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Exploring a Novel Conv-Transformer Network for Multi-Modality Heart Segmentation 认领 引用
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作者 Youyou Ding Hao Dang +4 位作者 Jiayi Luo Xiaoyu Zhuo Ningyu Huang Junsheng Xiao Zongwang Lv 《iRADIOLOGY》 CSCD 2026年第1期13-22,共10页
Background:In recent years,deep convolutional neural networks(CNNs)have achieved great successes in medical imaging.However,it is difficult to obtain accurate pathological information for clinical diagnosis and treatm... Background:In recent years,deep convolutional neural networks(CNNs)have achieved great successes in medical imaging.However,it is difficult to obtain accurate pathological information for clinical diagnosis and treatment by leveraging single-modality medical images.This study aims to provide an efficient multimodality whole heart segmentation method for the diagnosis of coronary heart disease.Methods:We propose SFAM-TransUnet for multimodality whole heart segmentation,a novel deep learning framework combining CNNs and transformers.Primarily,the method integrates CNNs and visual transformers(Vits)into a unified fusion framework.Specifically,the shallow feature fusion module is designed to connect MRI and CT images,thereby providing a powerful and efficient multimodality fusion backbone for semantic segmentation.Furthermore,we propose a fusion ViT(FViT)module including self-attention(SA)and adaptive mutual boost attention(Ada-MBA)to enhance contextual information within and across modalities.The Ada-MBA module assigns attention to semantic perception regions by calculating SA and cross-attention,which improves the ability to understand context from the different modalities.Extensive experiments are con-ducted on the clinical Multi-Modality Whole Heart Segmentation datasets.Results:We successfully improved the whole heart segmentation DSCs to 0.902(AA),0.920(LV-blood),0.863(LA-blood),and 0.837(LV-myo),the HDs to 9.886(AA),9.947(LV-blood),11.911(LA-blood),and 13.599(LV-myo),the PSNR values to 33.577(AA),30.091(LV-blood),32.055(LA-blood),and 29.837(LV-myo),SSMI values to 0.901(AA),0.818(LV-blood),0.765(LA-blood),and 0.743(LV-myo).This demonstrate SFAM-TransUnet outperforms various alternative methods.Conclusions:We propose SFAM-TransUnet,an efficient framework tailored for whole heart segmentation that combines CNNs and transformers.It provides a powerful multimodality fusion network to improve the performance of whole heart semantic segmentation.These results demonstrate the efficacy of SFAM-TransUnet in integrating relevant information between different modalities in multimodal tasks. 展开更多
关键词 deep learning image segmentation multi-modality TransUNet
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On the Integration of GAI and Multi-modality in English Teaching:A Study on New Language Teaching Model Construction 认领 引用
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作者 LI Kunmei HU Kun 《US-China Education Review(A)》 2025年第7期526-532,共7页
This article systematically integrates the powerful generative features of Generative Artificial Intelligence(GAI)with the synergistic features of multi-modal technology and explores a new pedagogical approach to effe... This article systematically integrates the powerful generative features of Generative Artificial Intelligence(GAI)with the synergistic features of multi-modal technology and explores a new pedagogical approach to effective language teaching under the context of a lack of active engagement and motivation,and limited accessibility and dynamism of educational materials in traditional English courses in China’s advanced education.This study proposes a novel language teaching model(a three-tier structure)based on a critical review of the usage of GAI and multi-modality in educational environments.This new language teaching model combines GAI with multi-modal technology and centers around the G-M4 cycle(Generation-Input-Interaction-Output-Monitor/Feedback).This model means empowered generative capabilities,a more dynamic and interactive learning environment,multi-modal and creative output,and effective evaluation and prompt feedback.Furthermore,critical aspects that require attention,such as data privacy and ethical responsibilities,are also illustrated. 展开更多
关键词 Generative Artificial Intelligence multi-modal technology English teaching GAI integration
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Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots 认领 引用 被引量：1
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作者 赵其杰 《Journal of Shanghai University(English Edition)》 2006年第3期281-281,共1页
With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much att... With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues. 展开更多
关键词 service robot multi-modality human-robot interaction user model interaction protocol information perception and feedback.
