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1
Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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2
An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. …”
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3
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
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4
Rao-SVM machine learning algorithm for intrusion detection system
Published 2020“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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5
A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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6
Fuzzy modelling using butterfly optimization algorithm for phishing detection
Published 2020“…To generate the fuzzy parameter automatically, an optimization method is required and Butterfly Optimization Algorithm (BOA) is one of the good methods to be applied. …”
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7
Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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8
Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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9
Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
Published 2014“…Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%.…”
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10
A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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11
A comparative analysis of metaheuristic algorithms in fuzzy modelling for phishing attack detection
Published 2021“…The optimization method derives from the metaheuristic algorithm. …”
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12
A fast learning network with improved particle swarm optimization for intrusion detection system
Published 2019“…However, the internal power parameters (weight and basis) of FLN are initialized at random, causing the algorithm to be unstable. …”
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13
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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14
Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. To achieve optimal RBFN-DDA performance, HS (or GSA) is proposed to optimize the center and the width of each hidden unit in a trained RBFN. …”
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15
Fuzzy modelling using firefly algorithm for phishing detection
Published 2019“…To generate the fuzzy parameters automatically, an optimization method is needed. …”
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16
Road damage detection for autonomous driving vehicles using YOLOv8 and salp swarm algorithm
Published 2025“…Consequently, this paper proposes a method to improve the detection accuracy of You Only Look Once version 8 (YOLOv8) using Salp Swarm Algorithm (SSA) for hyperparameter optimization, focusing on eight key parameters. …”
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17
Improved Malware detection model with Apriori Association rule and particle swarm optimization
Published 2019“…Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. …”
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18
Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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19
Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review
Published 2025“…Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. …”
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20
Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…The ADAM optimizer effectively tackles challenges in continuous parameter optimization by dynamically updating the model's weights and biases, adapting the learning rate for each parameter based on accumulated historical gradient information to achieve more efficient minimization of the loss function during training. …”
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