Implementation of nature-inspired optimization algorithms in some data mining tasks

Hussein, Mohamed Eid; Alkhalaf, Salem; Mady, Alie El‐Din; Mahmoud, Eman A.; Hussein, Mohamed; Eldin, Ayman M. Baha

doi:10.1016/j.asej.2019.10.003

Cited by 33 publications

(18 citation statements)

References 51 publications

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“…Due to the capability of DL models to enable the implicit capturing of the intricate structures of large-scale data, they have helped in designing of optimal signaling and detection schemes that ensure reliable data transfer [168]. While it is an impossible task to write robust algorithms that can handle multiple tasks and handle large amounts of data of different modes using traditional optimization techniques, DL makes the implementation of algorithms that can learn to accomplish such tasks beyond the level of accuracy of traditional methods a possibility [136]. As a result, with DL, it is possible to design algorithms that enable straightforward analytic algorithms for symbol detection for a variety of channels and modes of information.…”

Section: ) Multimodal Information Understandingmentioning

confidence: 99%

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe

Maharaj

2021

IEEE Access

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The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an indepth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

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Section: ) Multimodal Information Understandingmentioning

confidence: 99%

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe

Maharaj

2021

IEEE Access

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“…Random forest, neural network, and help vector machine classification output is used to identify fraudulent accounts. The precision rates of fake accounts are compared using certain algorithms, and the method is indicated with the highest accuracy [2]. In the past twenty years, social media have expanded exponentially.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Kavin

Karki²,

Hemalatha³

et al. 2022

Wireless Communications and Mobile Computing

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Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and F-measure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.

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“…Due to the rapid development of engineering technology, global optimization problems are widely found in the fields of economic scheduling [1,2], portfolio investment [3], image processing [4], mechanical design [5], neural networks [6,7], data mining [8,9], etc., whereas traditional algorithms can only effectively handle those optimization problems with typical mathematical characteristics [10]. However, a large number of global optimization problems in practical applications are high-latitude, large-scale, and without typical mathematical features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

Yang

Xia

et al. 2021

Symmetry

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The transient search algorithm (TSO) is a new physics-based metaheuristic algorithm that simulates the transient behavior of switching circuits, such as inductors and capacitors, but the algorithm suffers from slow convergence and has a poor ability to circumvent local optima when solving high-dimensional complex problems. To address these drawbacks, an improved transient search algorithm (ITSO) is proposed. Three strategies are introduced to the TSO. First, a chaotic opposition learning strategy is used to generate high-quality initial populations; second, an adaptive inertia weighting strategy is used to improve the exploration ability, exploitation ability, and convergence speed; finally, a neighborhood dimensional learning strategy is used to maintain population diversity with each iteration of merit seeking. The Friedman test and Wilcoxon’s rank sum test were also used by comparing the experiments with recently popular algorithms on 18 benchmark test functions of various types. Statistical results, nonparametric sign tests, and convergence curves all indicate that ITSO develops, explores, and converges significantly better than other popular algorithms, and is a promising intelligent optimization algorithm for applications.

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Implementation of nature-inspired optimization algorithms in some data mining tasks

Cited by 33 publications

References 51 publications

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

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