Research on Establish an Efficient Log Analysis System with Kafka and Elastic Search

Wei, Yuanzhou; Manman, Li; Xu, Bin-Sen

doi:10.4236/jsea.2017.1011047

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…On the other hand, ResNet can solve the problem of degradation and can achieve better performance with a deeper architecture. However, ResNet has a more complex architecture and requires more computational resources for training [13].…”

Section: Comparison Between Cnn and Resnetmentioning

confidence: 99%

Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning

Wei¹,

Gao²,

Xiao³

et al. 2023

JSEA

Self Cite

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Traffic sign recognition is an important task in intelligent transportation systems, which can improve road safety and reduce accidents. Algorithms based on deep learning have achieved remarkable results in traffic sign recognition in recent years. In this paper, we build traffic sign recognition algorithms based on ResNet and CNN models, respectively. We evaluate the proposed algorithm on public datasets and compare. We first use the dataset of traffic sign images from Kaggle. And then designed ResNet-based and CNN-based architectures that can effectively capture the complex features of traffic signs. Our experiments show that our ResNet-based model achieves a recognition accuracy of 99% on the test set, and our CNN-based model achieves a recognition accuracy of 98% on the test set. Our proposed approach has the potential to improve traffic safety and can be used in various intelligent transportation systems.

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Section: Comparison Between Cnn and Resnetmentioning

confidence: 99%

Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning

Wei¹,

Gao²,

Xiao³

et al. 2023

JSEA

Self Cite

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“…The Vertical-oriented Monitoring System (5Gr-VoMS) is an extension of 5G-TRANSFORMER monitoring platform (5GT-MP), designed with the objective of supporting heterogeneous set of services and technological domains; and, likewise, novel innovations devoted to enhance end-to-end reliability (via selfhealing and auto-scaling), vertical control-loops stability, and analytical features, such as forecasting, inference and anomaly detection. In order to provide such innovation support, 5GT-MP must be extended to include additional functionalities such as log aggregation, a scalable data distribution system and dynamic probe reconfiguration [7]. Elastic stack is included in the VoMS architecture to support log aggregation, Kafka distributed streaming platform as scalable data distribution system and Elastic Beats which will, together with Prometheus node exporter, assist to the dynamic reconfiguration of the monitoring probes.…”

Section: Vertical-oriented Monitoring System (5gr-voms)mentioning

confidence: 99%

5Growth: AI-driven 5G for Automation in Vertical Industries

Papagianni

Mangues‐Bafalluy

Bermudez³

et al. 2020

2020 European Conference on Networks and Communications (EuCNC)

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Spurred by a growing demand for higher-quality mobile services in vertical industries, 5G is integrating a rich set of technologies, traditionally alien to the telco ecosystem, such as machine learning or cloud computing. Despite the initial steps taken in prior research projects in Europe and beyond, additional innovations are needed to support vertical use cases. This is the objective of the 5Growth project: automate vertical support through (i) a portal connecting verticals to 5G platforms (a.k.a. vertical slicer), (ii) closed-loop machine-learning based Service Level Agreement (SLA) control, and (iii) end-to-end optimization. In this paper, we introduce a set of key 5Growth innovations supporting radio slicing, enhanced monitoring and analytics and integration of machine learning.

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“…This paper proposes a neural network-based cancer classification model, studying 802 people who were detected with different types of cancer, each sample contains more than 20K gene *These authors contributed equally to this work. Journal of Software Engineering and Applications expression values, and the ultimate goal is to build a robust classification model to identify each type of cancer, and we processed the collected cancer data by Kafka [2]. This will allow us to identify each type of cancer early, thereby reducing mortality.…”

Section: Introductionmentioning

confidence: 99%

Research and Implementation of Cancer Gene Data Classification Based on Deep Learning

Wei¹,

Gao²,

Xiao³

et al. 2023

JSEA

Self Cite

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Cancer has become a cause of concern in recent years. Cancer genomics is currently a key research direction in the fields of genetic biology and biomedicine. This paper analyzes 5 different types of cancer genes, such as breast, kidney, colon, lung and prostate through machine learning methods, with the goal of building a robust classification model to identify each type of cancer, which will allow us to identify each type of cancer early, thereby reducing mortality.

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Research on Establish an Efficient Log Analysis System with Kafka and Elastic Search

Cited by 7 publications

References 2 publications

Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning

Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning

5Growth: AI-driven 5G for Automation in Vertical Industries

Research and Implementation of Cancer Gene Data Classification Based on Deep Learning

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