Search Results - (( java implementation mining algorithm ) OR ( based regulation learning algorithm ))
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Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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4
Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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5
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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6
Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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8
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models
Published 2023“…In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. …”
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Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants
Published 2023“…The research elaborates on the design and implementation of machine learning models based on three algorithms: Decision Tree, Gradient Boosted Tree, and Support Vector Machine. …”
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Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure
Published 2004“…However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. …”
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Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…To anticipate occurrences, ML methods such as Decision Trees, Neural Networks, K-Nearest Neighbors, and Impact Learning are being utilized, and their performance is compared based on the data processing and modification used. …”
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A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications
Published 2025“…To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. …”
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Nonlinear dynamic system identification and control via self-regulating modular neural network
Published 2003“…In addition, the Fully Self-Organized Simplified Adaptive Resonance Theory (FOSART) is modified and adopted to generate an induced Delaunay triangulation that is highly desired for optimal function approximation. Self-adaptive learning rates Gradient Descent learning rules are employed in a supervised learning phase. …”
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15
Artificial Intelligence (AI) in the art and design industry / Fahmi Samsudin
Published 2023“…It encompasses different types, such as rule-based AI using if-then statements for decision-making, machine learning which employs algorithms to analyze and learn from data, and deep learning utilizing artificial neural networks to learn from extensive datasets. …”
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
Published 2019“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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Deep learning framework for hierarchical-based object identification and description
Published 2024“…Hence, DL approaches lack of global generalization, hierarchical learning, and correlation learning. In addition, DL-based CV approaches only depend on extracting high-level features which are not semantic to recognize parts or subsets of an object. …”
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18
Base pressure control through micro jets at supersonic Mach numbers using experimental and machine learning approach
Published 2026“…Furthermore, machine learning (ML) algorithms were utilized to predict base pressure outcomes and optimize control strategies. …”
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Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization
Published 2018“…This paper compares mean absolute error, mean square error, and mean absolute percentage error (MAPE) in the PCL biopolymerization process for 11 different training algorithms that belong to six classes, namely (1) additive momentum, (2) self-adaptive learning rate, (3) resilient backpropagation, (4) conjugate gradient backpropagation, (5) quasi-Newton, and (6) Bayesian regulation propagation. …”
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