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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9082678/ |
| _version_ | 1819176159223480320 |
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| author | Arun Kumar Sangaiah Alireza Goli Erfan Babaee Tirkolaee Mehdi Ranjbar-Bourani Hari Mohan Pandey Weizhe Zhang |
| author_facet | Arun Kumar Sangaiah Alireza Goli Erfan Babaee Tirkolaee Mehdi Ranjbar-Bourani Hari Mohan Pandey Weizhe Zhang |
| author_sort | Arun Kumar Sangaiah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T21:06:20Z |
| format | Article |
| id | doaj.art-31c7310b0a7d41f4896efa9a79ccabff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:06:20Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31c7310b0a7d41f4896efa9a79ccabff2022-12-21T18:12:39ZengIEEEIEEE Access2169-35362020-01-018822158222610.1109/ACCESS.2020.29913949082678Big Data-Driven Cognitive Computing System for Optimization of Social Media AnalyticsArun Kumar Sangaiah0https://orcid.org/0000-0002-0229-2460Alireza Goli1Erfan Babaee Tirkolaee2https://orcid.org/0000-0003-1664-9210Mehdi Ranjbar-Bourani3Hari Mohan Pandey4Weizhe Zhang5https://orcid.org/0000-0003-4783-876XSchool of Computing Science and Engineering, Vellore Institute of Technology, Vellore, IndiaDepartment of Industrial Engineering, Yazd University, Yazd, IranDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, IranDepartment of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, IranDepartment of Computer Science, Edge Hill University, Ormskirk, U.K.Peng Cheng Laboratory, Shenzhen, ChinaThe 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.https://ieeexplore.ieee.org/document/9082678/Big data-driven cognitive computing systemsocial mediaE-projects portfolio selection problemfuzzy system |
| spellingShingle | Arun Kumar Sangaiah Alireza Goli Erfan Babaee Tirkolaee Mehdi Ranjbar-Bourani Hari Mohan Pandey Weizhe Zhang Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics IEEE Access Big data-driven cognitive computing system social media E-projects portfolio selection problem fuzzy system |
| title | Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics |
| title_full | Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics |
| title_fullStr | Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics |
| title_full_unstemmed | Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics |
| title_short | Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics |
| title_sort | big data driven cognitive computing system for optimization of social media analytics |
| topic | Big data-driven cognitive computing system social media E-projects portfolio selection problem fuzzy system |
| url | https://ieeexplore.ieee.org/document/9082678/ |
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