Search Results - (( parameter evaluation method algorithm ) OR ( risk optimization method algorithm ))
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1
Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…PSO-EBGWO is used to optimize the parameters of the CatBoost model. In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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2
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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3
Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…Drawing from an extensive review of existing predictive models and cardiovascular health risk factors, this research proposes an enhanced ADAM optimization algorithm, integrated with advanced data processing and feature selection methodologies, to identify and refine key predictors for improved model performance. …”
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4
Optimizing n-1 contingency rankings using a nature-inspired modified sine cosine algorithm
Published 2025“…Compared with established methods such as Ant Colony Optimization (ACO) and Genetic Algorithm (GA), MSCA exhibits superior computational efficiency while maintaining competitive accuracy. …”
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5
Utilizing self-organization systems for modeling and managing risk based on maintenance and repair in petrochemical industries
Published 2018“…In order to evaluate the accuracy of the model, we compare it with the fuzzy model, and the results indicate that self-organizing systems optimized with the genetic algorithm have higher ability, flexibility and accuracy than the fuzzy model in predicting risk.…”
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6
Improving earth surface temperature forecasting through the optimization of deep learning hyper-parameters using barnacles mating optimizer
Published 2024“…To improve the DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) is integrated to optimize both weights and biases. …”
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7
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…In this task, two neural network algorithms, Recurrent Neural Networks (RNN) and Multi-Layer Perceptron Neural Networks (MLP-NN) were used and the hyper-parameters of the network architecture was optimized based on a systematic grid search. …”
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8
Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia
Published 2019“…Cuckoo search), and evaluator or model inducing algorithms (e.g SVM) were utilized for feature subset selection, which further compared to select the optimal conditioning factors subset. …”
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9
Chemometric approaches in the evaluation of trace metals in commercially raised tilapia and preliminary health risk assessment of its consumption / Low Kah Hin
Published 2012“…The most significant microwave parameters were further evaluated by Box–Behnken design, while others were kept constant. …”
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10
Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
Published 2025“…In recent years, statistical methods and, increasingly, machine learning-based approaches have gained popularity for landslide susceptibility modeling. …”
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11
Dynamic investment model for the restructed power market in the presence of wind source
Published 2014“…In the third step, the long term optimal investment strategies of the hybrid wind-thermal investor are determined based on the dynamic programming algorithm by considering the long term states of demand growth and fuel price uncertainties. …”
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12
Multi-Objective Portfolio Optimization Strategy using the SPEA-II Algorithm
Published 2025“…The study highlights that the SPEA-II algorithm can serve as an effective and efficient method for stock portfolio selection and optimization, helping investors to identify portfolios with lower risk and higher return…”
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13
Embedded Dual Band Rfid Based Blood Glucose Monitoring System For Internet Of Medical Things
Published 2020“…These two parameters are then taken into account in experimental setup for performance evaluation of the enhanced CSMA/CA (EN-CSMA/CA) algorithm that uses an external interrupt mechanism and a cross layer approach. …”
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14
An Empirical Study on the Construction of A Non-Convex Risk Parity Portfolio using a Genetic Algorithm
Published 2025“…While conventional numerical methods can be applied, they often struggle with inefficiency and fail to deliver optimal results. …”
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15
Optimizing Visual Sensors Placement with Risk Maps Using Dynamic Programming
Published 2022“…This article explores the efficiency of the visual sensor placement based on a combination of two methods namely, a deterministic risk estimation for the risk assessment and a dynamic programming for optimizing the placement of surveillance cameras. …”
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16
Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM
Published 2025“…EMA was applied for hyperparameter optimization, demonstrating improved convergence and generalization over conventional methods. …”
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17
Predicting bankruptcy using ant colony optimization / Nur Syafiqah Abdul Ghani
Published 2021“…The results have shown that the Ant Colony Optimization Algorithm produced a better predictive accuracy. …”
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Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
Published 2023“…Machine learning algorithm such as LightGBM can be used to evaluate credit risk. …”
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19
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
Published 2023“…Machine learning algorithm such as LightGBM can be used to evaluate credit risk. …”
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20
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
Published 2023“…Machine learning algorithm such as LightGBM can be used to evaluate credit risk. …”
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