Search Results - (( program normalization techniques algorithm ) OR ( based applications learning algorithm ))
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A review of classification techniques for electromyography signals
Published 2023“…All of the ML classifiers have their own algorithm, special specification, pros and cons based on the available input. …”
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Logic Programming In Radial Basis Function Neural Networks
Published 2013“…Two techniques were developed. The first technique is to encode the logic programming in radial basis function neural networks. …”
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Solving non-convex economic dispatch problem using computational intelligence technique
Published 2023“…Computer programming; Immune system; Scheduling; Artificial Immune System; Computational intelligence techniques; Economic Dispatch; Evolutionary programming algorithms; Log-normal; Non-convex economic dispatches; Reliability test system; Valve-point loading effect; Electric load dispatching…”
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Immune Log-Normal Evolutionary Programming (ILNEP) for solving economic dispatch problem with prohibited operating zones
Published 2023“…Computer programming; Electric power system planning; Electric power systems; Evolutionary algorithms; Immune system; Problem solving; Scheduling; Artificial Immune System; Economic Dispatch; Economic dispatch problems; Electrical power system; Log-normal; Optimization techniques; Prohibited operating zone; Reliability test system; Electric load dispatching…”
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Semi-automatic cortical boundary detection / Noor Elaiza Abdul Khalid.
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Multi -Objective Economic Dispatch Using Evolutionary Programming
Published 2023“…This study focused on solving multi-objectives economic dispatch using Heuristic Optimization (HO) technique which is Multi-Objective Evolutionary Programming (MOEP). …”
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Agent-based extraction algorithm for computational problem solving
Published 2015“…Various CPS techniques have been proposed such as PAC, IPO, Flowchart and Algorithm. …”
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Design Optimization of 3-Phase Rectifier Power Transformers by Genetic Algorithm and Simulated Annealing
Published 2008“…Two non-linear programming techniques, GA and SA are applied for the estimation of transformer design parameters and the results are compared. …”
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Translating medical image to radiological report: Adaptive multilevel multi-attention approach
Published 2022“…Background and Objective: Medical imaging techniques are widely employed in disease diagnosis and treatment. …”
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Design Optimization of 3-phase rectifier power transformers by Genetic Algorithm and Simulated Annealing
Published 2008“…Two non-linear programming techniques, GA and SA are applied for the estimation of transformer design parameters and the results are compared. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023Conference paper -
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Optimal Reactive Power Dispatch for Voltage Stability Improvement Considering Line Contingencies using PSO
Published 2013“…The simulations results are compared with those obtained from the Evolutionary Programming (EP) technique in the attempt to highlight its merit. …”
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Design Optimization of 3-phase rectifier power transformers by Genetic Algorithm and Simulated Annealing
Published 2008“…Two non-linear programming techniques, GA and SA are applied for the estimation of transformer design parameters and the results are compared. …”
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E4ML: Educational Tool for Machine Learning
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Subjects:Conference paper -
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Propose a New Machine Learning Algorithm based on Cancer Diagnosis
Published 2018“…In this review, we focus on the current status of machine learning applications in cancer research, also propose a new algorithm Fast Learning Network to work based on cancer research.…”
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A modified generalized RBF model with EM-based learning algorithm for medical applications
Published 2006“…In this paper, we propose a generalized RBF (GRBF) model to reduce the number of basis functions and thus alleviate curse of dimensionality. An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. …”
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