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共 155 条记录，以下是101-110 订阅

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Genetic Algorithm Based Service Broker Policy to find Optimal datacenters in cloud Services

Genetic Algorithm Based Service Broker Policy to find Optima...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Shusmoy Chowdhury Ajay Katangur Alaa Sheta Nagesh Rao Psayadala Siming Liu Department of Computer Science Missouri State University Springfield USA Computer Science Department Southern Connecticut State University New Haven Connecticut

In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. this paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. this research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service’s response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the cloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms.

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Breast Cancer Identification Study Using Improved VGG

Breast Cancer Identification Study Using Improved VGG

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Yanzhang Li Kaiqi Deng Department of Electronic Science and Technology Hainan University Hainan China

A classification recognition algorithm based on improved VGG16 for breast cancer histopathology images is proposed to deal with the problem of binary recognition of breast cancer pathology images. Because some pathological images are very similar to each other, which leads to the problem of false detection. therefore, attention mechanism is applied to increase the weight of effective feature map to make the training model get better effect and improve the accuracy of the algorithm. Verified by comparative experiment, the convergence speed and accuracy of the improved VGG16 model are higher than those of the original DenseNet and VGG16 models, reaching 98.41% of the recognition accuracy.

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Path Planning of Mobile Robots Based on Genetic Algorithm

Path Planning of Mobile Robots Based on Genetic Algorithm

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Yansheng Zhang BingHao Ou YuanHong Xu ChaoShu Dai School of Electronic Information Engineering Zhuhai College of Science and Technology ZhuHai China

On the path planning of planar mobile robot, the grid map model is transformed into a two-dimensional matrix, and a mobile operator with probability attribute is designed. A genetic coding method based on Number of matrix rows and columns is proposed, and the reciprocal of path distance is used as the normalized fitness. the path planning based on genetic algorithm is realized by using single point duplication on different paths to cross and multi point duplication to mutate. the optimal path can be obtained by designing the appropriate number of groups. the experimental results show that the method is feasible and effective.

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Government Support, Corporate Digital Transformation and Stock Liquidity

Government Support, Corporate Digital Transformation and Sto...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Yong Li Institute of Finance and Economics Guangdong University of Science and Technology Dongguan China

this research uses listed companies in the Chinese stock exchange, calculated corporate digital transformation and local government support through textual analysis, and conducted an empirical study on the relationship between government support, corporate digital transformation and stock liquidity. We obtain the following research results. First, corporate digital transformation improves the level of stock liquidity. Second, corporate digital transformation can improve the stock liquidity through corporate sales and operations, corporate governance and media attention. third, government support is important external conditions for corporate digital transformation to improve stock liquidity.

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State-of-Health Estimation of Lithium-ion Batteries Based on WOA-CNN-LSTM-Attention

State-of-Health Estimation of Lithium-ion Batteries Based on...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Zhiwei Li Yong Li Chenglin Liao Chengzhong Zhang Liye Wang Lifang Wang Institute of Electrical Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shunde Innovation School University of Science and Technology Beijing China School of Mechanical Engineering University of Science and Technology Beijing Beijing China

In this paper, five health characteristics are extracted from battery charging and discharging data, and combined with the whale optimization algorithm, the performance of the two models in battery health state estimation is compared, namely, the long-short term memory neural network and the long-short term memory neural network with attention mechanism. On this basis, the convolution neural network module is added to extract hidden features from the data, so that the model can obtain more information, thus improving the performance of the model. Finally, the relative error of the improved model for battery health state estimation is controlled within 1.6%.

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Spectral Clustering Algorithm Based on Density Representative Points

Spectral Clustering Algorithm Based on Density Representativ...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Weiyu Kong Guoyin Wang Jiang Xie Hao Bai Chongqing Key Laboratory of Computer Science and Technology Chongqing University of Posts and Telecommunications ChongQing China

In order to solve the problems of long operation time and large memory consumption of the traditional spectral clustering algorithm applied to large-scale data sets, an improved spectral clustering algorithm based on density representative points is proposed. the algorithm changes the similarity matrix of spectral clustering. the construction method transforms the original spectral clustering from all sample points to construct a similarity matrix to use density representative points to construct a similarity matrix. In this way, the scale of the similarity matrix that needs to be calculated is greatly reduced, and the calculation of the spectral clustering algorithm is improved. In addition, the clustering effect of the spectral clustering algorithm is also improved. Simulation results show that the algorithm in this paper can effectively improve the processing ability of spectral clustering algorithm for data sets.

