Search Results - (( data replication clustering algorithm ) OR ( data implication mining algorithm ))
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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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A cluster-based hybrid replica control protocol for high availability in data grid
Published 2019“…In Data Grid, data replication is a widely used technique for managing data, where exact copies of data or replicas are created and stored at many distributed sites. …”
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A technique of dispatching algorithms for web-server cluster
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Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…It is conflicting with the standard K-means that return different clustering structure in each run, while K-means + + replicates several times to achieve minimum objective function. …”
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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system
Published 2011“…Nowadays, in the grid community, distributed and clustering system, a lot of work has been focused on providing efficient and safe replication management services through designing of algorithms and systems. …”
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Enhanced replication strategy with balanced quorum technique and data center selection method in cloud environment
Published 2022“…Besides, there are also issues that arise during data placement techniques consuming extensive space and high replication time due to replica copies located in every storage node in the same cluster environment to achieve high data availability. …”
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7
Intelligent mining of large-scale bio-data: bioinformatics applications
Published 2017“…It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. …”
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Evaluation and optimization of frequent, closed and maximal association rule based classification
Published 2014“…Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand.The algorithms for closed and maximal item sets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined.In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal item set mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. …”
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Organizational Culture Automated Audit System (OCAAS)
Published 2017“…Several state of the art technologies and techniques were used to design and developed OCAAS which include the use of machine learning and sentiment analysis based novel opinion mining algorithms for electronic opinion analysis and computerized statistics based mathematical algorithms for electronic data analysis as well as MySQL database integration for faster data processing and cognitive ergonomics system interface for user friendly interface navigation.…”
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Datasets Size: Effect on Clustering Results
Published 2013“…This gives a wider acceptance to data mining, being an interdisciplinary field that implements algorithm on stored data with a view to discovering hidden knowledge. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud
Published 2023“…The best algorithm will not be the same for all the data sets. …”
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Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming
Published 2025“…This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production, utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis
Published 2025“…Complementary K-Means clustering grouped the data into two major clusters, indicating that a clear differentiation between economic-based and entrepreneurship-based courses in terms of student enrolment volume and approval distribution. …”
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A framework for automatic modelling of survival using fuzzy inference.
Published 2012“…After the initialisation of the fuzzy inference structure, the replication data (until time to event) will be subject to be trained using the gradient descent and nonnegative least square algorithm to estimate the conditional event probability. …”
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Smart Agriculture Economics and Engineering: Unveiling the Innovation Behind AI-Enhanced Rice Farming
Published 2024“…To address these challenges, an innovative Artificial Intelligence-based (AI-based) predictive algorithm has been proposed, leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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Application of artificial intelligence (AI) in islamic investments
Published 2020“…The technology helps investors to analyse their stocks in terms of price levels, the current stability of each stock and the future price forecasts based on current price and stock data. The study is a conceptual discussion on the application of AI in Islamic investment, which focuses on the discussion of Text Mining, Algorithmic Trading, Stock Pick and Robo in Investment, which include Robo Advisor, Robo Islamic Advisor (RIA) and Robo Financial Advisor (RFA) operating in Islamic investment system. …”
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A guided artificial bee colony (GABC) heuristic for permutation flowshop scheduling problem (PFSP)
Published 2021“…The study experimented according to ten datasets of Taillard benchmark and divided the experiments into several categories and the experiments run every data for several iterations, and for each dataset, there are 20 replications. …”
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