Search Results - (( java implication based algorithm ) OR ( based voting ((means algorithm) OR (path algorithm)) ))
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Re-engineering grid-based quorum replication into binary vote assignment on cloud: A scalable approach for strong consistency in cloud databases
Published 2025“…This study proposes the Binary Vote Assignment in Cloud (BVAC), a cloud-native replication algorithm re-engineered from the Binary Vote Assignment on Grid Quorum (BVAGQ). …”
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Dense-cluster based voting approach for license plate identification
Published 2018“…This paper presents a new method called Dense Cluster based Voting (DCV) for identifying an input license plate image as normal or taxi such that suitable recognition algorithms can be used to achieve better recognition rate. …”
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Ant system-based feature set partitioning algorithm for classifier ensemble construction
Published 2016“…In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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An improved multiple classifier combination scheme for pattern classification
Published 2015“…Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. …”
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TOPSIS-based Regression Algorithms Evaluation
Published 2022“…Following that, using three datasets, namely Combined Cycle Power Plant, Real Estate, and Concrete, Voting using multiple classifiers (k-means-based classifiers) was the top-ranked in the Combined Cycle Power Plant and Real Estate datasets. …”
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Smart thermal comfort system: a development of the fundamental control algorithm
Published 2004“…This paper reports on the development of the fundamental algorithm for a smart thermal comfort system. Using Predictive Mean Vote (PMV) as a means of measuring thermal comfort, this system would able the user to define their own expression towards the surroundings, from slightly warm to slightly cold. …”
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Smart thermal comfort system: a development of the fundamental control algorithm
Published 2004“…This paper reports on the development of the fundamental algorithm for a smart thermal comfort system. Using Predictive Mean Vote (PMV) as a means of measuring thermal comfort, this system would able the user to define their own expression towards the surroundings, from slightly warm to slightly cold. …”
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The new efficient and accurate attribute-oriented clustering algorithms for categorical data
Published 2012“…This work firstly reveals the significance of attributes in categorical data clustering, and then investigates the limitations of algorithms MMR and G-ANMI respectively, and correspondingly proposes a new attribute-oriented hierarchical divisive clustering algorithm termed Mean Gain Ratio (MGR) and an improved genetic clustering algorithm termed Improved G-ANMI (IG-ANMI) for categorical data. …”
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Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization
Published 2018“…The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. …”
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RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD
Published 2023“…To evaluate indoor thermal comfort situations, predicted mean vote (PMV) and predicted percentage of dissatisfaction (PPD) indicators were used. …”
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2D and 3D video scene text classification
Published 2014“…A voting method is finally proposed to clasify each text block as either 2D or 3D by counting the text representatives that satisfy MNNS. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization
Published 2023“…Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. …”
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Prediction on the mechanical strength of coal ash concrete using artificial neural network
Published 2022“…Model performance was evaluated using several well-known metrics, including R2, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) (R-square). …”
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