformatics analysis,where la... Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid(DNA)sequence,making DNA sequence classification an integral step in performing bioinformatics analysis,where large biomedical datasets are transformed into valuable knowledge.Existing methods rely on a feature extraction step and suffer from high computational time requirements.In contrast,newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency.In this paper,we investigate the performance of various deep learning architectures:Convolutional Neural Network(CNN),CNN-Long Short-Term Memory(CNNLSTM),CNN-Bidirectional Long Short-Term Memory(CNN-BiLSTM),Residual Network(ResNet),and InceptionV3 for DNA sequence classification.Various numerical and visual data representation techniques are utilized to represent the input datasets,including:label encoding,k-mer sentence encoding,k-mer one-hot vector,Frequency Chaos Game Representation(FCGR)and 5-Color Map(ColorSquare).Three datasets are used for the training of the models including H3,H4 and DNA Sequence Dataset(Yeast,Human,Arabidopsis Thaliana).Experiments are performed to determine which combination of DNA representation and deep learning architecture yields improved performance for the classification task.Our results indicate that using a hybrid CNN-LSTM neural network trained on DNA sequences represented as one-hot encoded k-mer sequences yields the best performance,achieving an accuracy of 92.1%. 展开更多
关键词 DNA sequence classification deep learning data visualization
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A novel deep learning-based framework for forecasting 认领 引用 被引量：1
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作者 Congqi Cao Ze Sun +2 位作者 Lanshu Hu Liujie Pan Yanning Zhang 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第1期22-26,共5页
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep... Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance. 展开更多
关键词 Weather forecasting Deep learning Semantic segmentation models Learnable Gaussian noise Cascade prediction
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Research on the visualization method of lithology intelligent recognition based on deep learning using mine tunnel images 认领 引用 被引量：2
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作者 Aiai Wang Shuai Cao +1 位作者 Erol Yilmaz Hui Cao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第1期141-152,共12页
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction... An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects. 展开更多
关键词 rock picture recognition convolutional neural network intelligent support for roadways deep learning lithology determination
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Face stability analysis of deep tunnels crossing water-rich weak zone 认领 引用 被引量：1
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作者 Xinjin Wang Kai Wang +4 位作者 Guohua Zhang Shenglian Li Luzhao Dan Feng Xiong Dongjie Hua 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期1811-1829,共19页
The deep tunnels are prone to mud and water inrush disasters when crossing water-rich weak zones.A good understanding the hydromechanical behavior of the water-rich weak zone in deep tunnels is the prerequisite for de... The deep tunnels are prone to mud and water inrush disasters when crossing water-rich weak zones.A good understanding the hydromechanical behavior of the water-rich weak zone in deep tunnels is the prerequisite for determining the limit support pressure on the tunnel face.However,the seepage forces within the water-rich weak zone are not well estimated in existing models.To overcome this,an analytical model is proposed in this study to determine the limit support pressure of a deep tunnel crossing the water-rich weak zone.The seepage force in the water-rich weak zone is obtained by solving a group of Laplace equations about the hydraulic head,avoiding complex physical and mathematical approximation in existing models.Besides,the seepage boundary conditions in the water-rich weak zone are considered at the nodes on the Neumann boundary and the Dirichlet boundary.The effectiveness of the proposed model is then validated by numerical simulations and engineering practice.It shows that the proposed model has higher accuracy and wider applicability in estimating the hydraulic head.The proposed model can be used for stability analysis of tunnel faces. 展开更多
关键词 Deep tunnels Water-rich weak zone Limit support pressure Mud and water inrush
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Experimental techniques for qualitative and quantitative characterization of shale oil reservoir space,minerals,and fluids 认领 引用 被引量：1
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作者 Siyu Chen Zhenkai Huang +6 位作者 Guangyou Zhu Xi Li Zhiyong Chen Ruilin Wang Yue Huang Shengnan Liu Bijun Huang 《Energy Geoscience》 EI CAS CSCD 2026年第2期1-16,共16页
