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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Veröffentlicht in:	IEEE access Jg. 8; S. 82215 - 82226
Hauptverfasser:	Sangaiah, Arun Kumar, Goli, Alireza, Tirkolaee, Erfan Babaee, Ranjbar-Bourani, Mehdi, Pandey, Hari Mohan, Zhang, Weizhe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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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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