Research on Ocean Big Data Service Technology in Distributed Network Environment

Zou, Cui; Yang, Jun

doi:10.2112/si98-035.1

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Wireless Communications and Mobile Computing one for storage and computing, a master private key that contains decryptable user data, realizes computing outsourcing by constructing a secure protocol [17]. This paper is an overview of machine learning, followed by an explanation of the theory of cluster analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Liu

2022

Wireless Communications and Mobile Computing

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Attacks on network systems are becoming more and more common, the current state of increasingly sophisticated attack methods, the emergence of intrusion prevention technology is the inevitable result of the development of computer technology and network technology, and research on intrusion prevention has become a new focus of network security technology research in recent years. In order to ensure the security of computer network confidential information, the authors propose a semisupervised clustering intrusion detection algorithm. An overview of machine learning, followed by an explanation of the theory of cluster analysis, simulation experiments were carried out using the K -means algorithm and the semisupervised clustering algorithm proposed by the author, for 10,000 records, the K -means clustering algorithm and the semisupervised clustering algorithm described in this paper are used, respectively, and intrusion detection data tests were performed. At the same time, different K values were selected, three datasets were selected from “kddcup.newtestdata_10_percent_corrected,” the test data were tested separately, and their average value was taken as the test result. From the simulation results, the detection rate of the semisupervised clustering algorithm is higher than that of the K -means clustering algorithm, and the false alarm rate and K -means algorithms have also been improved. Therefore, the author’s semisupervised algorithm enhances the stability of the system, and the performance of the K -means algorithm is improved to a certain extent. When the value of K gradually increases, the false alarm rate also increases; however, when K is 20, the detection rate is maximized, from this, it can be known that when K is 20, its detection rate reaches 91.76%, and the false alarm rate is 8.54%. The detection rate of the author’s algorithm is significantly higher than the other two algorithms, the false positive rate is slightly higher than K -means, and the false positive rate is lower than that of the other algorithm, proving the superior performance of our algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Liu

2022

Wireless Communications and Mobile Computing

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“…The era of big data puts forward new requirements for human resources and revolutionary changes to the cultivation of human resources, so that they have the ability to acquire and update knowledge on their own, rather than emphasizing the direct teaching of large amounts of knowledge to learners [13]. The information age has put forward brand-new requirements for the training objectives, training contents and training methods of our education, and distance education has arisen precisely in response to the needs of society.…”

Section: Introductionmentioning

confidence: 99%

Research on personalized Japanese language teaching model based on big data technology

2023

Applied Mathematics and Nonlinear Sciences

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In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized Japanese language teaching in a big data technology environment. We build a model of learner characteristics and use collaborative filtering techniques to push learning information from learners with the same or similar interest and preference characteristics. Adjust teaching strategies based on visual user information in the information panel. Build a personalized teaching model based on big data, extract and visualize the data of each student’s learning behavior, find out the gap between the teaching objectives and the preset ones, and record the students’ learning methods. Based on data to dynamically update the teaching content, assign the most appropriate learning tasks, determine the distance of teaching objectives, and improve students’ learning ability and learning effectiveness. Through inductive learning inference on the training dataset, the probabilities of all subsets are calculated, the relevant data subset intervals are merged, and the business sensitivity index is introduced to measure the whole source, which makes the improved decision tree gradually reduce the uncertainty of the division and decrease the weight degree of the attributes, which makes the personalized Japanese teaching better. The analysis results showed that the improved decision tree model reached 71% accuracy in 80 cases and stayed above 73% afterwards, and the ratio of students’ mastered knowledge to the number of knowledges learned was 0.4. Personalized Japanese language teaching under this method led to a significant improvement in students’ performance.

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“…Ini dibutuhkan karena negara berkembang menghadapi banyak tantangan dalam layanan pengiriman (Masiya, Davids, & Mangai, 2019). Teknologi pelayanan adalah sebuah cara yang penting untuk melayani konstruksi ekonomi dan sosial (Cui & Yang, 2019).…”

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Penerapan Inaportnet dalam Proses Pelayanan Penyandaran Kapal: Studi Kasus

Aini

Sianturi

Nofandi

2021

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The significance of ports in global trade activities has a significant impact on a city, region, and even a nation. A port with adequate facilities and a superior system will make port activities more effective, neutral, and even simple for its users. Inaportnet is a facility that is currently used by major Indonesian ports, including the port of Tanjung Emas in Semarang. The users of this system include exporters, importers, shipping lines, and others, in addition to port players. PT Global Logistic Agency is one of the user companies that has been using the Inaportnet framework. This study was descriptive research using a structured interview method. This study aims to examine the use of Inaportnet from the side of the organizer represented by PT. Global Logistics Agency. The results showed that the use of Inaportnet played an important role in the implementation of ship berthing services in terms of time efficiency, energy, and demurrage, the Inaportnet system is still found down or system errors are due to maintenance from the management unit so that PT. Global Logistic Agency must carry out conventional or manual implementation.

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Research on Ocean Big Data Service Technology in Distributed Network Environment

Cited by 4 publications

References 6 publications

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Research on personalized Japanese language teaching model based on big data technology

Penerapan Inaportnet dalam Proses Pelayanan Penyandaran Kapal: Studi Kasus

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