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.
Highlights
analysis of variance (ANOVA) Analysis of variance data envelopment analysis (DEA)Data envelopment analysisEPPS E-projects portfolio selectionexpected net present value (ENPV) Expected net present value genetic algorithm (GA)Genetic algorithm invasive weed optimization (IWO)Invasive weed optimization Ku Kurtosis
In this research, to solve the EPPS problem on social media platforms, a mathematical model based on a new approach was formulated using a novel multi-objective metaheuristic algorithm named as NSGA-III-multi-objective invasive weed optimization (MOIWO)
The proposed model aimed to minimize the risk of EPPS problems including variance, skewness and kurtosis while maximizing their returns
Summary
Sangaiah et al.: Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics. Web development projects have recently received lots of attention from investors in different countries In this field, E-portfolio is a new concept that aims to find the best portfolio for social media investors. In the EPPS problem, big data-driven decision-making has a great importance in web development environments. Customer behavior can be recognized and five characteristics of big data, which are known as volume, value, velocity, variety and veracity, can be handled These features provide the required input information for EPPS optimization. A mathematical model is proposed to address the EPPS based on social media and big data-driven computing.
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