farzad peyravi

In the modern world, social network analysis has various applications including social network management, market trend analysis, identifying influential individuals, supporters, and enhancing the performance of descriptive systems. One of the existing issues in the field of information diffusion is examining the extent of dissemination in such systems. In large-scale networks, computational complexity has increased, and one of the methods to cope with this problem is using evolutionary algorithms. In this paper, a new method is proposed to increase the accuracy of predicting information diffusion in social networks by combining the colonial competition algorithm and the centrality algorithm. In the proposed method, the centrality of nodes is first calculated for all nodes in the network. Then, based on the distance between users in the social network and the speed of information dissemination among users using the colonial competition algorithm, the optimal path for dissemination is determined, and information diffusion prediction is made accordingly. In the colonial competition algorithm, the optimal path for information dissemination in the network is identified based on how the colonies move towards the colonizer. In this paper, four different datasets were examined. The results show that the proposed method leads to better results. The algorithm's performance is evaluated based on accuracy, NMI, and error criteria, showing desirable results.

This research aims to enhance the accuracy of credit assignment for rules in a learning classifier system using Markov Reinforcement Learning for predicting the secondary structure of proteins. Markov Reinforcement Learning has replaced the Bucket Brigade algorithm in the learning classifier system. The Protein Data Bank dataset is utilized to train the system, specifically the protein 4L1W with 5741 samples, where 70% is used for training and 30% for testing purposes. Following the system's training, a set of valuable classifiers (rules) is generated, which will be employed in the testing phase to predict the protein's secondary structure. The experimental results demonstrate an improvement in the accuracy of the Markov Reinforcement Learning classifier system, both in the Learning classifier system and the eXtended classifier system. Through Markov Reinforcement Learning, the credit assignment to each rule is enhanced, resulting in an accuracy improvement of 82.5% for the Learning classifier system and 85% for the eXtended classifier system.

Call centers have become one of the most cost effective ways of selling products to customers and giving services to them in different industries. By applying knowledge management solutions, we can meet call centers’ challenges and gain benefits of reduced training costs, improved call handling and greater flexibility. This paper describes an agent mediated knowledge management system in call centers using the Tropos methodology. We use structure-in-5 for architectural design which specifies that our KM system is an aggregation of five sub-structures. Furthermore we develop a formal methodology and technique to verify the validity of communication protocols defined in a multi-agent environment. This is accomplished by examining agent conversations before deploying the system. The methodology leads to the definition of six different classes of agents. Our experiments develop proof of concept module for a call center that automatically verifies some of the important properties identified in this methodology. Results prove the agent’s specification and indicate that our proposed model works accurately. The paper concludes with observations on the call centers and the role of agents in the proposed model.