Secure Machine Learning Scenario from Big Data in Cloud Computing via Internet of Things Network

Stergiou, Christos; Plageras, Andreas P.; Psannis, Kostas E.; Gupta, Brij B.

doi:10.1007/978-3-030-22277-2_21

Cited by 76 publications

(30 citation statements)

References 68 publications

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“…In the new economic era, college students need to pay attention to the environmental advantages of entrepreneurial capital in the process of entrepreneurship. at is to say, college students need to pay attention to environmental factors in the influencing factors of entrepreneurial performance and realize the implementation of entrepreneurial countermeasures through the assessment of environmental advantages [29][30][31][32][33]. Under the circumstances, college students need to pay attention to the use of new environments and new conditions in the entrepreneurial environment.…”

Section: Results Analysismentioning

confidence: 99%

A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

Zhou

Wang

2021

Complexity

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College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine (SVM) and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis.

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Section: Results Analysismentioning

confidence: 99%

A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

Zhou

Wang

2021

Complexity

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“…It aims to help website vendors and online video websites achieve professional and accurate placement and page-based website targeting. System software is for tasks such as delivery process management and website operation data statistics [2][3][4][5]. As a leading video sharing website, online websites have played more than tens of millions of times a day.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction of an Intelligent Analysis Model for Website Information Based on Big Data and Cloud Computing Technology

Chen

Yang

2022

Discrete Dynamics in Nature and Society

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Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. The main functions in the following aspects are realized: first, we built the website back-end delivery subsystem and data collection and analysis subsystem to realize the control of website delivery and data collection; second, we designed and developed a process management subsystem for booking, management, and delivery of website resources and developed the contract management and order management subsystems to realize the accurate placement of user portraits on the website; then, the placement data monitoring and effect feedback subsystems, and the data inventory subsystem of the website system were designed and developed. Finally, based on the research of Android and based on the Eclipse platform, this article has completed the construction of the Android 4.0.3 version environment, successfully used the Java development language to develop a website information intelligent analysis and navigation system, and analyzed the various functional modules of the entire system. Experimental results show that the system not only realizes ordinary route and site query functions but also combines map API, integrates big data and cloud computing technology, and realizes congestion avoidance query, time optimal query, population heat map, and real-time viewing. The nearby use of this software’s personnel density distribution and other functions provides great convenience for personal travel, which can facilitate the real-time planning of travel plans, and has great practical and practical significance. Through the different levels of testing of various subsystems, the website delivery system meets the functional and nonfunctional requirements proposed by the network and on this basis realizes the use of group wisdom based on Pearson correlation coefficient, Cosine similarity, and Tanimoto coefficient for collaborative filtering website recommendation algorithm.

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“…This implies that the security of RFID systems may not be fully guaranteed. In addition, RFID tags are limited in battery, hence may not be powered over a long time without incurring the cost of battery replacement [ 110 ].…”

Section: Technology-based Contact Tracing Methods Against Covid-19mentioning

confidence: 99%

Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

Alo¹,

Nkwo²,

Nweke

et al. 2021

Sensors

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The COVID-19 Pandemic has punched a devastating blow on the majority of the world’s population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.

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Secure Machine Learning Scenario from Big Data in Cloud Computing via Internet of Things Network

Cited by 76 publications

References 68 publications

A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

[Retracted] Construction of an Intelligent Analysis Model for Website Information Based on Big Data and Cloud Computing Technology

Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

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