Density approach: a new model for BigData analysis and visualization

Zhang, Jinson; Huang, Mao Lin

doi:10.1002/cpe.3337

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…Deep learning can learn about patient health data and medical records. These patient characterizations can be easily assisted in clinical testing, while deep learning can be embedded as a systematic framework in clinical decision making systems [10]. Relevant scholars have proposed a convolutional neural network to construct a ''medical record'' that can be used to help improve the theory of clinical diagnosis [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

Yang

Chen

2019

IEEE Access

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With the development of information technology, the informationization of the medical industry is also constantly developing rapidly, and medical data is growing exponentially. In the context of ''Big Data +'', people began to study the application of data visualization to medical data. Data visualization can make full use of the human sensory vision system to guide users through data analysis and present information hidden behind the data in an intuitive and easy-to-use manner. This paper first introduces the workflow of DBN, a deep learning algorithm, and summarizes the computational characteristics of the algorithm. The classification function is translated into an assembler using an instruction set-based assembly language, and the program is evaluated for performance. Secondly, based on the Hadoop ecosystem, this paper analyzes the BDMISS system for big data medical information resource sharing. Based on the system's requirements and functional positioning, from the medical information collection and sharing, data mining and knowledge management level, the big data medical service system is constructed. Based on the semantic network and ontology theory, big data mining technology and the design of ''medical cloud'', the resource sharing mechanism is analyzed. Based on the Spring MVC framework, using Echarts, HCharts and other data visualization technology, according to the design of specific modules, the visualization and display of medical data is realized, which has certain promotion effect on the research and development of medical big data visualization analysis.

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Section: Introductionmentioning

confidence: 99%

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

Yang

Chen

2019

IEEE Access

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“…In health area, Liu et al (2015) From what was previously exposed, we can identify that visual analysis tools facilitate decision making in the context of the real world (Shneiderman & Plaisant, 2015). From the perspective of big data, the decision making process is based on information gathered in the data repository, and through visualization techniques, decision makers can analyze the information through search and analysis tools (Zhang & Huang, 2014). Importantly, many visualization technologies have been developed to improve visual analysis, but there is no standard for universal viewing that works for all decision making tasks.…”

Section: The Importance Of Visual Analysis On Decisionsmentioning

confidence: 99%

The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

Poleto

Carvalho

Costa

2017

International Journal of Decision Support System Technology

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Big Data is a radical shift or an incremental change for the existing digital infrastructures, that include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work aims to provide a theoretical approach about the elements necessary to apply the big data concept in the decision making process. It identifying key components of the big data to define an integrated model of decision making using data mining, business intelligence, decision support systems, and organizational learning all working together to provide decision support with a reliable visualization of the decision-related opportunities. The concepts of data integration and semantic also was explored in order to demonstrate that, once mined, data must be integrated, ensuring conceptual connections and bequeathing meaning to use them appropriately for problem solving in decision.

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“…[8][9][10] Therefore, it is necessary to establish a detection model that can accurately identify most of attacks, and handle large-scale data fast enough. Although the recent literatures [11][12][13] have solved these problems, they are not suitable for the detection of persistent attacks. Instead of detecting data flows individually, we subcontract data flows according to a certain rule (eg, all samples in each bag have the same source port), which can improve recall and precision of detection for persistent attacks.…”

Section: Introductionmentioning

confidence: 99%

An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing

Wei

Long

et al. 2020

Concurrency and Computation

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The increase of security incidents brings a challenge to the cloud computing security. Intrusion detection technologies have been applied to protect information in cloud from being compromised, and complicated learning-based detection methods have been used to improve the performance of intrusion detection systems. Higher quality and well-formed samples are crucial to the performance of detection algorithm. Therefore, we mainly study the intrusion detection model based on data optimization processing. In this article, we establish an intrusion detection algorithm based on ensemble support vector machine with bag representation. Specifically, the sample flows are divided into bags, where the sample flows in each bag are related to each other. Each bag contains multiple related data flows that can accurately reflect intrusion behavior, especially persistent intrusion. What's more, ensemble algorithm is applied to detection model, which greatly optimizes the performance of detection algorithm. The experimental results on open access datasets show that the proposed model detects the persistent attack with 90.58% recall.

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Density approach: a new model for BigData analysis and visualization

Cited by 14 publications

References 28 publications

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing

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