標(biāo)題: Titlebook: Data Science; 10th International C Chengzhong Xu,Haiwei Pan,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The Au [打印本頁(yè)] 作者: Gram114 時(shí)間: 2025-3-21 19:21
書目名稱Data Science影響因子(影響力)
書目名稱Data Science影響因子(影響力)學(xué)科排名
書目名稱Data Science網(wǎng)絡(luò)公開(kāi)度
書目名稱Data Science網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Data Science被引頻次
書目名稱Data Science被引頻次學(xué)科排名
書目名稱Data Science年度引用
書目名稱Data Science年度引用學(xué)科排名
書目名稱Data Science讀者反饋
書目名稱Data Science讀者反饋學(xué)科排名
作者: 表示向前 時(shí)間: 2025-3-21 22:29
A Lightweight Edge Network Intrusion Detection System Based on MobileVitthms, and the large resource overhead required to deploy deep learning algorithms to edge networks, we proposes a lightweight intrusion detection system based on MobileViT, which can detect cyber-attack traffic in edge networks with little resource consumption. The system uses smote to balance the d作者: 哪有黃油 時(shí)間: 2025-3-22 02:40 作者: libertine 時(shí)間: 2025-3-22 05:13 作者: 沒(méi)有希望 時(shí)間: 2025-3-22 09:18 作者: Prologue 時(shí)間: 2025-3-22 15:39
Enhancing Relevance and Efficiency in Visual Question Generation Through Redundant Object Filtering provides answers. The quality of question generation is vital for the task, but existing methods do not consider the redundant objects brought by Faster RCNN for object detection, leading to meaningless, repetitive questions. To address this, we propose Question Improvement and Redundancy Eliminati作者: Prologue 時(shí)間: 2025-3-22 17:28
Chinese Named Entity Recognition Algorithm Integrating Vocabulary Informationicient learning of Chinese character vocabulary information during the training process. This article proposes an entity recognition model LEBERT-IDGRU-CRF based on BERT and introducing external dictionaries for training. The model performs lexical matching on the data text through an external dicti作者: aggrieve 時(shí)間: 2025-3-22 23:51
WSDSum: Unsupervised Extractive Summarization Based on Word Weight Fusion and Document Dynamic Compaverall content. Numerous prevalent research methods predominantly prioritize the significance of sentences within a document, potentially overlooking the importance of varying keywords within a sentence. Moreover, many methods confine the summarization to information present only in the current docu作者: 針葉樹(shù) 時(shí)間: 2025-3-23 05:09 作者: Expertise 時(shí)間: 2025-3-23 06:18
Multi-modal Variable-Channel Spatial-Temporal Semantic Action Recognition Networktion. However, existing models often lack the spatial-temporal discriminative ability required for fine-grained recognition tasks. To address this issue, we introduce a flexible attention block called Variable-Channel Spatial-Temporal attention (VCSTA) to enhance the discriminative capacity of spati作者: 分散 時(shí)間: 2025-3-23 10:54 作者: osteoclasts 時(shí)間: 2025-3-23 15:01 作者: 極小 時(shí)間: 2025-3-23 21:40 作者: occurrence 時(shí)間: 2025-3-23 22:55
Non-invasive Load Decomposition Model Based on Inception-SimAM-BiLSTMovide help for residents to improve the way of electricity consumption. To solve the problems of single feature extraction scale and low decomposition accuracy of current load decomposition models a sequence-to-sequence model based on Inception-SimAM (simple, parameter-free attention)-BiLSTM (bidire作者: FLIT 時(shí)間: 2025-3-24 06:13 作者: 圓錐體 時(shí)間: 2025-3-24 08:17 作者: committed 時(shí)間: 2025-3-24 12:44
1865-0929 tists, Engineers and Educators, ICPCSEE 2024, held in Macau, China, during September 27–30, 2024...The 74 full papers and 3 short papers presented in these three volumes were carefully reviewed and selected from 249 submissions...The papers are organized in the following topical sections:..Part I: N作者: 刻苦讀書 時(shí)間: 2025-3-24 18:55 作者: Nonporous 時(shí)間: 2025-3-24 19:45 作者: 跟隨 時(shí)間: 2025-3-25 02:54
CCU-NET: CBAM and Cascaded Edge Detection Optimization U-NET for Remote Sensing Image Segmentationary segmentation performance. As a result, the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images. The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.作者: inflate 時(shí)間: 2025-3-25 05:12
Conference proceedings 2024and engine; data security and privacy; big data mining and knowledge management...Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis..作者: compassion 時(shí)間: 2025-3-25 10:10 作者: 懶鬼才會(huì)衰弱 時(shí)間: 2025-3-25 12:03
