Metaheuristics with deep learning driven phishing detection for sustainable and secure environment-Reference-Cited by-同舟云学术

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Metaheuristics with deep learning driven phishing detection for sustainable and secure environment

Published:2023-03 Issue: Volume:56 Page:103114
ISSN:2213-1388
Container-title:Sustainable Energy Technologies and Assessments
language:en
Short-container-title:Sustainable Energy Technologies and Assessments

Author:

Abdullah Alohali Manal,Alasmari Naif,Maashi Mashael,Nouri Amal M.,Rizwanullah Mohammed,Yaseen Ishfaq,Elneil Osman Azza,Alneil Amani A.

Funder

Princess Nourah Bint Abdulrahman University

Deanship of Scientific Research, King Khalid University

Prince Sattam bin Abdulaziz University

King Saud University

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference24 articles.

1. A Look at Machine Learning in the Modern Age of Sustainable Future Secured Smart Cities;Monteiro,2021

2. Green energy harvesting strategies on edge-based urban computing in sustainable internet of things;Lu;Sustain Cities Soc,2021

3. Industry 4.0 and Jobs 2030;Maria,2023

4. Enabling technologies and sustainable smart cities;Ahad;Sustain Cities Soc,2020

5. Achieving smart sustainable cities with GeoICT support: The Saudi evolving smart cities;Aina;Cities,2017

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1. Advancing cybersecurity: a comprehensive review of AI-driven detection techniques;Journal of Big Data;2024-08-04

2. Bald eagle search algorithm: a comprehensive review with its variants and applications;Systems Science & Control Engineering;2024-08

3. Forecasting solar power generation using evolutionary mating algorithm-deep neural networks;Energy and AI;2024-05

4. Machine learning-driven sustainable urban design: transforming Singapore's landscape with vertical greenery;Asian Journal of Civil Engineering;2024-04-15

5. Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence;Concurrency and Computation: Practice and Experience;2024-01-29

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