Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107408 - 107408

Published: Aug. 29, 2023

Language: Английский

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Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 291 - 291

Published: May 13, 2024

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, unsatisfactory speed when facing complex problems. In response, this paper proposes the multi-strategy improved algorithm (MDBO). core improvements include using Latin hypercube sampling better initialization introduction of novel differential variation strategy, termed "Mean Differential Variation", enhance algorithm's ability evade optima. Moreover, strategy combining lens imaging reverse learning dimension-by-dimension was proposed applied current optimal solution. Through comprehensive performance testing on standard benchmark functions CEC2017 CEC2020, MDBO demonstrates superior in terms accuracy, stability, compared with other classical metaheuristic algorithms. Additionally, efficacy addressing real-world engineering problems validated through three representative application scenarios namely extension/compression spring design problems, reducer welded beam

Language: Английский

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A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 142, P. 110319 - 110319

Published: April 22, 2023

Language: Английский

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Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(6), P. 3791 - 3844

Published: April 12, 2023

Language: Английский

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PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 10, 2024

Abstract The challenge of developing an Android malware detection framework that can identify in real-world apps is difficult for academicians and researchers. vulnerability lies the permission model Android. Therefore, it has attracted attention various researchers to develop using or a set permissions. Academicians have used all extracted features previous studies, resulting overburdening while creating models. But, effectiveness machine learning depends on relevant features, which help reducing value misclassification errors excellent discriminative power. A feature selection proposed this research paper helps selecting features. In first stage framework, t -test, univariate logistic regression are implemented our collected data classify their capacity detecting malware. Multivariate linear stepwise forward correlation analysis second evaluate correctness selected stage. Furthermore, as input development models three ensemble methods neural network with six different machine-learning algorithms. developed models’ performance compared two parameters: F-measure Accuracy. experiment performed by half million apps. empirical findings reveal implementing achieved higher rate set. Further, when previously frameworks methodologies, experimental results indicates study accuracy 98.8%.

Language: Английский

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Multi-objective optimization algorithm based on clustering guided binary equilibrium optimizer and NSGA-III to solve high-dimensional feature selection problem

Information Sciences, Journal Year: 2023, Volume and Issue: 648, P. 119638 - 119638

Published: Sept. 1, 2023

Language: Английский

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Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 24516 - 24524

Published: Jan. 1, 2024

Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target operating systems (OS) that are extremely utilized in smartphones tablets. As ecosystem endures produce, therefore risk attacks on these devices. Identifying vital for keeping user data, privacy, device integrity. detection utilizing deep learning (DL) signifies a cutting-edge system maintenance mobile DL approaches namely recurrent neural network (RNN) convolutional (CNN) best automatically removing intricate designs behaviors app data. By leveraging features such as application programming interface (API) call sequences, code patterns, permissions, efficiently differentiated between benign apps, even face previous unseen attacks. This study presents an Intelligent Pattern Recognition using Equilibrium Optimizer with Deep Learning (IPR-EODL) Approach Malware Recognition. The purpose IPR-EODL approach properly identify categorize way security can be achieved. In technique, data pre-processing step was applied convert input into compatible setup. addition, technique applies channel attention long short-term memory (CA-LSTM) methodology malware. To enhance solution CA-LSTM algorithm, employs optimization (EO) algorithm hyperparameter tuning method. experimentation evaluation model verified benchmark database. extensive results highlight significant result process.

Language: Английский

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Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 22, 2024

Abstract Feature selection is a critical component of machine learning and data mining to remove redundant irrelevant features from dataset. The Chimp Optimization Algorithm (CHoA) widely applicable various optimization problems due its low number parameters fast convergence rate. However, CHoA has weak exploration capability tends fall into local optimal solutions in solving the feature process, leading ineffective removal features. To solve this problem, paper proposes Enhanced Hierarchy for adaptive lens imaging (ALI-CHoASH) searching classification subset Specifically, enhance exploitation CHoA, we designed chimp social hierarchy. We employed novel class factor label situation each chimp, enabling effective modelling relationships among individuals. Then, parse chimps’ collaborative behaviours with different classes, introduce other attacking prey autonomous search strategies help individuals approach solution faster. In addition, considering poor diversity groups late iteration, propose an back-learning strategy avoid algorithm falling optimum. Finally, validate improvement ALI-CHoASH capabilities using several high-dimensional datasets. also compare eight state-of-the-art methods accuracy, size, computation time demonstrate superiority.

Language: Английский

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Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 10671 - 10715

Published: May 9, 2024

Language: Английский

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Adaptive multi-population artificial bee colony algorithm based on fitness landscape analysis

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111952 - 111952

Published: July 14, 2024

Language: Английский

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