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Multi-modality hierarchical fusion network for lumbar spine segmentation with magnetic resonance images 认领 引用 被引量：1
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作者 Han Yan Guangtao Zhang +1 位作者 Wei Cui Zhuliang Yu 《Control Theory and Technology》 EI CSCD 2024年第4期612-622,共11页
For the analysis of spinal and disc diseases,automated tissue segmentation of the lumbar spine is vital.Due to the continuous and concentrated location of the target,the abundance of edge features,and individual diffe... For the analysis of spinal and disc diseases,automated tissue segmentation of the lumbar spine is vital.Due to the continuous and concentrated location of the target,the abundance of edge features,and individual differences,conventional automatic segmentation methods perform poorly.Since the success of deep learning in the segmentation of medical images has been shown in the past few years,it has been applied to this task in a number of ways.The multi-scale and multi-modal features of lumbar tissues,however,are rarely explored by methodologies of deep learning.Because of the inadequacies in medical images availability,it is crucial to effectively fuse various modes of data collection for model training to alleviate the problem of insufficient samples.In this paper,we propose a novel multi-modality hierarchical fusion network(MHFN)for improving lumbar spine segmentation by learning robust feature representations from multi-modality magnetic resonance images.An adaptive group fusion module(AGFM)is introduced in this paper to fuse features from various modes to extract cross-modality features that could be valuable.Furthermore,to combine features from low to high levels of cross-modality,we design a hierarchical fusion structure based on AGFM.Compared to the other feature fusion methods,AGFM is more effective based on experimental results on multi-modality MR images of the lumbar spine.To further enhance segmentation accuracy,we compare our network with baseline fusion structures.Compared to the baseline fusion structures(input-level:76.27%,layer-level:78.10%,decision-level:79.14%),our network was able to segment fractured vertebrae more accurately(85.05%). 展开更多
关键词 Lumbar spine segmentation Deep learning Multi-modality fusion Feature fusion
Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm 认领 引用 被引量：1
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作者 宋红 李佳佳 +1 位作者 王树良 马婧婷 《Journal of Central South University》 SCIE EI CAS 2014年第1期287-292,共6页
A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-sp... A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration. 展开更多
关键词 multi-modal image registration affine transformation B-splines free-form deformation （FFD） L-BFGS
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The Peasant Identity in ZHAO's Comic Sketches in the Perspective of Multi-modality Theory 认领 引用
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作者 ZHANG Ting-guo SHI Li-na 《US-China Foreign Language》 2014年第9期712-727,共16页
In recent years, the research on identity has brought about a great importance in sociolinguistic research. However, the research on the construction of peasant identity in comic sketches or in real life is rare. The ... In recent years, the research on identity has brought about a great importance in sociolinguistic research. However, the research on the construction of peasant identity in comic sketches or in real life is rare. The topic of this research is ＂The Peasant Identity in ZHAO＇s Comic Sketches in the Perspective of Multi-modality Theory＂, which is for the purpose to explore the construction of ZHAO＇s peasant identity in his performances. This paper takes both quantitative and qualitative research methods and the data for the research are collected from ZHAO＇s comic sketches in the Spring Festival Gala. Based on the multi-modality theory and for the purpose to find how peasant identity is constructed in his comic sketches, the research finds that ZHAO＇s peasant identity in his comic sketches could be constructed through the language, body language, costumes and stage design modalities, which are revealed in detail in section ＂Research Results＂. In addition, this research also achieves the fact that people could use different kinds of semiotics to communicate with each other in real life, and benefits us with a pretty new understanding of peasant emotion and peasant identity in China. What is more, it enables us to perceive that language cannot only reflect people＇s life, their social activities, but also reveal people＇s social identity 展开更多
关键词 multi-modality peasant identity comic sketches body language
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On Multi-Modality in English Listening Teaching 认领 引用
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作者 Zhang Rui 《International Journal of Technology Management》 2013年第12期115-117,共3页
