Search Results - (( java data optimization algorithm ) OR ( build construction learning algorithm ))
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Undergraduates Project Papers -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Automated system for concrete damage classification identification using various classification techniques in machine learning / Nur Haziqah Mat ... [et al.]
Published 2021“…The demand of experienced inspectors also presents a challenge for the pressing lack of highly skilled and experienced construction inspectors. To overcome the issues, datasets of reinforced concrete damage images are intelligently trained and classified by selected Machine Learning algorithms such as Naïve- Bayesian, Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine. …”
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Conference or Workshop Item -
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A random search based effective algorithm for pairwise test data generation
Published 2011“…This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. …”
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Conference or Workshop Item -
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Scalable approach for mining association rules from structured XML data
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Conference or Workshop Item -
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EasyA: Easy and effective way to generate pairwise test data
Published 2013“…This paper proposes a matrix based calculation for pairwise test data generation algorithm named EasyA to optimize the number of test cases. …”
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Conference or Workshop Item -
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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Thesis -
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Machine learning for data classification in construction project planning
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Final Year Project / Dissertation / Thesis -
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Automatic email classification system / Phang Siew Ting
Published 2003“…For this purpose, several Machine Learning algorithms has been purposed to automate the classification of emails. …”
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Thesis -
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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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Thesis -
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
Published 2022“…Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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Thesis -
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Productivity monitoring in building construction projects: a systematic review
Published 2021“…Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. …”
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Article -
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Productivity monitoring in building construction projects: a systematic review
Published 2021“…Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. …”
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Productivity monitoring in building construction projects: a systematic review
Published 2022“…Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. …”
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Productivity monitoring in building construction projects: a systematic review
Published 2022“…Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. …”
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Article -
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Productivity monitoring in building construction projects: a systematic review
Published 2022“…Findings: A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. …”
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An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load
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Conference or Workshop Item -
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A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
Published 2023“…However, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. …”
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