An Autonomous Agent Based Incident Detection System for Cloud Environments

Abstract Automated Incident Detection (AID) is an important part of Advanced Trafﬁc Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the inﬂuence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artiﬁcial Neural Networks (ANNs) in freeway incident detection.

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Agent-Based Network Intrusion Detection System

2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07) ◽

10.1109/iat.2007.111 ◽

2007 ◽

Cited By ~ 4

Author(s):

Vojtech Krmicek ◽

Pavel Celeda ◽

Martin Rehak ◽

Michal Pechoucek

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Agent Based ◽

Network Intrusion ◽

Network Intrusion Detection System

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Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

Microorganisms ◽

10.3390/microorganisms9020417 ◽

2021 ◽

Vol 9 (2) ◽

pp. 417

Author(s):

Sherli Koshy-Chenthittayil ◽

Linda Archambault ◽

Dhananjai Senthilkumar ◽

Reinhard Laubenbacher ◽

Pedro Mendes ◽

...

Keyword(s):

Species Interactions ◽

Computational Models ◽

Human Microbiome ◽

Agent Based Modeling ◽

Autonomous Agent ◽

Agent Based Models ◽

Agent Based ◽

Complex Interactions ◽

Mucosal Surfaces ◽

Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

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