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1
Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system
Published 2023Subjects:Conference paper -
2
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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4
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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6
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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Article -
7
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 -
8
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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9
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
Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023“…Genetic algorithms are powerful optimizing techniques that are used successfully to solve problems in many different disciplines. …”
Conference paper -
11
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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12
Investigation of evolutionary multi-objective algorithms in solving view selection problem / Seyed Hamid Talebian
Published 2013“…In addition to the normal metrics, the computational time for executing each algorithm was also measured and compared. …”
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Thesis -
13
Statistical approach on grading: mixture modeling
Published 2006“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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14
The comparison of tardiness in scheduling techniques for task distribution using grid simulation tool
Published 2008“…A good scheduling algorithm normally shows lower value of total tardiness and schedule time. …”
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15
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…One of the important assumptions of the linear model is that the error terms are normally distributed. Unfortunately, many researchers are not aware that the performance of the OLS can be very poor when the data set that one often makes a normal assumption, has a heavy-tailed distribution which may arise as a result of the presence of outliers. …”
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16
Mitigating Unbalanced And Overlapped Problems Of Large Network Intrusion Data Using Multiplelevel Detection Techniques
Published 2022“…Besides, data set classes may overlap because of their high similarity. These problems have caused a low detection rate for intrusions that are the minority in data sets because learning algorithms favour the majority class (normal traffic). …”
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Final Year Project / Dissertation / Thesis -
17
Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
Published 2021“…The existing techniques (HS and its variants) have issues in terms of searching strategy (exploration and exploitation), slow convergence rate and high computation time for solving the scheduling problems maximally or near to optimal one. Therefore, a tasks distribution in driver scheduling using dynamic set of bandwidth in harmony search algorithm with 2-opt (SBHS2-opt) was proposed in this research. …”
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18
A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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19
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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20
Statistical approach on grading the student achievement via normal mixture modeling
Published 2006“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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