Listening is the breakthrough for conquering English castle, it is not only the requirement of English test, but also the practical use of English knowledge and the embodiment of English comprehensive ability. Listeni... Listening is the breakthrough for conquering English castle, it is not only the requirement of English test, but also the practical use of English knowledge and the embodiment of English comprehensive ability. Listening teaching plays a crucial role in foreign language teaching. However, the effect of listening teaching is undesirable. In recent years, multi-modality theory has been focused by many researchers. In view of particularity of the listening teaching, it is urgent to apply the multi-modality theory to English listening teaching which will produce very good teaching result. 展开更多
关键词 listening multi-modality teaching
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On a New Diffeomorphic Multi-Modality Image Registration Model and Its Convergent Gauss-Newton Solver 认领 引用
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作者 Daoping ZHANG Anis THELJANI Ke CHEN 《Journal of Mathematical Research with Applications》 CSCD 2019年第6期633-656,共24页
In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradie... In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence;a multilevel technique is employed to speed up the initialization and avoid likely local minima of the underlying functional. Finally, numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation. 展开更多
关键词 Multi-modal image registration variational model diffeomorphic transformation
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GaitMAFF:Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios 认领 引用
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作者 Zhongbin Luo Zhaoyang Guan +2 位作者 Wenxing You Yunteng Wang Yanqiu Bi 《Computers, Materials & Continua》 SCIE EI 2026年第5期540-558,共19页
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini... Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications. 展开更多
关键词 Gait recognition multi-modal fusion adaptive feature fusion skeleton map silhouette
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Asymmetric flow in multi-mode continuous casting and rolling mold under electromagnetic braking 认领 引用
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作者 Jing-Pei Shi Xiao-Xian Shang +3 位作者 Xin-Yue Shi Zhan-Long Piao Cai-Jun Zhang Li-Guang Zhu 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第1期159-179,共21页
The flow behavior of molten steel in the thin slab mold under high casting speed conditions was investigated,with a focus on the multi-mode continuous casting and rolling mold.A steel-slag two-phase flow model was est... The flow behavior of molten steel in the thin slab mold under high casting speed conditions was investigated,with a focus on the multi-mode continuous casting and rolling mold.A steel-slag two-phase flow model was established using large eddy simulation,the volume of fluid,and magnetohydrodynamics methods through numerical simulation.The maximum flow velocity and wave height at the steel-slag interface within the mold are critical evaluation criteria for analyzing asymmetric flow under varying casting speeds and electromagnetic braking.The results indicate that the asymmetric flows within the mold do not occur synchronously.The severity of the asymmetric flow correlates with the velocity difference across the steel-slag interface.A greater biased flow prolongs the time required to revert to a steady state.When the magnetic field intensity is set to 0.24 T and the magnetic pole position is at 390 mm from the steel-slag interface,this configuration can reduce the velocity of the steel-slag interface,thereby mitigating the asymmetric flow.Additionally,it can diminish the velocity,impact depth,and impact intensity on the narrow face of the jet,thus improving the distribution of velocity and turbulent kinetic energy within the mold.This configuration prolongs the time required for the steel-slag interface to transition from a stable state to its maximum velocity and shortens the time for the interface to return to stability from an unstable state.Moreover,it ensures the positional stability of the steel-slag interface,confining its position within−3 mm. 展开更多
关键词 Multi-mode continuous casting and rolling mold Asymmetric flow Steel-slag interface Turbulent kinetic energy Electromagnetic braking
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MDGET-MER:Multi-Level Dynamic Gating and Emotion Transfer for Multi-Modal Emotion Recognition 认领 引用
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作者 Musheng Chen Qiang Wen +2 位作者 Xiaohong Qiu Junhua Wu Wenqing Fu 《Computers, Materials & Continua》 SCIE EI 2026年第3期872-893,共22页
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method... In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets. 展开更多
关键词 Multi-modal emotion recognition dynamic gating emotion transfer module cross-modal dynamic alignment noise robustness
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Railway Track Defect Detection Based on Dynamic Multi-Modal Fusion and Challenging Object Enhanced Perception 认领 引用
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作者 Yaguan Wang Linlin Kou +3 位作者 Yang Gao Qiang Sun Yong Qin Genwang Peng 《Structural Durability & Health Monitoring》 EI 2026年第2期195-212,共18页
