Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics

The 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 selecti...

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Vydáno v:	IEEE access Ročník 8; s. 82215 - 82226
Hlavní autoři:	Sangaiah, Arun Kumar, Goli, Alireza, Tirkolaee, Erfan Babaee, Ranjbar-Bourani, Mehdi, Pandey, Hari Mohan, Zhang, Weizhe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Big Data Big data-driven cognitive computing system Cognitive systems Computation Computational modeling Data analysis Decision analysis Decision making Digital media E-projects portfolio selection problem fuzzy system Genetic algorithms Investment Kurtosis Mathematical analysis Multiple objective analysis Optimization Performance evaluation Portfolios social media Social networking (online) Social networks Software development Sorting algorithms
ISSN:	2169-3536, 2169-3536
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Abstract	The 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.
AbstractList	The 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.
Author	Ranjbar-Bourani, Mehdi Zhang, Weizhe Pandey, Hari Mohan Goli, Alireza Tirkolaee, Erfan Babaee Sangaiah, Arun Kumar
Author_xml	– sequence: 1 givenname: Arun Kumar orcidid: 0000-0002-0229-2460 surname: Sangaiah fullname: Sangaiah, Arun Kumar organization: School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India – sequence: 2 givenname: Alireza surname: Goli fullname: Goli, Alireza organization: Department of Industrial Engineering, Yazd University, Yazd, Iran – sequence: 3 givenname: Erfan Babaee orcidid: 0000-0003-1664-9210 surname: Tirkolaee fullname: Tirkolaee, Erfan Babaee organization: Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran – sequence: 4 givenname: Mehdi surname: Ranjbar-Bourani fullname: Ranjbar-Bourani, Mehdi organization: Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran – sequence: 5 givenname: Hari Mohan surname: Pandey fullname: Pandey, Hari Mohan organization: Department of Computer Science, Edge Hill University, Ormskirk, U.K – sequence: 6 givenname: Weizhe orcidid: 0000-0003-4783-876X surname: Zhang fullname: Zhang, Weizhe email: wzzhang@hit.edu.cn organization: Peng Cheng Laboratory, Shenzhen, China
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Cites_doi	10.1007/978-1-4613-0303-9_33 10.1016/j.ijinfomgt.2018.08.006 10.1108/LHTN-11-2018-0070 10.1126/science.220.4598.671 10.1016/j.engappai.2017.10.010 10.1007/s00521-018-3924-0 10.1016/j.ejor.2004.01.040 10.1016/j.eswa.2004.10.014 10.1016/j.eswa.2011.04.183 10.1016/S0377-2217(02)00881-0 10.1016/j.jdeveco.2008.07.002 10.2307/2975974 10.1007/s12190-008-0154-0 10.1016/j.swevo.2016.01.001 10.1111/risa.12801 10.1109/4235.996017 10.1002/sys.21301 10.7551/mitpress/3927.001.0001 10.1166/jmihi.2019.2692 10.1002/cpe.5130 10.1016/j.eswa.2015.06.057 10.1016/j.indmarman.2019.09.001 10.1109/SYSMART.2018.8746983 10.1016/j.ecoinf.2006.07.003 10.1016/j.ijinfomgt.2018.06.005 10.1016/j.asoc.2015.11.005 10.1016/j.eswa.2017.02.033 10.1016/j.cam.2018.10.039 10.1109/TFUZZ.2018.2842752 10.1016/j.techfore.2017.07.012 10.1007/978-3-030-05252-2_11 10.1109/TFUZZ.2002.800692 10.1016/j.ejor.2006.04.010 10.1016/j.swevo.2019.02.003 10.1016/j.cam.2007.06.009 10.1007/s00607-018-00692-2 10.1016/j.econmod.2017.03.020 10.1016/j.ejor.2013.10.060 10.1016/j.chb.2018.08.039 10.1016/j.automatica.2012.08.036 10.1109/TEVC.2013.2281535 10.1016/j.amc.2015.01.050 10.1007/s11771-016-3061-9
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References	ref35 zitzler (ref10) 2001 ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 hao (ref25) 2009; 30 ref24 ref23 ref26 ref20 ref42 ref41 ref22 ref44 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
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SubjectTerms	Algorithms Big Data Big data-driven cognitive computing system Cognitive systems Computation Computational modeling Data analysis Decision analysis Decision making Digital media E-projects portfolio selection problem fuzzy system Genetic algorithms Investment Kurtosis Mathematical analysis Multiple objective analysis Optimization Performance evaluation Portfolios social media Social networking (online) Social networks Software development Sorting algorithms
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Title	Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics
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