Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (2022)

Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (2)

This journal was converted to full Gold Open Access on January 1, 2022

Editor-in-Chief: Liang-Jie Zhang (Kingdee International Software Group, China)

Indexed In: Compendex (Elsevier Engineering Index), INSPEC, SCOPUS, Web of Science Science Citation Index Expanded (SCIE) and 23 more indices

Published: Continuous Volume |Established: 2004

ISSN: 1545-7362|EISSN: 1546-5004|DOI: 10.4018/IJWSR

Submission to Acceptance: Approx. 23 - 27 Weeks

Acceptance to Publication: Approx. 6 - 10 Weeks

Acceptance Rate: Approx. 11 - 21%

Published: Jan 1, 2022

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DOI: 10.4018/IJWSR.296688

Volume 19

A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage SystemsOpen Access Article

The use of the Cloud computing has been constantly on the rise. However, there are many challenges associated with the Cloud, such as high bandwidth requirements, data security, vendor lock-in and...Show More

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MLA

Khan, Hooria,et al. "A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage Systems." vol.19, no.1 2022: pp.1-20. http://doi.org/10.4018/IJWSR.296688

APA

Khan, H., Zahoor, E., Akhtar, S., & Perrin, O. (2022). A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage Systems. , 19(1), 1-20. http://doi.org/10.4018/IJWSR.296688

Chicago

Khan, Hooria and Ehtesham Zahoor, Sabina Akhtar, and Olivier Perrin. "A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage Systems," 19, no.1: 1-20. http://doi.org/10.4018/IJWSR.296688

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Published: Jan 1, 2022

DOI: 10.4018/IJWSR.299018

Volume 19

An Adaptive System for a Real-Time Matching ApplicationOpen Access Article

In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and...Show More

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Matsutsuka, Taka,et al. "An Adaptive System for a Real-Time Matching Application." vol.19, no.1 2022: pp.1-22. http://doi.org/10.4018/IJWSR.299018

APA

Matsutsuka, T., Ogawa, M., Toriyama, Y., Aso, N., & Iida, I. (2022). An Adaptive System for a Real-Time Matching Application. , 19(1), 1-22. http://doi.org/10.4018/IJWSR.299018

Chicago

Matsutsuka, Taka and Masatoshi Ogawa, Yohei Toriyama, Noriyasu Aso, and Ichiro Iida. "An Adaptive System for a Real-Time Matching Application," 19, no.1: 1-22. http://doi.org/10.4018/IJWSR.299018

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Published: Jan 1, 2022

DOI: 10.4018/IJWSR.299019

Volume 19

Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter DataOpen Access Article

This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform...Show More

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Harendranath, Vegi and Sireesha Rodda. "Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data." vol.19, no.1 2022: pp.1-25. http://doi.org/10.4018/IJWSR.299019

APA

Harendranath, V., & Rodda, S. (2022). Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data. , 19(1), 1-25. http://doi.org/10.4018/IJWSR.299019

Chicago

Harendranath, Vegi, and Sireesha Rodda. "Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data," 19, no.1: 1-25. http://doi.org/10.4018/IJWSR.299019

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Description:

The International Journal of Web Services Research (IJWSR) is the first refereed, international publication featuring the latest research findings and industry solutions involving all aspects of Web services technology. This journal covers advancements, standards, and practices of Web services, as...

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Mission & Scope:

Web Services are among the most important emerging technologies in the e-business, computer software and communication industries. The Web Services technologies will redefine the way that companies do business and exchange information in twenty-first century. They will enhance business efficiency by enabling dynamic provisioning of resources from a pool of distributed resources. Due to the...

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Coverage:

  • Business grid
  • Business process integration and management using web services
  • Case studies for web services
  • Communication applications using web services
  • Composite web service creation and enabling infrastructures
  • Dynamic invocation mechanisms for web services
  • E-commerce applications using web services
  • Frameworks for building web service applications
  • Grid-based web services applications (e.g. OGSA)
  • Interactive TV applications using web services
  • Mathematic foundations for service oriented computing
  • Multimedia applications using web services
  • Quality of service for web services
  • Resource management for web services
  • Semantic services computing
  • SOAP enhancements
  • Solution management for web services
  • UDDI enhancements
  • Web services architecture
  • Web services discovery
  • Web services modeling
  • Web services performance
  • Web services security

Editorial Board

Editor in Chief

Liang-Jie Zhang, IBM T.J. Watson Research, United States
Managing Editor

Bo Hu, Research Institute of Technology, China
Associate Editors
Anup Kumar, University of Louisville, United States
Frank Leymann, University of Stuttgart, Germany
Hemant Jain, University of Wisconsin-Milwaukee, United States
Jeffrey Tsai, University of Illinois, United States
Jun Shen, University of Wollongong, Australia Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (6)https://orcid.org/0000-0002-9403-7140
Keqing He, Wuhan University, China
Ling Liu, Georgia Institute of Technology, United States
Stanley Su, University of Florida, United States
YIwen Zhang, Anhui University,China
Yutao Ma, Wuhan University, China Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (7)https://orcid.org/0000-0003-4239-2009
Editorial Review Board
Akhilesh Bajaj, University of Tulsa, United States Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (8)https://orcid.org/0000-0002-7061-1953
Anas Alsobeh, Yarmouk Univerity, Jordan Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (9)https://orcid.org/0000-0002-1506-7924
Ankur Bist, Graphic Era Hill University, India Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (10)https://orcid.org/0000-0001-8679-2624
Atul Sajjanhar, Deakin University, Australia Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (11)https://orcid.org/0000-0002-0445-0573
Banage Kumara, Sabaragamuwa University of Sri Lanka, Sri Lanka Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (12)https://orcid.org/0000-0003-3941-2275
Bo Cheng, Beijing University of Posts and Telecommunications, China
Bo Wang, Dalian University of Technology, China Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (13)https://orcid.org/0000-0001-9320-7534
David Buttler, Lawrence Livermore National Laboratory, United States
Dilip Sharma, GLA University, India
Hanchuan Xu, Harbin Institute of Technology, China
Jian Wang, Wuhan University, China
Marcus Fontoura, Stone Co., United States
Mayur Ramgir, London School of Emerging Technology, United States Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (14)https://orcid.org/0000-0002-9006-9073
Mohan Tanniru, Oakland University, United States Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (15)https://orcid.org/0000-0002-9584-0090
Pedro Furtado, Universidade de Coimbra, Portugal
Pelin Angin, Middle East Technical University, Turkey
Qiang He, Swinburne University of Technology, Australia
Rui Xu, Hohai University, China
Sang-Bing Tsai, Wuyi University, China; International Engineering and Technology Institute, Hong Kong, Taiwan Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (16)https://orcid.org/0000-0001-6988-5829
Shijun Liu, Shandong University, China Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (17)https://orcid.org/0000-0002-4108-1391
Simantra Mitra, Iowa State University, United States
Soe-Tsyr Yuan, National Chengchi University, Taiwan
Thomas Potok, Oak Ridge National Lab, United States
Wei Zhang, Amazon, United States
Weifeng Pan, Zhejiang Gongshang University, China Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data (18)https://orcid.org/0000-0001-6355-1385
Wu Chou, Avaya Labs Research, United States
Xian-He Sun, Illinois Institute of Technology, United States
Xinlian Liu, Hood College, United States
Yunni Xia, Chongqing University, China

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