The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defec... The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset. 展开更多
关键词 Railway safety track defect detection multi-modal data object detection
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Adaptive Reinforcement Learning with Multi-Modal Perception for Autonomous Formation Control and Exploration in Large-Scale Multi-UAV Swarms 认领 引用
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作者 Ziyuan Ma Huajun Gong Xinhua Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期63-83,共21页
To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,w... To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination. 展开更多
关键词 multiple unmanned aerial vehicle(multi-UAV)swarm autonomous control reinforcement learning(RL) multi-modal perception pigeon flock optimization(PFO)
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Green and mild synthesis of Ca-MOF/COF functionalized silica microspheres in an acid-base tunable deep eutectic solvent for multi-mode chromatography 认领 引用 被引量：2
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作者 Yuanfei Liu Wanjiao Wei +5 位作者 Xu Liu Rui Hua Yanjuan Liu Yuefei Zhang Wei Chen Sheng Tang 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第1期547-551,共5页
Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through... Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through covalent bonds exhibits excellent structural stability.It has been shown that the stationary phases prepared by combining MOF and COF can make up for the poor stability of MOF@SiO2,and the MOF/COF composites have superior chromatographic separation performance.However,the traditional methods for preparing COF/MOF based stationary phases are generally solvent thermal synthesis.In this study,a green and low-cost synthesis method was proposed for the preparation of MOF/COF@SiO2 stationary phase.Firstly,COF@SiO2 was prepared in a choline chloride/ethylene glycol based deep eutectic solvent(DES).Secondly,another acid-base tunable DES prepared by mixing p-toluenesulfonic acid(PTSA)and 2-methylimidazole in different proportions was introduced as the reaction solvent and reactant for rapid synthesis of MOF/COF@SiO2.Compared with the toxic transition metal-based MOFs selected in most previous studies,a lightweight and non-toxic S-zone metal(calcium) based MOF was employed in this study.PTSA and calcium will form the calcium/oxygen-containing organic acid framework in acidic DES,which assembles with terephthalic acid dissolved in basic DES to form MOF.The strong hydrogen bonding effect of DES can facilitate rapid assembly of Ca-MOF.The obtained Ca-MOF/COF@SiO2 can be used for multi-mode chromatography to efficiently separate multiple isomeric/hydrophilic/hydrophobic analytes.The synthesis method of Ca-MOF/COF@SiO2 is green and mild,especially the use of acid-base tunable DES promotes the rapid synthesis of non-toxic Ca-MOF/COF@silica composites,which offers an innovative approach of greenly synthesizing novel MOF/COF stationary phases and extends their applications in the field of chromatography. 展开更多
关键词 Metal organic framework Covalent organic framework Deep eutectic solvent Silica composites Multi-mode chromatography
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Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems 认领 引用
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作者 Mu Niu Keshuang Han +3 位作者 Xudong Zhong Baoquan Ren Pinchang Zhang Ji He 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期618-629,共12页
This paper exploits multi-modal Physical(PHY)-layer features in terms of artificial fingerprint,In-phase/Quadrature(IQ)imbalance and Angle of Arrival(AoA)to propose a novel PHY-layer authentication framework for a Mil... This paper exploits multi-modal Physical(PHY)-layer features in terms of artificial fingerprint,In-phase/Quadrature(IQ)imbalance and Angle of Arrival(AoA)to propose a novel PHY-layer authentication framework for a Millimeter Wave(mmWave)Multiple-Input Multiple-Output(MIMO)Unmanned Aerial Vehicle(UAV)-enabled communication system.First,we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility.To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques,artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs.Then,we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities.To theoretically evaluate the effectiveness of the proposed authentication framework,the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing.Finally,we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models,and also discuss the relation between system security and communication service quality under different artificial fingerprint level. 展开更多
关键词 Multi-modal PHY-layer authentication UAV-enabled communication systems MmWave MIMO technology Wireless security Artificial fingerprint
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A Multi-Modal Deep Learning Framework for Robust Polymorphic Malware Detection 认领 引用