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Research on Re-recognition Method of Multi-branch Fusion Attention Mechanism for Occluded Pedestrian

Research on Re-recognition Method of Multi-branch Fusion Att...

引用

ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Haiyan Zhao Yan Xu Shandong University of Science and Technology Qingdao China

Due to the problem of feature misalignment between pedestrian images, illumination, posture, occlusion and other factors have a great impact on re-recognition, an attention-based multi-branch fusion pedestrian re-recognition framework is proposed. Firstly, Resnet50 is used as the backbone network to extract the initial features, and then the extracted initial features are entered into the network in parallel, and the final features are obtained through feature fusion. the network was trained by cross entropy loss, and experiments were performed on Occluded-ReID data sets, P-EthZ data sets and Partial-ReID data sets with serious occlusion. the accuracy was significantly improved, which proved that this method could improve the recognition rate in practical application scenarios.

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Research on Recommendation Methods Based on data Augmentation in a Heterogeneous View

Research on Recommendation Methods Based on Data Augmentatio...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Qian Mao Xiaomei Yu Xueyu Che Zhaokun Gong Wenxiang Fu Zehong Xu School of Information Science and Engineering Shandong Normal University Jinan China

In recent years, many domains contain different object and relationship types, forming Heterogeneous Information Networks (HINs). these HINs include richer semantic and structural information than homogeneous information networks. In this paper, a comprehensive survey of data mining and applications using HINs is presented. Specifically, we first review the current achievements in HIN research and outline the research trends of HINs in data mining. Next, we explore the applications of HINs in various fields, such as finance and education. Finally, through a series of experiments, we explore research hotspots and the development of HINs in educational applications and make recommendations for future areas of study in HIN applications. the experimental results demonstrate the effectiveness of HIN based techniques in real-world data mining tasks, particularly in educational applications such as course recommendation.

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A Flash-Based Cache Optimization Strategy

A Flash-Based Cache Optimization Strategy

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Xiaoguang Ma Jinfeng Dou Jiayu Song Sida Wang College of Information Science and Engineering Ocean University of China Qingdao China

Flash-based key-value caching is becoming increasingly popular in data centers. By caching most of data on SSDs, caching systems can eliminate a large number of time-consuming requests to back-end data storage and provide low-latency services. According to research, data redundancy is prevalent at all levels of storage systems with duplicate data taking up cache space and reducing the logical space for caching. Moreover, optimizing the performance of flash-based key-value caches is challenging due to the unique technical limitations of flash devices. In this paper, we propose a cache optimization strategy called PRCache, which designs a two-stage garbage collection scheme combining data deduplication techniques and replacement algorithms. We implement and evaluate PRCache, and experimental results show that PRCache improves the hit rate by 14.8% and reduces the average access latency by 13.7%.

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Deep-Reinforcement-Learning-Based Service Placement for Video Analysis in Edge computing

Deep-Reinforcement-Learning-Based Service Placement for Vide...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Qijun Zhu Sichen Wang Hualong Huang Yuchuan Lei Wenhan Zhan Hancong Duan University of Electronic Science and Technology of China Sichuan Branch of China Telecom Group Co. Ltd.

Bringing computing power closer to data sources, edge computing is considered a solution for many low-latency data analysis requirements, such as video analysis. this paper studies the placement of video analysis tasks in an edge computing scenario, where each video analysis task is divided into several microservices, forming a microservice chain. Due to the complex topology of edge networks, the heterogeneity of edge servers, and the different resource requirements of microservices, it is difficult to achieve an optimal microservice placement strategy in a dynamic environment. To minimize the long-term average response time of video analysis tasks, we model the placement process with the Markov decision process and adopt deep reinforcement learning to explore the optimal strategy. Extensive simulation experiments and comprehensive comparisons clearly demonstrate the advantages of the proposed deep reinforcement learning-based microservice placement method.

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