Big data-driven cognitive computing system for optimization of social media analytics

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorSangaiah, Arun Kumar
dc.contributor.authorGoli, Alireza
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorRanjbar-Bourani, Mehdi
dc.contributor.authorPandey, Hari Mohan
dc.contributor.authorZhang, Weizhe
dc.date.accessioned2020-08-30T20:06:18Z
dc.date.available2020-08-30T20:06:18Z
dc.date.issued2020
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.descriptionBabaee Tirkolaee, Erfan/0000-0003-1664-9210en_US
dc.description.abstractThe integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.en_US
dc.description.sponsorshipKey-Area Research and Development Program of Guangdong Province [2019B010136001]; National Key Research and Development Plan [2017YFB0801801]; National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61672186, 61872110]en_US
dc.description.sponsorshipThis work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010136001, in part by the National Key Research and Development Plan under Grant 2017YFB0801801, and in part by the National Natural Science Foundation of China (NSFC) under Grant 61672186 and Grant 61872110.en_US
dc.identifier.citationSangaiah, A. K., Goli, A., Tirkolaee, E. B., Ranjbar-Bourani, M., Pandey, H. M., & Zhang, W. (2020). Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics. IEEE Access, 8, 82215-82226.en_US
dc.identifier.doi10.1109/ACCESS.2020.2991394en_US
dc.identifier.endpage82226en_US
dc.identifier.issn2169-3536en_US
dc.identifier.scopus2-s2.0-85085153050en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage82215en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2991394
dc.identifier.urihttps://hdl.handle.net/20.500.12713/466
dc.identifier.volume8en_US
dc.identifier.wosWOS:000549502200044en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaeeen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig Data-Driven Cognitive Computing Systemen_US
dc.subjectSocial Mediaen_US
dc.subjectE-Projects Portfolio Selection Problemen_US
dc.subjectFuzzy Systemen_US
dc.titleBig data-driven cognitive computing system for optimization of social media analyticsen_US
dc.typeArticleen_US

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