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![Fig. 1. Traditional architecture of IISs (modified from [3])](profile/Egons-Lavendelis/publication/258049882/figure/fig1/AS:392612562391041@1470617537128/Traditional-architecture-of-IISs-modified-from-3_Q320.jpg)
Modern insurance information systems need intelligence to provide new functions that till now as a rule have been carried out by humans. Introduc-tion of intelligent mechanisms into information systems allows the insurance companies to automate processes in the insurance business and achieve two benefits. Firstly, the amount of work done by humans...
Citations
... In the case of insurance, it means that each insurance company is represented by its agent as well as the client is represented by his/her agent. The use of intelligent agents is motivated by their capabilities to automatically represent their owners and do proactive actions to obtain the user's goals as well as by the possibility to implement the whole insurance system as a set of agents [4]. In this case, the agent that represents the company in the electronic marketplace would also be a participant of the multi-agent system that implements the insurance information system of the corresponding company. ...
The paper presents a simulation tool for automated interactions between insurance companies and their clients during the travel insurance buying process. Insurance deal evaluation model using price and insured risks has been developed based on the study of the Latvian insurance market. The proposed model is used together with well-known agent auction protocols, thus providing a multi-agent negotiation protocol. It allows automating one-to-many negotiations between client and insurance companies simulating electronic insurance policy marketplace. The simulation tool has been developed using the MASITS methodology and tool, thus providing a case study for the methodology and tool for a new type of systems
This paper presents a new method in the field of healthcare security that specifically targets cloud‐based wireless sensor networks (WSNs). The suggested method integrates a goal‐based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder‐based agent (AE‐A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal‐driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder‐enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal‐based AI agent is improved by ongoing real‐time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real‐time analysis, and improved responsiveness. Utilizing goal‐driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare‐oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short‐term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM‐RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates that the suggested model achieved the most significant values of 93% MCC, 94% prediction rate, 91% accuracy, 98% recall, 98% precision, and 98% F1 score, respectively when compared to the existing models.
