Search Results - (( variable generation learning algorithm ) OR ( parallel distribution function algorithm ))
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Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system
Published 2023Subjects:Conference paper -
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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3
High performance visualization of human tumor growth software
Published 2008“…The platform for high performance computing of the parallel algorithms run on a distributed parallel computer system. …”
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High performance simulation for brain tumors growth using parabolic equation on heterogeneous parallel computer systems
Published 2007“…This paper focuses on the implementation of parallel algorithm for the simulation of brain tumours growth using one dimensional parabolic equation, design on a distributed parallel computer system. …”
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5
Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…The first step lies at the modelling of parallel applications running on heterogeneous parallel computers. …”
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High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system
Published 2007“…This paper focuses on the implementation of parallel algorithm for the simulation of brain tumours growth using one dimensional parabolic equation, design on a distributed parallel computer system. …”
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WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm
Published 2023“…The algorithm works by finding the minimum transmitter power with the help of Distributed Parallel Genetic Algorithm (DPGA) employed on an offload microcontroller system to form optimal beam coverage to reduce power usage of adaptive antenna at WCDMA base station. …”
Conference paper -
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A critical analysis of simulators in grid
Published 2015“…In parallel and distributed computing environment such as "The Grid", anticipating the behavior of the resources and tasks based on certain scheduling algorithm is a great challenging.Thus, studying and improving these types of environments becomes very difficult. …”
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Implementation of Parallel K-Means Algorithm to Estimate Adhesion Failure in Warm Mix Asphalt
Published 2020“…The results showed that the PKIP algorithm decreases the execution time up to 30 to 46 if compared with the sequential k means algorithm when implemented using multiprocessing and distributed computing. …”
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10
Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
Published 2024“…Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. …”
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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. …”
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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
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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14
Automatic generic process migration system in linux
Published 2012“…A fexible interface to the underlying checkpoint/ restart subsystem is designed which permits users to specify the migration mechanism according to process constraints. A migration algorithm is designed which attempts to exploit the unique features of the basic migration algorithms to form a generic algorithm. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia
Published 2025“…Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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19
Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System
Published 2013“…A number of benchmark function processes were conducted to assess the performance of proposed FOA (Fly Optimisation Algorithm).…”
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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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