In this short paper, we first establish the existence of periodic solutions to parabolic equation in the whole space by using the probability method. Then, the periodicity of some function of stochastic process is als...
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In this short paper, we first establish the existence of periodic solutions to parabolic equation in the whole space by using the probability method. Then, the periodicity of some function of stochastic process is also studied.
In recent years, the use of large-scale pre-trained models for vision-language tasks has gained significant attention and has shown promising results in the video question answering. However, the increasing size of th...
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Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked d...
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Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature ***,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch *** global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor *** features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important ***,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority *** results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)***,feature interpretability analysis validated the effectiveness of the proposed *** suggests that the method holds significant potential for brain tumor image classification.
With the rapid development of economy and ongoing financial reform in China, the phenomenon of financial agglomeration has aroused considerable attention. Currently, the determinants for financial agglomeration have b...
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The rapid growth of online services has led to the emergence of many with similar functionalities,making it necessary to predict their non-functional attributes,namely quality of service(QoS).Traditional QoS predictio...
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The rapid growth of online services has led to the emergence of many with similar functionalities,making it necessary to predict their non-functional attributes,namely quality of service(QoS).Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training,leading to high user data upload *** the help of edge computing,users can upload data to edge servers(ESs)adjacent to them for training,reducing the upload ***,shallow models like matrix factorization(MF)are still used,which cannot sufficiently extract context features,resulting in low prediction *** this paper,we propose a context-aware edge-cloud collaboration framework for QoS prediction,named ***,to reduce the users upload latency,a distributed model training algorithm is designed with the collaboration of ESs and ***,a context-aware prediction model based on convolutional neural network(CNN)and integrating attention mechanism is proposed to improve the *** based on real-world dataset demonstrate that CQEC outperforms the baselines.
This article investigates the secure stabilization of networked systems against denial-of-service (DoS) attacks, by developing a new aperiodic sampled-data transmission scheme without attack detection and correspondin...
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NJmat is a user-friendly,data-driven machine learning interface designed for materials design and *** platform integrates advanced computational techniques,including natural language processing(NLP),large language mod...
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NJmat is a user-friendly,data-driven machine learning interface designed for materials design and *** platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials *** platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular *** automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural ***,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding *** automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials *** latest version(available at https://***/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction *** utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials *** NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training *** providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
The Neural Radiance Field (NeRF) method has emerged as a groundbreaking technique for human reconstruction, enabling the generation of high-quality, photorealistic rendering of reconstructed objects. Despite its promi...
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This paper proposes a noise-resistant coded aperture snapshot spectral imaging (CASSI) reconstruction algorithm based on a spectral awareness network (SANet). The method maps the snapshot compressed measurements to a ...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume an...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification(HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.
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