Search Results - (( data evaluation method algorithm ) OR ( parameter estimation max algorithm ))
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1
Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System
Published 2016“…This algorithm based on the angle of arrival (AOA) and angle of departure (AOD) measurement parameter completed the new SMBS algorithm with virtual base station (SMVirBS). …”
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2
Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. All of the algorithms are later combined to provide device location estimation for multi-floor environment. …”
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3
Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
Published 2025“…This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. …”
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Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach
Published 2024“…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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New correlation for oil formation volume factor
Published 2014“…It is a family of inductive algorithms which executes computer-based mathematical modeling of multi-parametric data sets. …”
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Conference or Workshop Item -
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An Enhanced Hybrid Method Using AES Algorithm And Arabic Text Steganography
Published 2024thesis::master thesis -
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Case Slicing Technique for Feature Selection
Published 2004“…Since the 1960s, many algorithms for data classification have been proposed. …”
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8
Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream
Published 2023“…Existing clustering algorithms for outlier detection encounter significant challenges due to insufficient data pre-processing methods and the absence of a suitable data summarization framework for effective data stream clustering. …”
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9
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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10
Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…In fact, no previous studies employ mixed evaluation method in evaluating and comparing the performance measures. …”
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11
Performance evaluation of different data aggregation algorithms for different types of sensors in WSN based cluster
Published 2018“…In this project three different data aggregation algorithms coding schemes based relative difference (CS-RD), an adaptive method of data aggregation that exploits the spatial correlation between the sensor nodes (ADAM) and coding schemes based the factor of precision (CS-FP) are evaluated. …”
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Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: a review
Published 2015“…Higher power demand in data centers and changes in computing technology together to maximize data center performance has led to deploying multitude methods to estimate power intensity. …”
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Evaluation of Lightning Current Using Inverse Procedure Algorithm.
Published 2013“…In order to validate the proposed method, a set of measured electromagnetic fields are applied on proposed algorithm and the evaluated currents are compared to the corresponding measured current as well as the results are discussed accordingly. …”
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14
Data depublication using : Hashing algorithm / Naimah Nayan
Published 2019“…The recommendation for future work is to evaluate various type of data and different type of hashing algorithm.…”
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15
Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting
Published 2013“…The results of the aggregation algorithms of NNs ensemble are also compared with the Simple Averaging method. …”
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16
Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm
Published 2020“…To fill in missing data, Nearest Neighbour Method(NNM) and Expectation Maximization(EM) algorithm are the two most widely used methods. …”
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A new ant based rule extraction algorithm for web classification
Published 2011“…Web documents contain enormous number of attributes as compared to other type of data. Ant-Miner algorithm is also still lacking in efficiency, accuracy and rule simplicity because of the local minima problem.Therefore, the Ant-Miner algorithm needs to be improved by taking into consideration of the accuracy and rule simplicity criteria so that it could be used to classify Web documents data sets or any large data sets.The best attribute selection method for Web texts categorization is the combination of correlation-based evaluation with random search as the search method.However, this attribute selection method will not give the best performance in attributes reduction. …”
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Monograph -
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Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu
Published 2014“…The success evaluation of data mining classification algorithms have been realized through the data mining programs Weka and RapidMiner. …”
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Hybrid subjective evaluation method using weighted subsethood - based (WSBA) rule generation algorithm
Published 2013“…The use of fuzzy rules, which were extracted directly from input data through Weighted Subsethood-based (WSBA) Rule Generation Algorithm.WSBA rule generation use the subsethood values to generate the weights which finally produced the fuzzy general rules.The rules generated through the data provided knowledge in developed fuzzy rule The fuzzy rules embedded in the framework of subjective evaluation method showed advantages in generalizing the evaluation of the performance achievement, where the evaluation process can be conducted consistently in producing good evaluation results with the use of the membership set score.The results from the numerical examples are comparable to other fuzzy evaluation methods, even with the use of small rule size.…”
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