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

Arun Kumar Sangaiah, Alireza Goli, Erfan Babaee Tirkolaee, Mehdi Ranjbar-Bourani, Hari Mohan Pandey, Weizhe Zhang

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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.

Original languageEnglish
Article number9082678
Pages (from-to)82215-82226
Number of pages12
JournalIEEE Access
Volume8
Early online date30 Apr 2020
DOIs
Publication statusE-pub ahead of print - 30 Apr 2020

Keywords

  • Big data-driven cognitive computing system
  • E-projects portfolio selection problem
  • fuzzy system
  • social media

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    Sangaiah, 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. [9082678]. https://doi.org/10.1109/ACCESS.2020.2991394