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

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 decisionmaking 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.INDEX TERMS Big data-driven cognitive computing system, social media, E-projects portfolio selection problem, fuzzy system.

Virtual Simulation Management of Data Traffic Optimization of Big Data Cloud Platform considering Multipoint Mapping Algorithm

“…e service performance of cloud platform will certainly be affected. In addition, it will also cause hidden dangers of network security [19,20].…”

A Capacitated Vehicle Routing Problem considering Satisfaction Level of Customers: A Genetic Algorithm

“…In the following, the most important recommendations are given: 1) Considering a time horizon to fulfill the demand of customers [25], 2) Extending the problem considering more real-world assumptions, such as time windows constraint [26], 3) Applying uncertainty techniques to study the uncertain nature of the parameters, such as fuzzy programming [27][28] and robust optimization [29][30], 4) Developing other algorithms to evaluate the performance of the proposed GA, such as runner root algorithm (RRA) [31], particle swarm optimization (PSO) algorithm [32] and cuckoo optimization algorithm (COA) [33]. 5) Considering other objectives (e.g., pollution minimization [34]) and applying efficient multiobjective meta-heuristic algorithms, such as non-dominated sorting genetic algorithm III (NSGA-III) [35].…”

A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

“…Future works can investigate resource constraintsand inventory [32,33] into the model, using fuzzy uncertainty and robust stochastic programming [34]. Moreover, given the type problem (i.e., nonlinearity of the model and NP-hardness of the problem), heuristics [45,46] and meta-heuristic algorithms [51,55,65,66] can be developed to solve large-sized problems in project management.…”