Efficient feature selection for inconsistent heterogeneous information systems based on a grey wolf optimizer and rough set theory

Hamed, Ahmed Abdeen; Nassar, Hamed

doi:10.1007/s00500-021-06375-z

Cited by 15 publications

(5 citation statements)

References 40 publications

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“…The position vector specifies the variable values for the problem under study. For example, M 32 = [5,10,7,9] means that the problem is of 4 variables and whose values, for monkeypox copy number 3 at generation 2, are 5, 10, 7 and 9, respectively. At first (generation j = 1), the virus searches for the desired cell and then performs the rest of the steps.…”

Section: Monkeypox Optimization (Mo) Algorithmmentioning

confidence: 99%

“…Yet, another direction is to mimic how natural swarms like birds, fish, and insects work collaboratively to find a food source. Examples in this direction are particle swarm optimization [3], artificial bee colony [4], grey wolf optimization [5] and whale optimization algorithm (WOA) [6]. One more direction is to mimic the natural physical phenomena involving the physics of matter and energy.…”

Section: Introductionmentioning

confidence: 99%

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A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

Mohamed

Hamed

2023

Preprint

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Section: Monkeypox Optimization (Mo) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

Mohamed

Hamed

2023

Preprint

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“…Feature selection is employed to discern significant features from inconsequential features within a predetermined feature collection ( Hamed & Mohamed, 2023 ; Hamed & Nassar, 2021 ; Ablel-Rheem et al, 2020 ). Feature selection aims to minimize the size of high-dimensional classification issues while improving prediction accuracy in classification problems ( Sağbaş & Ballı, 2024 ).…”

Section: Introductionmentioning

confidence: 99%

“…Mobile continuous authentication models typically employ feature selection to enhance the accuracy of machine learning-based biometric authentication for smartphone users ( Hamed & Nassar, 2021 ). The literature also emphasizes the use of bioinspired feature extraction algorithms such as Grey Wolf Optimization (GWO) ( Almazroi & Eltoukhy, 2023 ), Particle Swarm Optimization (PSO) ( Rostami et al, 2020 ), Whale Optimization Algorithm (WOA) ( Mirjalili & Lewis, 2016 ), Harris Hawks Optimization (HHO) ( Heidari et al, 2019 ), and Bayesian Optimization Algorithm (BOA) ( Yang, Liu & Wen, 2024 ).…”

Section: Introductionmentioning

confidence: 99%

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

Eltoukhy,

Gaber,

Almazroi

et al. 2024

PeerJ Computer Science

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This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62 ± 0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.

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“…Feature selection is utilized to identify important features from irrelevant features of a predefined feature set [1,2]. The key objectives of feature selection are to reduce data dimensionality and improve prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Almazroi¹,

Eltoukhy²

2023

Computers, Materials &Amp; Continua

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Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user's identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users' touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones. We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy. The random forest classifier is used to evaluate these tactics. The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset. It achieved a 97.89% accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly; processing power of the device and memory to validate individuals. Simultaneously, the findings revealed that our approach has a lower equal error rate (EER) of 0.5104, a false acceptance rate (FAR) of 1.00, and a false rejection rate (FRR) of 0.0209 compared to the methods discussed in the literature. These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

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Efficient feature selection for inconsistent heterogeneous information systems based on a grey wolf optimizer and rough set theory

Cited by 15 publications

References 40 publications

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

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