scholarly journals Computer Network Security Defense Model

2022 ◽  
Vol 2146 (1) ◽  
pp. 012041
Author(s):  
Yiming Niu ◽  
Wenyong Du ◽  
Zhenying Tang
Keyword(s):  
Recommendation System ◽  
Computer Network ◽  
Rapid Development ◽  
Algorithm Design ◽  
Recommendation Algorithm ◽  
Analysis And Design ◽  
Internet Industry ◽  
Similarity Algorithm ◽  
Distribution Process ◽  
Hybrid Recommendation

Abstract With the rapid development of the Internet industry, hundreds of millions of online resources are also booming. In the information space with huge and complex resources, it is necessary to quickly help users find the resources they are interested in and save users time. At this stage, the content industry’s application of the recommendation model in the content distribution process has become the mainstream. The content recommendation model provides users with a highly efficient and highly satisfying reading experience, and solves the problem of information redundancy to a certain extent. Knowledge tag personalized dynamic recommendation technology is currently widely used in the field of e-commerce. The purpose of this article is to study the optimization of the knowledge tag personalized dynamic recommendation system based on artificial intelligence algorithms. This article first proposes a hybrid recommendation algorithm based on the comparison between content-based filtering and collaborative filtering algorithms. It mainly introduces user browsing behavior analysis and design, KNN-based item similarity algorithm design, and hybrid recommendation algorithm implementation. Finally, through algorithm simulation experiments, the effectiveness of the algorithm in this paper is verified, and the accuracy of the recommendation has been improved.

The Instructional SEM Laboratory at Iowa State University

1996 ◽  
Vol 54 ◽  
pp. 396-397
Author(s):  
L. S. Chumbley ◽  
M. Meyer ◽  
K. Fredrickson ◽  
F.C. Laabs
Keyword(s):  
Materials Science ◽  
Computer Network ◽  
Energy Dispersive Spectrometer ◽  
Computer Control ◽  
State University ◽  
Iowa State University ◽  
Internet Server ◽  
Schematic Layout ◽  
Hands On ◽  
Improve Instruction

The Materials Science Department at Iowa State University has developed a laboratory designed to improve instruction in the use of the scanning electron microscope (SEM). The laboratory makes use of a computer network and a series of remote workstations in a classroom setting to provide students with increased hands-on access to the SEM. The laboratory has also been equipped such that distance learning via the internet can be achieved.A view of the laboratory is shown in Figure 1. The laboratory consists of a JEOL 6100 SEM, a Macintosh Quadra computer that acts as a server for the network and controls the energy dispersive spectrometer (EDS), four Macintosh computers that act as remote workstations, and a fifth Macintosh that acts as an internet server. A schematic layout of the classroom is shown in Figure 2. The workstations are connected directly to the SEM to allow joystick and computer control of the microscope. An ethernet connection between the Quadra and the workstations allows students seated there to operate the EDS. Control of the microscope and joystick is passed between the workstations by a switch-box assembly that resides at the microscope console. When the switch-box assembly is activated a direct serial line is established between the specified workstation and the microscope via the SEM’s RS-232.

Expert System for Monitoring of Computer Network work Rules Checking Automation

2014 ◽  
pp. 94-104
Author(s):  
В.М. Рувинская ◽  
◽  
А.С. Тройнина ◽  
Е.Л. Беркович ◽  
А.Ю. Черненко
Keyword(s):  
Expert System ◽  
Computer Network ◽  
Work Rules

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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