Balance-driven automatic clustering for probability density functions using metaheuristic optimization

Nguyen‐Trang, Thao; Nguyen-Thoi, Trung; Nguyen-Thi, Kim-Ngan; Vovan, Tai

doi:10.1007/s13042-022-01683-8

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…Identification: Legitimate or Counterfeit (ILC), is designed to enhance the biometricbased access control system. ILC leverages the smartphone's capabilities and the advantages of the probability distribution function [25] to differentiate between the genuine owner and possible risks, such as counterfeit phone holders. By distinguishing between legitimate and counterfeit in real-time, ILC ensures that only authorized personnel gain access to restricted resources, thereby enhancing overall security and mitigating potential security threats.…”

Section: Identification: Legitimate or Counterfeit (Ilc) Systemmentioning

confidence: 99%

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Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Edrah,

Ouda

2024

Computers

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With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold.

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Section: Identification: Legitimate or Counterfeit (Ilc) Systemmentioning

confidence: 99%

“…It is used to measure the similarity score between the registered data and the real-time outcome data. This score indicates the likelihood of an event occurring within a specific range of values [25].…”

mentioning

confidence: 99%

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Edrah,

Ouda

2024

Computers

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“…Achieving more effective quasi-optimal solutions for optimization problems has been the main motivation of researchers in the development of numerous metaheuristic algorithms [8]. These metaheuristic algorithms have been used to solve optimization problems in sciences such as Internet of Things (IoT) applications [9][10][11][12][13], data mining [14], wireless network systems [15], clustering [16,17], power engineering applications [18][19][20], and feature selection [21,22].…”

Section: Introductionmentioning

confidence: 99%

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Dehghani,

Bektemyssova,

Montazeri

et al. 2023

Biomimetics

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In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.

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“…Therefore, the number of clusters is determined by the sum of all the values assigned to the genes. Nguyen‐Trang, Nguyen‐Thoi, Nguyen‐Thi, and Vo‐Van (2023) employed the conventional Differential Evolution algorithm to address the balance‐driven CDF problem. In their study, the number of clusters

k

is determined by the value incorporated into the first gene of the chromosomes.…”

Section: Introductionmentioning

confidence: 99%

An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification

Nguyen‐Trang

Vovan

Che‐Ngoc

2023

Statistica Neerlandica

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Clustering is a technique used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, clustering for probability density functions (CDF) has been studied to capture data uncertainty. In CDF, automatic clustering is a clever technique that can determine the number of clusters automatically. However, current automatic clustering algorithms update the new probability density function (pdf) fi(t) based on the weighted mean of all previous pdfs fj(t − 1), j = 1, 2, …, N, resulting in slow convergence. This paper proposes an efficient automatic clustering algorithm for pdfs. In the proposed approach, the update of fi(t) is based on the weighted mean of {f1(t), f2(t),…, fi − 1(t), fi(t − 1), fi+1(t − 1),…,fN(t − 1)}, where N is the number of pdfs and i = 1,2,…, N. This technique allows for the incorporation of recently updated pdfs, leading to faster convergence. This paper also pioneers the applications of certain CDF algorithms in the field of surface image recognition. The numerical examples demonstrate that the proposed method can result in a rapid convergence at some early iterations. It also outperforms other state‐of‐the‐art automatic clustering methods in terms of the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). Additionally, the proposed algorithm proves to be competitive when clustering material images contaminated by noise. These results highlight the applicability of the proposed method in the problem of surface image recognition.This article is protected by copyright. All rights reserved.

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Balance-driven automatic clustering for probability density functions using metaheuristic optimization

Cited by 10 publications

References 35 publications

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification

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