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作者 Phil Steadman Paul Jenkins +1 位作者 Rajkumar Singh Rathore Chaminda Hewage 《Computers, Materials & Continua》 SCIE EI 2026年第9期1967-1986,共20页
Modern malware is increasingly employing polymorphism,packing,and metamorphism to evade traditional signature-based detection.Because of this,there is an urgency to have more reliable classification systems.Visual mal... Modern malware is increasingly employing polymorphism,packing,and metamorphism to evade traditional signature-based detection.Because of this,there is an urgency to have more reliable classification systems.Visual malware analysis,where binaries are converted into grayscale images,has demonstrated potential in revealing structural patterns of malware family classification.However,recent methods mostly rely on single-stream,lightweight Convolutional Neural Networks(CNNs).These models have a major blind spot.The visual representation textures can be heavily obscured without changing the underlying malicious code,causing severe performance drops on newer or even rare malware classes.This paper presents a Hybrid Multi-Modal Deep Learning framework to fix this vulnerability.The proposed dual-stream architecture uses image recognition via EfficientNetB0 alongside metadata analysis using 1D-convolutional byte embeddings.This paper evaluated the framework on the modern MalwareVision-2025 dataset(approximately 125000 samples)and the legacy Malimg dataset(9339 samples).On MalwareVision-2025,the model reached a weighted accuracy of 88.44%and achieved 100%benign recall on the evaluated split.The testing across both datasets shows that combining visual and structural features reduces modality collapse.This creates a much stronger system compared to using either input type on its own.In particular,the hybrid approach improves detection performance on deeply hidden contemporary threats,including the AveMariaRAT and CobaltStrike families. 展开更多
关键词 Polymorphic malware deep learning multi-modal convolutional neural networks EfficientNetB0 cybersecurity MalwareVision-2025 Malimg
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Personalized Fashion Recommendation Fusing Multi-Behavior and Multi-Modal Features 认领 引用
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作者 Xin Lu Jian-Hong Wang Kuo-Chun Hsu 《Computers, Materials & Continua》 SCIE EI 2026年第7期962-981,共20页
Aiming at the problems of data sparsity,uneven behavior weight allocation,and insufficient timeliness modeling existing in traditional recommendation systems in the scenario of personalized fashion recommendation,this... Aiming at the problems of data sparsity,uneven behavior weight allocation,and insufficient timeliness modeling existing in traditional recommendation systems in the scenario of personalized fashion recommendation,this paper proposes a personalized recommendation method that integrates multi-behavior weights and multi-modal features.A dynamic weighted collaborative filtering algorithm is designed,which comprehensively considers the multi-dimensional behaviors of users,and introduces a time attenuation factor to construct a time-sensitive user-item scoring matrix,so as to more accurately depict the dynamic changes of user interests.A multi-modal deep fusion framework is built:ResNet-50 is used to extract commodity image features,and the pre-trained BERT model is combined to extract text features;meanwhile,the multi-head self-attention mechanism is adopted to realize semantic-level interaction and adaptive fusion of cross-modal features,thereby enhancing the expressive ability of commodity representation.Then,user preference score prediction is carried out based on the deep predictive network to generate a personalized recommendation list.Experimental results on real e-commerce datasets show that the method in this paper achieves 0.703 and 0.491 on HR@5 and NDCG@5,respectively,which is significantly superior to other baseline models.Ablation experiments further verify the effectiveness of each module including time attenuation,multi-behavior weights and multi-modal features.This study provides a more accurate,dynamic and transparently interpretable personalized recommendation solution for e-commerce platforms,and has certain theoretical value and practical significance. 展开更多
关键词 Fashion recommendation collaborative filtering multi-modal fusion personalized recommendation deep learning
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Comparative evaluation of threshold-based and CNN-based segmentation methods for multi-modal digital images of geotechnical materials 认领 引用
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作者 Zhijie Jian Jiangfeng Liu +4 位作者 Shijia Ma Zhipeng Wang Qing Jian Ruinian Sun Chenghao Wu 《Intelligent Geoengineering》 2026年第1期11-35,共25页