Die Dreiermenge von Georg Cantor,ing, and then tested in a test set. Experimental results with relatively few model parameters show that our proposed method has good detection performance, while the model requires less storage space and has low computational overhead, making it suitable for network traffic detection and classification under edge networks.作者: gangrene 時(shí)間: 2025-3-25 19:28
https://doi.org/10.1007/978-3-642-52575-9and using the adaptive mechanism to fuse these features, we aim to improve the accuracy of network performance evaluation. Furthermore, our extensive experiments have shown that TrafficNet can improve the Mean Squared Error(MSE) by 58.3% compared with the SOTA models.作者: Kidnap 時(shí)間: 2025-3-25 22:14
https://doi.org/10.1007/978-3-322-93533-5n addition, we put forward the Target Category Learner (TCL) module to simulate human questioning thinking, and apply a penalty mechanism to reduce repetition. Experimental results on the GuessWhat?! dataset show QIRE’s competitiveness in question quality and dialog effectiveness compared to existing methods.作者: Pageant 時(shí)間: 2025-3-26 02:27
https://doi.org/10.1007/978-3-642-90899-6formation in videos, resulting in more precise prediction and analysis. The experimental results show that our multimodal variable-channel spatial-temporal semantic action recognition network achieves 98.3% and 89.9% accuracy in classifying actions on the large-scale human activity datasets NTU-RGB+D 60 and NTU-RGB+D 120 respectively.作者: rods366 時(shí)間: 2025-3-26 08:03 作者: Indict 時(shí)間: 2025-3-26 12:19
A Lightweight Edge Network Intrusion Detection System Based on MobileViting, and then tested in a test set. Experimental results with relatively few model parameters show that our proposed method has good detection performance, while the model requires less storage space and has low computational overhead, making it suitable for network traffic detection and classification under edge networks.作者: 緩解 時(shí)間: 2025-3-26 15:05 作者: libertine 時(shí)間: 2025-3-26 18:59 作者: GRIPE 時(shí)間: 2025-3-27 00:20
Multi-modal Variable-Channel Spatial-Temporal Semantic Action Recognition Networkformation in videos, resulting in more precise prediction and analysis. The experimental results show that our multimodal variable-channel spatial-temporal semantic action recognition network achieves 98.3% and 89.9% accuracy in classifying actions on the large-scale human activity datasets NTU-RGB+D 60 and NTU-RGB+D 120 respectively.作者: 全國(guó)性 時(shí)間: 2025-3-27 03:55 作者: 職業(yè)拳擊手 時(shí)間: 2025-3-27 06:36
Chinese Named Entity Recognition Algorithm Integrating Vocabulary Informationonary to construct word pairs, and then passes the vector matrix to the feature extraction layer, which introduces an attention mechanism for further extraction. Through comparative experiments on four data sets, the model results were improved and the feasibility of the model was verified.作者: ABASH 時(shí)間: 2025-3-27 11:23
,Ab?nderungen des Gesellschaftsvertrages,onary to construct word pairs, and then passes the vector matrix to the feature extraction layer, which introduces an attention mechanism for further extraction. Through comparative experiments on four data sets, the model results were improved and the feasibility of the model was verified.作者: 上坡 時(shí)間: 2025-3-27 14:37
Communications in Computer and Information Sciencehttp://image.papertrans.cn/e/image/284449.jpg作者: 就職 時(shí)間: 2025-3-27 17:57 作者: Induction 時(shí)間: 2025-3-27 22:55 作者: interpose 時(shí)間: 2025-3-28 02:24
https://doi.org/10.1007/978-3-642-52575-9rameter tuning, and capacity planning. The most popular methods have recently been based on Convolutional Neural Network(CNN) or Recurrent Neural Network(RNN). However, many of these methods focus excessively on particular aspects of network features. They often overlook the diversity and complexity作者: chlorosis 時(shí)間: 2025-3-28 07:00 作者: embolus 時(shí)間: 2025-3-28 12:19 作者: 該得 時(shí)間: 2025-3-28 17:32
