A Survey of Multi-agent Systems and Case-Based Reasoning Integration

Jubair, Mohammed Ahmed; Mostafa, Salama A.; Hafit, Hanayanti

doi:10.1109/isamsr.2018.8540549

Cited by 8 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The confusion matrix is used to campout the classification accuracy of the MLP1 model, as illustrated in Figure 5. The MLP1 model achieves the best accuracy of 95.62% using (1) [25][26][27][28][29][30][31] after 100 epochs.…”

Section: Simulation Evaluation and Resultsmentioning

confidence: 99%

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Mohammed¹,

Arif²,

Ali³

2020

IJ-AI

View full text Add to dashboard Cite

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

show abstract

Section: Simulation Evaluation and Resultsmentioning

confidence: 99%

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Mohammed¹,

Arif²,

Ali³

2020

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Multi-Agent System (MAS) based CBR designs are well discussed in detail in the survey paper [36]. It is one of the most common techniques that has achieved promising results.…”

Section: Related Cbr Literaturementioning

confidence: 99%

F-CBR: An Architecture for Federated Case-Based Reasoning

2022

View full text Add to dashboard Cite

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility. INDEX TERMSCase-based reasoning, data minimization, data privacy, data silos, decision support systems, federated architecture, federated case-based reasoning. TABLE 1. Literature comparison table.

show abstract

“…This process is especially dreary as t ime is a fundamental variab le as a result of the quick transient nature of philanthropy and certain scenarios need philanthropy in a short time in the midst of such essential conditions. Further, the records are stored in large inventories [25,[27][28][29].…”

Section: Figure 1 the Framework Of The Cfismentioning

confidence: 99%

Social networking mobile apps framework for organizing and facilitating charitable and voluntary activities in Malaysia

et al. 2020

Self Cite

View full text Add to dashboard Cite

Numerous non-profit driven establishments depend on volunteers to help achieve their administrative targets. Despite the fact that volunteers work side-by-side or now and again substitute representatives in delivering services, inputting volunteer work into non-profit ventures of delivering services presents remarkable difficulties. Understanding these difficulties provides a significant fundamental building step in comprehending the influence these challenges have on service developmental plans and operations when utilizing volunteers. In this study, the paper brings forward a Charity Fundraising Information System (CFIS) framework and presents the modelling and evaluation of a plan and operational variables applicable to volunteer fulfilment in non-profit driven organizations. Discoveries indicate that fulfilled volunteers are bound to stay longer with the same establishment, give monetarily to the non-profit driven organization, and prescribe the volunteer involvement to other people. Every one of these results guarantees the continuous sustenance of the non-profit driven establishment.

show abstract

A Survey of Multi-agent Systems and Case-Based Reasoning Integration

Cited by 8 publications

References 29 publications

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

F-CBR: An Architecture for Federated Case-Based Reasoning

Social networking mobile apps framework for organizing and facilitating charitable and voluntary activities in Malaysia

Contact Info

Product

Resources

About