Search Results - (( data active learning algorithm ) OR ( parameter optimization based algorithm ))
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1
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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2
Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
Published 2026“…In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). …”
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3
Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…In this study, the performance of these three algorithms in obtaining the optimal blade design based on the �436�45D are investigated and compared. …”
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4
Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
Published 2019“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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5
Forecasting of fine particulate matter based on LSTM and optimization algorithm
Published 2024“…The proposed models predict the hourly air pollutant concentration based on 4-h historical input based on six air pollutant data, meteorology parameters, and PM2.5 concentration data from the neighboring air quality monitoring stations. …”
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6
Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. …”
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7
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Two publicly activity datasets are used; Wireless Sensor Data Mining (WISDM) and Physical Activity Monitoring for Aging People (PAMAP2). …”
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8
Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations
Published 2022“…The gradient descent optimization algorithm was observed to help improve the modelâ��s performance. …”
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9
The effect of pre-processing techniques and optimal parameters on BPNN for data classification
Published 2015“…The architecture of artificial neural network (ANN) laid the foundation as a powerful technique in handling problems such as pattern recognition and data analysis. It’s data-driven, self-adaptive, and non-linear capabilities channel it for use in processing at high speed and ability to learn the solution to a problem from a set of examples. …”
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10
Analysis of online CSR message authenticity on consumer purchase intention in social media on Internet platform via PSO-1DCNN algorithm
Published 2024“…Secondly, this work designs optimization measures from inertia weight and learning factor to build an improved particle swarm optimization algorithm (IPSO). …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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12
Stock indicator scanner customization tool using deep reinforcement learning
Published 2022“…Other than that, many current stock indicator scanners only allow user to specify some simple conditions to scan the stocks and do not harness the advancement of machine learning. This project will deliver a web application with dynamic stock prediction model based on deep reinforcement learning or more particularly, Deep Q-Network (DQN) algorithm which enable input customization. …”
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13
Improving neural networks training using experiment design approach
Published 2005“…Conventionally, the parameters of a neural network are tuned by minimizing an objective function based on a pre-determined set of training data. …”
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14
Modular deep neural network in reducing overfitting to enhance generalization / Mohd Razif Shamsuddin
Published 2024“…Most of those research results varies as it uses different data, different network design, different parameters and optimizing algorithm. …”
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15
Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…The synthetic reaction was optimized by Taguchi method based on orthogonal array to evaluate the effect of each parameters and interactive effects of reaction parameters including temperature, time, amount of enzyme, amount of molecular sieve, amount of solvent, and molar ratio of substrates (xylitol: fatty acid). …”
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
Published 2011“…The main idea is the integration of optimal control and parameter estimation. In this work, a simplified model-based optimal control model with adjustable parameters is constructed. …”
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18
On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.…”
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19
Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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20
Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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