This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network(CNN)models across multi-modal geotechnical m... This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network(CNN)models across multi-modal geotechnical material images,including computed tomography(CT)and scanning electron microscopy(SEM)data.Based on their characteristics,thresholding methods are classified into three categories:histogram-based,entropy-based,and other approaches.Four types of geotechnical material CT images and two types of SEM images were selected as the evaluation datasets,and an objective assessment criterion that does not require manual annotation was proposed.The results indicate that the performance of different thresholding algorithms varies considerably across imaging modalities:the Otsu method performs best on coal and sandstone CT images,the Liu-S method(implemented in the JHNY-DPM software)excels on sandy soil CT images,and the Yen method demonstrates strong robustness on SEM images.However,all thresholding methods fail to effectively segment granite images with uneven grayscale distributions.In contrast,deep learning models exhibit superior performance across all modalities,with U-Net achieving the highest accuracy and stability during both training and validation without noticeable overfitting,significantly outperforming Fcn and Deeplabv3.Further experiments on a combined CT-SEM dataset reveal that despite domain adaptation challenges,U-Net can consistently segment complex geotechnical structures across different imaging modalities.Overall,the analysis demonstrates that deep learning models substantially enhance the accuracy and robustness of multi-modal geotechnical image segmentation,providing guidance for algorithm selection and supporting the unified processing of multi-source imaging data toward automation and intelligent analysis in digital geotechnical research. 展开更多
关键词 Multi-modal image segmentation Thresholding algorithms Geotechnical materials Digital rock images Deep learning JHNY-DPM
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Real-Time Multi-Modal Image Matching Based on Lightweight Learning Model 认领 引用
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作者 Jixuan Li Chenzhong Gao +2 位作者 Desheng Weng Yute Li Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2026年第3期253-262,共10页
This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network,targeting the challenges of multi-modal image matching in multi-source data analysis.Th... This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network,targeting the challenges of multi-modal image matching in multi-source data analysis.The algorithm addresses significant multi-modal feature differences and real-time processing limitations by incorporating key technologies including reparameterization in convolutional neural networks,multi-scale image pyramids,and feature fusion modules.The matching process employs a coarse-to-fine strategy,ensuring robust performance in complex environments.Experimental results using multi-modal datasets demonstrate that the proposed algorithm achieves superior accuracy and speed,with a success rate of 98.3%and an average matching time of 30.51 ms per 500×500 image pair.These results highlight the practical value and strong generalization capability of the algorithm in real-time applications. 展开更多
关键词 image matching multi-modal images deep learning neural network lightweight
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YOLOv8-RGBT-MT:A Fast Multi-Modal Fusion Network for Worker-and-Equipment Detection on Construction Sites 认领 引用
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作者 Yan Li Cunxin Sun Baihai Zhang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期244-252,共9页
To address the challenges of dusty,foggy and other complex construction site environments leading to the failure of visible light imaging and difficulties in small target detection,as well as the high resource consump... To address the challenges of dusty,foggy and other complex construction site environments leading to the failure of visible light imaging and difficulties in small target detection,as well as the high resource consumption hindering model deployment,an enhanced and lightweight algorithm is proposed.This algorithm employs a hybrid architecture,integrating red green blue(RGB)(visible light)and thermal infrared(RGBT)multi-modal images through a fusion framework based on you only look once(YOLO)version 8 and Mamba-Transformer(MT).We refer to this integrated model as YOLOv8-RGBT-MT.In terms of network improvements,a frequency enhancement module is first employed to enhance visible light and infrared images.And then,a module integrating Mamba and Transformer components is designed to replace base convolutional blocks in the backbone network,thereby expanding the receptive field of the model and improving feature extraction in complex backgrounds.Finally,a multi-modal feature fusion mechanism is introduced,through which complementary information from visible and infrared images is effectively integrated via an adaptive weighting strategy,so that both the detection accuracy and robustness for small targets are enhanced.Experimental results demonstrate that,compared to YOLOv8-RGBT,the enhanced algorithm achieves an improvement of 18.7%in mAP50,while reducing the number of inference time by 79.7%. 展开更多
关键词 RGBT images multi-modal feature fusion YOLOv8 Mamba-Transformer real-time object detection
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