https://doi.org/10.1007/978-3-322-93533-5 provides answers. The quality of question generation is vital for the task, but existing methods do not consider the redundant objects brought by Faster RCNN for object detection, leading to meaningless, repetitive questions. To address this, we propose Question Improvement and Redundancy Eliminati作者: Cardiac-Output 時(shí)間: 2025-3-28 19:55
,Ab?nderungen des Gesellschaftsvertrages,icient learning of Chinese character vocabulary information during the training process. This article proposes an entity recognition model LEBERT-IDGRU-CRF based on BERT and introducing external dictionaries for training. The model performs lexical matching on the data text through an external dicti作者: Provenance 時(shí)間: 2025-3-29 00:44
https://doi.org/10.1007/978-3-658-28573-9verall content. Numerous prevalent research methods predominantly prioritize the significance of sentences within a document, potentially overlooking the importance of varying keywords within a sentence. Moreover, many methods confine the summarization to information present only in the current docu作者: 六邊形 時(shí)間: 2025-3-29 05:38
Konservierung und Desinfektion der Haut,ormation. To solve this problem, this paper proposes an efficient IPFS keyword retrieval model – IPFS-DKRM (IPFS-Distributed keyword retrieval model). This model combines the global index with Adaptive Radix Tree, and optimizes the storage mode of IPFS network and node-local data index: The model ad作者: 多產(chǎn)魚(yú) 時(shí)間: 2025-3-29 10:06 作者: Console 時(shí)間: 2025-3-29 15:21
https://doi.org/10.1007/3-7643-7670-8 semantic bird‘s-eye view and enhanced and pruned motion planning. This study utilizes a depth estimation network to infer pixel depth and combines camera intrinsic and extrinsic parameters to map image features to bird‘s-eye view features. Subsequently, an enhancement module and a pruning module ar作者: 啟發(fā) 時(shí)間: 2025-3-29 17:08
https://doi.org/10.1007/978-3-662-30560-7mages due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images. We propose an advanced U-Net variant model that addresses these issues. By introducing the CBAM attention mechanism, we enhance the extraction of b作者: 傷心 時(shí)間: 2025-3-29 21:50 作者: 民間傳說(shuō) 時(shí)間: 2025-3-30 01:00
https://doi.org/10.1007/978-3-662-33175-0ovide help for residents to improve the way of electricity consumption. To solve the problems of single feature extraction scale and low decomposition accuracy of current load decomposition models a sequence-to-sequence model based on Inception-SimAM (simple, parameter-free attention)-BiLSTM (bidire作者: 朦朧 時(shí)間: 2025-3-30 04:17 作者: 不透氣 時(shí)間: 2025-3-30 09:37
https://doi.org/10.1007/978-3-531-94182-0and stable operation. Traditional computer vision detection model have problems such as low detection efficiency, many missed detections and poor robustness. To address these problems, we propose a single-stage target detection model PDTNet, which can better extract defect features and can be better作者: 喊叫 時(shí)間: 2025-3-30 16:25 作者: OASIS 時(shí)間: 2025-3-30 17:57 作者: abysmal 時(shí)間: 2025-3-30 22:14
978-981-97-8748-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 銼屑 時(shí)間: 2025-3-31 01:47
Android Malware Detection Method Based on Machine Learning 80%, whereas the dynamic method achieves an accuracy of 91%. Through the utilization of software intention analysis and permission usage checks in combination, the accuracy rate can be further enhanced to 94%. Upon comparison of the different algorithms utilized in each detection method, it is conc作者: SHRIK 時(shí)間: 2025-3-31 05:46 作者: 起波瀾 時(shí)間: 2025-3-31 10:23
Personalized Novel Recommendation System Based on Filtering and Sentiment Analysisommendation system that incorporates sentiment analysis together with content-based recommendation and coordinated filtering. Tests were carried out on publicly available datasets and shown superior accuracy in comparison to cutting-edge techniques.作者: 飛鏢 時(shí)間: 2025-3-31 13:55 作者: GROG 時(shí)間: 2025-3-31 19:17
IPFS-DKRM: An Efficient Keyword Retrieval Model of IPFS Based on ART efficient retrieval services. In the simulation experiment, the index of open source data set Crosswikis was constructed, and the performance was analyzed based on the results of Siva data. The experimental results showed that compared with the Siva model, the response retrieval time of IPFS-DKRM w
