Search Results - (( developing navigation mining algorithm ) OR ( java optimization techniques algorithm ))
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A Web-Based Recommendation System To Predict User Movements Through Web Usage Mining
Published 2009“…The approach in the offline phase is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining. …”
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A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.
Published 2009“…In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. …”
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A new classification model for online predicting users' future movements
Published 2008“…The WUM can model user behavior and, therefore, to forecast their future movements by mining user navigation patterns. To provide online prediction efficiently, we advance architecture for online predicting in web usage mining system by proposing novel model based on Longest Common Subsequence algorithm for classifying user navigation patterns. …”
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WebPUM : a web-based recommendation system to predict user future movements.
Published 2010“…The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. …”
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Organizational Culture Automated Audit System (OCAAS)
Published 2017“…Several state of the art technologies and techniques were used to design and developed OCAAS which include the use of machine learning and sentiment analysis based novel opinion mining algorithms for electronic opinion analysis and computerized statistics based mathematical algorithms for electronic data analysis as well as MySQL database integration for faster data processing and cognitive ergonomics system interface for user friendly interface navigation.…”
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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT
Published 2006“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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Hybrid genetic algorithm for improving fault localization
Published 2018“…Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023Conference paper -
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Examination timetabling using genetic algorithm case study: KUiTTHO
Published 2005“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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Scalable approach for mining association rules from structured XML data
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Recently, various techniques based on different algorithms have been developed. …”
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Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO
Published 2005“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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Improving Class Timetabling using Genetic Algorithm
Published 2006“…We have targeted the research on class timetabling problem. Hence, Genetic Algorithm (GA) is used as one of the most popular optimization solutions. …”
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An enhancement of multi-factor weighted approach technique in prioritizing test cases by comparing similarity distance
Published 2025“…Therefore, numerous factors and techniques have been used to optimize the prioritization process. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study
Published 2019“…After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). …”
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