Search Results - (( parameter optimization window algorithm ) OR ( variable estimation learning algorithm ))
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1
An optimized aggregate marker algorithm for bandwidth fairness improvement in classifying traffic networks
Published 2016“…This article analyses and evaluates a new time sliding window traffic marker algorithm called the Optimized time sliding window Three Colour Marker (OtswTCM). …”
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2
Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Second, an Optimized time sliding window packet marker (OTSWTCM) algorithm. …”
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3
Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm
Published 2023“…This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. …”
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4
Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. …”
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5
Design of intelligent Qira’at identification algorithm
Published 2017“…The process of the SPAP Algorithm is to extend parameters of the Affine Projection Block with two different selections of windowing length that affect the final accuracy on pattern classification. …”
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6
Development of an education simulator for particle swarm optimization in solving economic dispatch problems: article / Mohd Hafiz Mat Hussain
Published 2009“…In the developed simulator, users are able to set the parameters that have influences on particle swarm optimization performance. …”
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Development of an education simulator for particle swarm optimization in solving economic dispatch problems / Mohd Hafiz Mat Hussain
Published 2009“…In the developed simulator, users are able to set the parameters that have influences on particle swarm optimization performance. …”
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8
Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Published 2025Subjects:Article -
9
Efficient management of Top-k queries over Uncertain Data Streams with dynamic Sliding Window Model
Published 2024“…This method reduces computational costs by efficiently handling the insertion and exit policy for the appropriate tuple candidates within a specified window frame. The experiments in this study compare the SWMTop-kDelta algorithm with two previous researchers and two baseline approach algorithms to evaluate their effectiveness. …”
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10
SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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11
Improving forest above-ground biomass estimation by integrating individual machine learning models
Published 2024“…Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. …”
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12
Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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13
Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…Observations of the results of this study revealed that the solar radiation (Rs) is the most essential variable for estimating ET0 in Peninsular Malaysia. …”
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14
A case study on quality of sleep and health using Bayesian networks
Published 2012“…The network scores computation is implemented to estimate the fitting of the resulting network of each structural learning algorithm in order to choose the best-fitted network. …”
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15
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms
Published 2023Article -
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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17
Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…The ECDRQ Routing Algorithm integrates the ECQ and Dual Reinforcement Q (DRQ) Routing Algorithms with Alternative Q Value Approach to minimise the effect of partially learning cycle. …”
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18
Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management
Published 2024“…Particle Swarm Optimization (PSO) has demonstrated its efficacy in addressing the issue of construction waste reduction and enhancing the accuracy of cost estimation through the identification of optimal combinations of variables. …”
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19
Distributed joint power control, beamforming and spectrum leasing for cognitive two-way relay networks
Published 2017“…A search method with numerous advantages over conventional algorithms, has been designed to solve the optimization problems with an enhanced global optimality and convergence speed. …”
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20
Creating Air Temperature Models for High-Elevation Desert Areas Using Machine Learning
Published 2023“…This is particularly important in high-elevation regions. In this study, we estimate Ta in the high-elevation desert zone of Kilimanjaro (>4500m) using four models (five models including the benchmark model) with unique sets of inputs using five machine learning (ML) algorithms. …”
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