Search Results - parallel operation ((learning algorithm) OR (mining algorithm))*
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…This research opens a wide range of future work to be considered, which includes applying the proposed method in other areas such as web mining, text mining or multimedia mining; and extending the proposed approach to work in parallel computing in data mining.…”
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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A parallel ensemble learning model for fault detection and diagnosis of industrial machinery
Published 2023“…Accordingly, this paper proposes a new parallel ensemble model comprising hybrid machine and deep learning for undertaking FDD tasks. …”
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A study of the high-performance computing parallelism in solving complexity of meteorology data and calculations
Published 2024“…It is associated with utilizing HPC parallelism to simultaneously execute multiple tasks or operations for meteorological research. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. Machine learning algorithms have widely been adopted recently to enhance the performance of IDSs. …”
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K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata
Published 2018“…K Nearest Neighbor Joins (KNN join) are regarded as highly primitive and expensive operations in the data mining.The efficient use of KNN join has proven good results in finding the objects from two data sets prevailed in the huge databases.This has been achieved with the combination of K-Nearest Neighbor query and join operation to find the distinct objects from different data sets.MapReduce is a newly introduced program with the combination of Map Procedure method and Reduce Method widely used in BigData.MapReduce is enriched with parallel distributed algorithm to find the results on a cluster of data sets in BigData.In this paper,the combination of KNN join and MapReduce methods are utilized on the cluster of data sets in BigData for knowledge discovery.Exploring the pinpoint data from huge data sets stored in Big Data demands the distributed large scale data processing.The present research paper is focusing on generic steps for KNN joins exploration operations on MapReduce.The operations of KNN Join are targeted to perform the data partitioning and data pre-processing and necessary calculations.By utilizing the combination of KNN joins with MapReduce methods on BigData data sets will demonstrate a solution for complex computational analysis. …”
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Neuro Symbolic Integration and Agent Based Modelling
Published 2018“…The major domain of neuro-symbolic integration is designed by the theory are usually known as deductive systems which less such elements of human reasoning as adaptation, learning and self-organisation. Meanwhile, neural networks, known as a mathematical model of neurons in the human brain, and have various abilities, and moreover, they also provide parallel computations and therefore can perform some calculations quicker than classical learning algorithms. …”
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Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour
Published 2022“…As a result of this mapping study, five major hybridization strategies were identified in which two-third of solutions have been based on modifying algorithm operators or integration with another metaheuristic. …”
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A modified artificial neural network (ANN) algorithm to control shunt active power filter (SAPF) for current harmonics reduction
Published 2013“…The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. …”
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Super resolution imaging using modified lanr based on separable filtering
Published 2019“…The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. …”
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Investigating computational thinking among primary school students in Terengganu using visual programming
Published 2022“…Quantitative approach was used to measure student’s CT skills of Flow Control, Abstraction, Parallelism, Decomposition, Synchronization, User Interactivity and Logic from their computational artifacts. …”
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