检索结果-内蒙古大学图书馆

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

来源：评论

学校读者我要写书评

暂无评论

Cancer Prediction using Clinical and Genomic data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

GAF-GAN: Gated Attention Feature Fusion Image Inpainting network Based on Generative Adversarial network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

来源：评论

学校读者我要写书评

暂无评论

Evaluating Naive Bayes Classifiers for Traffic Crash Prediction in Rome, Italy: A Comparative Examination

Evaluating Naive Bayes Classifiers for Traffic Crash Predic...

引用

International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： El Ferouali, Soukaina Elamrani Abou Elassad, Zouhair Abdali, Abdelmounaîm CISIEV Team IT Department Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco SARS Research Team Department of Computer Science ENSAS Cadi Ayyad University Safi Morocco Data Science Laboratory Department of Information Technology Management Daffodil International University Birulia Bangladesh

ISBN: (纸本)9783031714252

It has been observed that technological innovations like Geographic Information Systems (GIS), Machine Learning, Artificial Intelligence (AI), Internet of Things (IoT), Big data, and Intelligent transportation systems can offer useful techniques for identifying and providing details on variables impacting road safety. This is because there is no chance of human error when using these technologies to gather and analyze data. The majority of research on road safety focuses on predicting and averting technological, organizational, and human errors that may cause serious issues or collisions. Traffic accidents are recognized as one of the major global causes of injury and death. Trucks, cars, buses, motorbikes, and pedestrians are involved in crashes that result in almost 3700 fatalities and over 1.3 million deaths. This article aims to examine how well Naive Bayes classifiers perform in the domain of traffic crash prediction for the deceased class. The main objective is to assess and contrast the effectiveness of the three Naive Bayes variations in correctly categorizing occurrences linked to fatalities in traffic accident incidents, which are Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes. Our study’s findings show that the three Naive Bayes classifiers perform consistently better when classifying traffic accident outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Clarifying the Concept of Enterprise Architecture & System of Systems: A Systematic Literature Review 7

Clarifying the Concept of Enterprise Architecture & System o...

引用

7th International Conference on Sustainable Information Engineering and Technology, SIET 2022

作者： Diana, Lupna Kurniawan, Tri Astoto Priharsari, Diah Prabowo, Wahyu Setyo Brawijaya University Faculty of Computer Science Indonesia National Research and Innovation Agency Center for Data and Information

ISBN: (纸本)9781450397117

Enterprise-level system development requires a grand design to effectively achieve the system development objectives, which may involve more than one application. Currently, two approaches are available for creating such a grand design, i.e. Enterprise Architecture (EA) and System of Systems (SoS). In practice, EA plays a significant role in such development. However, it is not easy to choose correctly between the two because comparative studies do not yet exist in the literature, which, in turn, confuses. Thus, this research aims to develop a comparative study between EA and SoS using the Systematic Literature Review (SLR) approach. This study used four major digital libraries, i.e. Cambridge, Emerald Publishing, IEEE Xplore, and science Direct, resulting in 48 primary studies published in the last decade. The comparison relied on three main criteria, *** definition, the characteristics, and the framework. As a result, we have successfully clarified that EA and SoS have specific purposes. Finally, the result may become one of the knowledge resources for academia, researchers, and practitioners to adopt in creating a system development grand design at the enterprise level. © 2022 ACM.

关键词： computer architecture

来源：评论

学校读者我要写书评

暂无评论

Can Lexicon-Based Sentiment Analysis Boost Performances of Transformer-Based Models? 7

Can Lexicon-Based Sentiment Analysis Boost Performances of T...

引用

7th International Conference on New Media Studies, CONMEDIA 2023

作者： Manik, Lindung Parningotan Susianto, Harry Dinakaramani, Arawinda Pramanik, Niken Suhardijanto, Totok National Research and Innovation Agency Research Center for Data and Information Sciences Bandung Indonesia University of Nusa Mandiri Department of Computer Science Jakarta Indonesia University of Indonesia Faculty of Psychology Depok Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia University of Indonesia Faculty of Humanities Department of Linguistics Depok Indonesia

ISBN: (纸本)9798350307504

An essential endeavor in natural language processing, sentiment analysis entails determining the sentiment expressed in a text. Transformer-based models like BERT have attained state-of-the-art performance in sentiment analysis tasks. However, these algorithms may have difficulty distinguishing sentiment-laden words. In response, we proposed combining lexicon-based sentiment analysis and transformer-based models. This study investigates the effect of lexicon-based sentiment analysis, particularly SentiStrength, on BERT's efficacy in sentiment analysis tasks. Experimental evaluations reveal that incorporating sentiment lexicons enhances the accuracy and F1-score of classical sentiment analysis compared to the baseline BERT model. Our findings demonstrate the value of incorporating external knowledge sources into transformer-based sentiment analysis models. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

引用

20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Smart Grid Application: Addressing data Scarcity Challenges and Enhancing Load Forecasting Efficiency

Deep Learning for Smart Grid Application: Addressing Data Sc...

引用

International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Amalou, Ibtissam Mouhni, Naoual Abdali, Abdelmounim Jakimi, Abdeslam Tourad, Mohamedou Cheikh CISIEV FSTG University Cadi Ayyad Marrakech Morocco LAMIGEP EMSI Marrakech Marrakech Morocco GL-ISI Team Faculty of Science and Technology Errachidia Moulay Ismail University Meknes Morocco Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania

ISBN: (纸本)9783031714252

The surge in domestic electricity consumption necessitates innovative solutions for the integration of smart grids. Traditional automation methods prove insufficient for handling unforeseen challenges and sustainability issues, especially with the increasing adoption of renewable energy sources. This paper reviews the application of deep learning (DL) methods to smart grids. The research explores various DL approaches employed in smart grid data collection, such as generative adversarial networks, convolutional neural networks (CNNs), auto-encoders, restricted Boltzmann machines, and multilayer perceptron models. These techniques aim to refine features, detect electricity theft, and identify patterns related to false data injection attacks and energy consumption. data scarcity emerges as a critical challenge impacting smart grid data management, affecting forecasting accuracy, operational insights, efficiency, and innovation. The study advocates for investments in advanced data collection technologies and taking advantage of machine learning techniques to optimize the use of available data. To demonstrate the impact of data scarcity on DL model efficiency, a Machine learning benchmark models are trained for energy consumption forecasting. Results reveal the significance of training models with sufficient data reaching a minimization of 3.1149 for validation mean squared error of one of the models (CNN_LSTM) by increasing the use of training data to 75% of the Individual household electric power consumption dataset, emphasizing the need to address data scarcity challenges for effective model training. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：