Search Results - (( evolution classification _ algorithm ) OR ( data replication server algorithm ))
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1
Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)
Published 2013“…Proper mechanism is significantly required in order to manage the complex heterogeneous data replication. This paper presents a new algorithm namely the Persistence Layer Synchronous Replication (PLSR) in order to manage the agent handling the replication. …”
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2
Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system
Published 2011“…All the replication servers established its connection through interfaces. …”
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3
Improving response time, availability and reliability through asynchronous replication technique in cluster architecture of web server cluster
Published 2010“…This paper proposes an algorithm of data replication scheme based on asynchronous replication in order to improve web server cluster system reliability. …”
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4
A technique of dispatching algorithms for web-server cluster
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5
Fault Tolerence on Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique
Published 2013“…It also increases the degrees of data availability. This is because the missing data from during the server failure has been reconciled and replicated after that server recovered.…”
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6
Fault Tolerance On Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique
Published 2013“…It also increases the degrees of data availability. This is be cause the missing data from during the server failure has been reconciled and replicated after that server recovered.…”
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7
Binary Vote Assignment on Cloud Quorum Algorithm for Fragmented MyGRANTS Database Replication
Published 2015“…Data replication is one of the mechanisms to manage data since it improves data accessibility and reliability. …”
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Binary vote assignment on grid quorum replication technique with association rule
Published 2018“…However, these techniques have its weaknesses in terms of communication costs that is the total replication servers needed to replicate the data. Furthermore, these techniques also do not consider the correlation between data during the fragmentation process. …”
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9
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). …”
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10
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. …”
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12
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm
Published 2019“…The spectral features were performed by employing the relative spectral powers of delta (δRP), theta (θRP), alpha (αRP), beta (βRP), and gamma (γRP). The differential evolution-based channel selection algorithm (DEFS_Ch) was computed to find the most suitable EEG channels that have the greatest efficacy for identifying the various emotional states of the brain regions. …”
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14
A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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Intersection Features For Android Botnet Classification
Published 2019“…This paper proposed an enhancement approach for Android botnet classification based on features selection and classification algorithms. …”
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Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Unlike a conventional PSOIACO algorithm, this hybrid algorithm shows improvement of the classification accuracy in its generated rough reducts to solve NP-Hard problem. …”
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EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization
Published 2019“…In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification
Published 2012“…Individual emotional states are highly variable and are subject to evolution from personal experiences. For this reason, the above system is designed to be able to perform learning and classification in real-time to account for inter-individual and intra-individual emotional drift over time. …”
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20
A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
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