Search Results - (( deviation selection based algorithm ) OR ( based optimization method algorithm ))
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Optimal input features selection of wavelet-based EEG signals using GA
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Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana
Published 2018“…In this paper, we use two-step features selection and Quality Threshold with Optimization methods to design anomaly-based HIDS and NIDS separately. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
Published 2024“…Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.…”
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Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending
Published 2006“…To validate the finite element model physical experiments were conducted. A neural network algorithm based on the backpropagation algorithm has been developed. …”
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Optimal distribution system reconfiguration incorporating distributed generation based on simplified network approach / Mohammad Al Samman
Published 2020“…With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and voltage deviation of the EDN, this work proposes a new method based on a two-stage optimization. …”
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An enhanced sequential exception technique for semantic-based text anomaly detection
Published 2019“…ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. …”
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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm
Published 2020“…The existing data-driven neuroendocrine-PID (NEPID) utilizes the simultaneous perturbation stochastic approximation (SPSA) algorithm as the data-driven tool. However, this SPSA-based method is unable to find the optimal value of the design parameter due to unstable convergence obtained that degrades the controller performance in MIMO systems. …”
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Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
Published 2007“…For the image segmentation, genetic algorithm are use to segment the bone image and it is a new method that applies in the image processing field. …”
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Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin
Published 2013“…The problem was initially tackled by “exact methods” such as the branch and bound method, which is based on the exhaustive enumeration of a restricted region of solutions containing exact optimal solutions. …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…Second of all, this study operates within experimental design with Taguchi method to discover the optimal design factors for the two proposed genetic operators. …”
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Statistical band selection for descriptors of MBSE and MFCC-based features for accent classification of Malaysian English / Yusnita M. A. ...[et al.]
Published 2013“…This paper proposes an efficient way of analyzing the ethnical accent using statistical knowledge of log-energies of fourier transformed derived mel-filter banks. A simple algorithm to select bands so called statistical band selection (SBS) method using smallest variances within class scores was developed to optimize the presentation of speech features. …”
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Garra Rufa‐inspired optimization technique
Published 2020“…In welldeveloped optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. …”
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Development of robust procedures for partial least square regression with application to near infrared spectral data
Published 2021“…To fill-in the gap in the literature, a new robust procedure in wavelength selection based on input scaling method is developed using Filter-Wrapper method. …”
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