Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm-Reference-Cited by-同舟云学术

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Published:2022 Issue: Volume:10 Page:98475-98489
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ghaleb Sanaa A. A.¹^ORCID,Mohamad Mumtazimah²,Ghanem Waheed Ali H. M.¹^ORCID,Nasser Abdullah B.³,Ghetas Mohamed⁴,Abdullahi Akibu Mahmoud⁵,Saleh Sami Abdulla Mohsen⁶,Arshad Humaira⁷,Omolara Abiodun Esther⁸,Abiodun Oludare Isaac⁸

Affiliation:

1. Faculty of Engineering, University of Aden, Aden, Yemen

2. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia

3. School of Technology and Innovation, University of Vaasa, Vaasa, Finland

4. Faculty of Computer Science, Nahda University, Beni Suef Governorate, Egypt

5. Faculty of Computing and Informatics, Albukhary International University, Kedah, Malaysia

6. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia

7. Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Pakistan

8. Department of Computer Science, University of Abuja, Gwagwalada, Nigeria

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09878104.pdf?arnumber=9878104

Reference49 articles.

1. A semantic-based classification approach for an enhanced spam detection

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3. Disposition-Based Concept Drift Detection and Adaptation in Data Stream

4. Detecting Web Spam Based on Novel Features from Web Page Source Code

5. Analysis of adversarial attacks against traditional spam filters;ozkan;Proc Int Conf All Aspects Cyber Secur,2019

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