{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:55:35Z","timestamp":1755694535397},"reference-count":29,"publisher":"Tsinghua University Press","issue":"3","license":[{"start":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T00:00:00Z","timestamp":1567382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCS"],"published-print":{"date-parts":[[2019,9,2]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijcs-05-2019-0014","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T10:48:21Z","timestamp":1571050101000},"page":"249-266","source":"Crossref","is-referenced-by-count":2,"title":["Intelligence level analysis for crowd networks based on business entropy"],"prefix":"10.26599","volume":"3","author":[{"given":"Zhouxia","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoni","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11138","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.08.006"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/0278-6125(93)90306-E"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1108\/IJCS-04-2018-0007"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-9452(78)80049-5"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2009.04.001"},{"key":"ref15","first-page":"199","article-title":"Dynamic clustering using particle swarm optimization with application in unsupervised image classification","volume":"9","author":"omran","year":"0","journal-title":"Proceedings of World Academy of Science Engineering and Technology"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780272"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1108\/09574090110806226"},{"key":"ref18","first-page":"1","article-title":"BingHong Wang: research on importance measurement method of network nodes based on degree and clustering coefficient","volume":"62","author":"ren","year":"2013","journal-title":"Acta Physica Sinica"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICSAI.2018.8599440"},{"key":"ref4","first-page":"134","author":"colorni","year":"1991","journal-title":"Distributed Optimization by ant colonies European Conference on Artificial Life"},{"key":"ref27","first-page":"257","article-title":"Analyses on evolution of household energy consumption structure based on information entropy","volume":"19","author":"geng","year":"2004","journal-title":"Journal of natural resources"},{"key":"ref3","first-page":"11","author":"chen","year":"2015","journal-title":"Introduction to Complex Networks Models Structures and Dynamics"},{"key":"ref6","first-page":"227","article-title":"Cross flow: cross &#x2013; 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