Multi-agent-based distributed text information filtering method

2021 ◽  
Vol 13 (3) ◽  
pp. 123
Author(s):  
Wuxue Jiang
Keyword(s):  
Information Filtering ◽  
Agent Based ◽  
Filtering Method ◽  
Multi Agent ◽  
Text Information

Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks

2012 ◽  
Vol 532-533 ◽  
pp. 1036-1040
Author(s):  
Jian Qing Liu ◽  
Mei Luo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Information Filtering ◽  
Monitor System ◽  
Filtering Method ◽  
Media Attention ◽  
Text Information ◽  
Novel Method ◽  
Artificial Neural

Internet monitoring has recently been the focus of media attention and public debate. This paper proposed a novel method of multi-layer smart monitor system to filter unhealthy information using artificial neural networks (ANN). This method classified the text into multilayer and uses RPROP algorithm to implement the text classifier. Finally, the test was deployed and the feasibility of this algorithm was proven.

A Case Study on Chinese Text Information Filtering Method Based on User Ontology Model

2008 ◽  
Author(s):  
Bofeng Zhang ◽  
Jianguo Pan ◽  
Jianbo Hu ◽  
Zhongyuan Liu ◽  
Ruimin Zhang
Keyword(s):  
Chinese Text ◽  
Information Filtering ◽  
Filtering Method ◽  
Ontology Model ◽  
Text Information

MULTI-AGENT LEARNING IN RECOMMENDER SYSTEMS FOR INFORMATION FILTERING ON THE INTERNET

2001 ◽  
Vol 10 (01n02) ◽  
pp. 81-100 ◽  
Author(s):  
JOAQUIN DELGADO ◽  
NAOHIRO ISHII
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Target Function ◽  
Majority Voting ◽  
Information Goods ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Learning ◽  
Timely Fashion ◽  
Multi Agent

Recommender Systems (RS), allow users to share information about items they like or dislike and obtain, in a timely fashion, recommendations based on predictions about unseen items (physical or information goods and/or services). In this process, users' preferences are considered to be the learning target functions. We study Agent-based Recommender Systems (ARS) under the scope of online learning in Multi-Agent systems (MAS). This approach models the problem as a pool of independent cooperative predictor agents, one per each user (the masters) in the system, in situations in which each agent (the learners) faces a sequence of trials, with a prediction to make in every step, eventually getting the correct value from its master. Each learner is willing to discover the degree of similarity among the target function of its master and those of other agents' masters (i.e. preference similarity). The agent uses this information for the calculation of its own prediction task, the goal being to make as few mistakes as possible. A simple, yet effective method is introduced in order to construct a compound algorithm for each agent by combining memory-based individual prediction and online weighted-majority voting. We give a theoretical mistake bound for this algorithm that is closely related to the total loss of the best predictor agent in the pool. Finally, we conduct some experiments obtaining results that empirically support these ideas and theories.

A Multi-agent based Micro Grid Operation Method

2013 ◽  
Vol 133 (9) ◽  
pp. 1652-1657 ◽  
Author(s):  
Takeshi Nagata ◽  
Kosuke Kato ◽  
Masahiro Utatani ◽  
Yuji Ueda ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Agent Based ◽  
Operation Method ◽  
Multi Agent ◽  
Micro Grid ◽  
Grid Operation

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

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