Search Results - (( programming language mining algorithm ) OR ( java application optimization algorithm ))
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Using language-based search in mining large software repositories
Published 2011“…For the purpose of automating the data retrieval from the repository, a parser was written using the Python programming language, and based on the pattern matching algorithm. …”
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Proceeding Paper -
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Using language-based search in mining large software repositories
Published 2011“…For the purpose of automating the data retrieval from the repository, a parser was written using the Python programming language, and based on the pattern matching algorithm. …”
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Article -
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Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak
Published 2021“…Sentiment Analysis refers to natural language processing and text mining approaches to classify thoughts or sentiments from the tweet. …”
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Thesis -
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Final Year Project -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Article -
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Ant colony optimization algorithm for load balancing in grid computing
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Monograph -
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An efficient and effective case classification method based on slicing
Published 2006“…This paper introduces a new classification method based on slicing techniques that was proposed for procedural programming languages. The paper also discusses two of common classification algorithms that are used either in data mining or in general AI. …”
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Predicting students’ STEM academic performance in Malaysian secondary schools using educational data mining
Published 2023“…Four different data mining classification algorithms which are Random Forest, PART, J48 and Naive Bayes will be used on the dataset. …”
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Thesis -
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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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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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Thesis -
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Web page design for electronic commerce / Lee Fong Wai
Published 2003“…The sixth part covers the system implementation that involved the transformation of modules and algorithm into implementable commands by using the specified programming languages. …”
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Thesis -
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The discovery of Top-K DNA frequent patterns with approximate method / Nittaya Kerdprasop and Kittisak Kerdprasop
Published 2014“…These representatives are subsequently used in the main process of frequent pattern mining. Our designed algorithm had been implemented with the Erlang language, which is the functional programming paradigm with inherent support for pattern matching. …”
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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