Search Results - (( initial optimization based algorithm ) OR ( using combination learning algorithm ))
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1
Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…Firstly, an architecture for the clustering ensemble based on incremental genetic-based algorithms is proposed consisting of two phases: (i) to produce cluster partitions as initial populations, (ii) to combine cluster partitions and to generate final clustering solution by incremental genetic based clustering ensemble learning algorithm. …”
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2
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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3
An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approac...
Published 2023“…This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters.…”
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4
A new function of stereo matching algorithm based on hybrid convolutional neural network
Published 2022“…This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. …”
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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Published 2024journal::journal article -
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. …”
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7
An ensemble data summarization approach based on feature transformation to learning relational data
Published 2015“…In this thesis, a Feature Selection algorithm is investigated and proposed to optimize the TF-IDF vector space by selecting only relevant features from the initial TF-IDF vector space. …”
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8
Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying
Published 2019“…To classify image noise type, the CNN trained with Backpropagation (BP) algorithm and Stochastic Gradient Descent (SGD) optimization technique are implemented. …”
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9
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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10
An optimized ensemble for predicting reservoir rock properties in petroleum industry
Published 2013“…Ensemble is a learning algorithm that combines some experts instead of considering a single best expert for the predictions.The thesis proposed anoptimizing method leading to small structure of assemble GA. …”
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11
White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
Published 2022“…However, with vast amount of WBCs data and various DL architectures available, tuning an optimal DL model is a daunting task. In this project, a diagnostic algorithm for WBCs’ DL analysis is proposed by combining transfer learning approach with fine tuning (FT) approach and tested on Kaggle dataset (9957 images). …”
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12
Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations
Published 2024“…Initially, the algorithm utilized a truncated series. …”
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13
Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems
Published 2021“…For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. …”
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Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman
Published 2019“…In this HPSO algorithm, totally seven particles positions are initialized. …”
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15
A firefly algorithm based hybrid method for structural topology optimization
Published 2020“…In this paper, a firefly algorithm based hybrid algorithm through retaining global convergence of firefly algorithm and ability to generate connected topologies of optimality criteria (OC) method is proposed as an alternative method to solve stress-based topology optimization problems. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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17
Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling
Published 2025“…However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO) algorithm for CHFS. …”
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2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images
Published 2025“…Recently, intelligence-based hotspot detection has been widely used in solar photovoltaic (PV) image applications. …”
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Novel initialization strategy: Optimizing conventional algorithms for global maximum power point tracking
Published 2024“…The major advantages of this approach are eliminating the need to modify the original algorithm, hybridizing with other algorithms, or employing any complex procedures, as in metaheuristic and optimization MPPT algorithms. …”
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Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences
Published 2021“…Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. …”
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