Characterizing shale oil reservoirs encompassing pore space,mineralogy,and fluids is fundamental to effective exploration and development.Recent advances in experimental techniques have significantly improved both the... Characterizing shale oil reservoirs encompassing pore space,mineralogy,and fluids is fundamental to effective exploration and development.Recent advances in experimental techniques have significantly improved both the qualitative and quantitative analysis of these components;however,a comprehensive systematic review is lacking.High-resolution imaging technologies,such as Scanning Electron Microscopy(SEM),Field Emission Scanning Electron Microscopy(FE-SEM),and Focused Ion Beam Scanning Electron Microscopy(FIB-SEM),enable detailed visualization of pore structures.Gas adsorption and high-pressure mercury intrusion methods provide accurate pore-scale quantification.Moreover,techniques like X-ray Diffraction(XRD),X-ray Fluorescence Spectroscopy(XRF),and Electron Probe Microanalysis(EPMA)allow precise mineral identification and compositional analysis.Confocal Scanning Laser Microscopy(CSLM),Raman Spectroscopy,Nuclear Magnetic Resonance(NMR),and Rock Pyrolysis provide insights into fluid occurrence and content within shale reservoirs.Based on a comprehensive review of existing research,this study identifies several key future directions:(1)addressing the challenges of nanopore observation in reservoir space characterization while minimizing the impact of sample preparation on pore structure;(2)improving the accuracy of quantitative mineral analysis and developing advanced new technologies for the precise measurement of complex mineral compositions;(3)enhancing the fluid quantitative evaluation of fluids by more effectively restoring subsurface geological conditions.This paper presents a current synthesis and forward-looking perspective on experimental techniques supporting shale oil exploration,aiming to guide future research and technological innovation in this field. 展开更多
关键词 Experimental technique Shale oil Reservoir space Mineral analysis Fluid characterization Qualitative and quantitative analysis
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Achieving strength-ductility synergy of an additively manufactured metastable high-entropy alloy via deep cryogenic treatment followed by laser shock peening 认领 引用 被引量：1
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作者 Zhaopeng Tong Yuze Zhang +4 位作者 Yichen Wang YunXia Ye Jie Yin Yucheng Bao Xudong Ren 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期554-572,共19页
Laser powder bed fusion(LPBF)is an attractive additive manufacturing technology for preparing high-performance high-entropy alloys(HEAs)engineering components.Unfortunately,the existence of inherent thermal residual s... Laser powder bed fusion(LPBF)is an attractive additive manufacturing technology for preparing high-performance high-entropy alloys(HEAs)engineering components.Unfortunately,the existence of inherent thermal residual stress and non-equilibrium microstructures in the additively manufactured components results in unsatisfactory mechanical properties.Herein,we propose a novel strengthening strategy,namely deep cryogenic treatment(DCT)followed by laser shock peening(LSP),to tailor the microstructures and enhance performances of an LPBF additively manufactured metastable HEA.The post-treatment effects of DCT+LSP on the LPBF-fabricated Fe50Mn30Co10Cr10HEA are evaluated in terms of microstructural modifications,residual stress,and microhardness redistribution,as well as tensile properties.Results indicate that a gradient heterogeneous structure is formed on the as-built sample surface,featuring gradient variations in grain size,martensitic phase content,and dislocation density,due to the grain refinement and martensitic phase transformation under DCT+LSP.The initial tensile residual stress on the surface is fully transformed into compressive stress,achieving a peak of-289 MPa,and the surface microhardness attains a maximum of 380.8 HV.The various strengthening mechanisms of gradient heterogeneous structures,as well as the multiple effects of heterodeformation-induced(HDI)hardening,transformation-induced plasticity(TRIP),and twinning-induced plasticity(TWIP),are responsible for achieving strength-ductility synergy.This work provides a practical pathway and valuable scientific insights for enhancing the mechanical behaviors of additively manufactured metastable HEAs via microstructural engineering. 展开更多
关键词 metastable high-entropy alloys laser shock peening deep cryogenic treatment gradient heterogeneous structure strengthening mechanisms
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