جستجوی مقالات مرتبط با کلیدواژه « هوش جمعی » در نشریات گروه « علوم انسانی »

The purpose of this research is to present the evolutionary model of short selling using collective intelligence and an ant colony algorithm. In terms of method, this research is in the category of quantitative research, and the purpose of the study is practical. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is library-type and uses foreign and domestic articles and financial data of companies admitted to the stock exchange, which has been compiled by referring to financial statements and explanatory notes using the new Rahvard Novin software. In the following, we have presented the short-selling model by using EViews 9 software and MATLAB, and then using MATLAB software and the ant colony algorithm, we have presented the evolutionary model of short selling. In the end, by comparing the step-by-step regression model and the borrowed sales model (ant colony algorithm), he presented a model that is more efficient than other models. The result of the research indicates that the short-selling model with the help of the ant colony algorithm has a higher efficiency.

Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.

The insurance industry of Iran has a low level of maturity in utilizing the capabilities of Internet for improving its business processes، especially when it comes to effective interaction with customers. On the other hand، Web 2. 0 as one of most modern approaches in electronic business، incorporating tools such as weblogs، wikis، online social networks، mashups and R. S. S، creates a great opportunity for insurance companies to deepen their communications with their customers، gain extensive feedback and harness collective intelligence. Accordingly، the goal of this research is to design a conceptual model for the effective adoption of Web 2. 0 in insurance industry by identifying the relevant components in this context. In order to do this، after designing the model، by distributing a questionnaire، we ask the opinions of academic experts about the validity and importance of the components of the model. Consequently، the gathered data are analyzed by the Binomial test and then، using TOPSIS technique، the components are ranked in terms of importance. According to the results، reaching for a huge volume of information is the most important driver for Web 2. 0 adoption and gaining immediate feedback for the stakeholders is the most important capability of Web 2. 0 in the insurance industry.

This paper proposes a model for the Cybernetic Management of Communicative Crises. Our approach is based on the premise that although communication processes can lead to crisis themselves, they can be counted on as a source and channel for managing crisis in themselves. Thus crisis aroused from communication processes can be handled by other communication processes. But the question is: How a system of Communication-Solved Crisis (CSC) can be established to manage a Communication-Originated Crisis (COC)? We suggest designing a self-organizing communication system that diagnoses the critical symptoms intelligently, then retrieves and applies the most appropriate solutions to every case of the current critical circumstances to the point that the crisis is eliminated. Such a cybernetic system of communicative crisis management is implemented in two phases; The first level: Artificial Intelligence which provides a reliable Expert System of accumulated experiences of previous crisis management; therefore the techniques of managing of every crisis in the future will be extracted from this expert system. Development of this level of cybernetic management of crisis is dependent on the advances in the fields of Neural Networks, Evolutionary Computing, and Fuzzy Logic. Second level: Collective Intelligence which signifies the priority of collective common sense which emerged in the final decision-making sessions for crisis management. We maintain the idea that this level of crisis management needs an absolute reliance on the Collective Intelligence that provides managers merely with the useful information, related to the context of the current crisis, information should be exploited in managing this crisis, and decision should be made.