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Direct approach for mining association rules from structured XML data
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Published 2014“…The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. …”
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Web Algorithm search engine based network modelling of Malaria Transmission
Published 2013“…There are observed cases of attempting vector control on a trial and errors basis, with no scientific way of determining the locations of critical vector densities. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms
Published 2021“…This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and support vector regressor (SVR). …”
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Distance vector-hop range-free location algorithm for wireless sensor network
Published 2015“…Distance Vector-Hop (DV-Hop) algorithm has become the focus of studies for range-free localization algorithms. …”
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Short term forecasting based on hybrid least squares support vector machines
Published 2018“…In this study, hybrid Least Squares Support Vector Machines (LSSVM) with four meta-heuristic algorithms viz. …”
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Flood Control Distance Vector Hop (FCDV-Hop) localization in wireless sensor networks
Published 2020“…Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. …”
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Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors
Published 2020“…The hybrid algorithm was evaluated using a dataset of pipeline corrosion measurements collected by a Magnetic Flux Leakage (MFL) sensor (with an error margin of ±20 of the true values), and an Ultrasonic (UT) device (with an error margin of ±4). …”
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A new countermeasure to combat the embedding-based attacks on the goldreich-goldwasser-halevi lattice-based cryptosystem
Published 2024“…In this study, we showed that the error vector �⃗! is not unique. We proposed another error vector �⃗∗ to combat the embedding-based attacks. …”
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A new countermeasure to combat the embedding-based attacks on the Goldreich-Goldwasser-Halevi lattice-based cryptosystem
Published 2024“…Consequently, the simplified CVP can be reduced to a Shortest-Vector Problem (SVP) variant which can be solved by using lattice-reduction algorithms such as the LLL algorithm in a shorter amount of time. …”
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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting
Published 2020“…Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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Artificial intelligent power prediction for efficient resource management of WCDMA mobile network
Published 2023“…This artificial intelligent call admission control (CAC) was validated using a dynamic WCDMA mobile network simulator. A few comparative results in downlink have shown that our integrated support vector regression assists genetic algorithm (SVRaGA) is capable of predicting next interval power consumption at Node B with low prediction error and improving the quality of service (QoS) by reducing dropped calls. � 2008 IEICE.…”
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Error Detection of Personalized English Isolated-Word Using Support Vector Machine
Published 2012“…Subsequently, a well-known classification type of artificial intelligent algorithm namely Support Vector Machine (SVM) is used to evaluate those features under two class types of words with proper segregation of correct and erroneous words in two data sets. …”
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