Search Results - (( learner estimation using algorithm ) OR ( java application optimisation algorithm ))
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1
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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2
Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score
Published 2021“…The methodology is proposed as stacking ensemble ML and the best ML algorithms are used as a base learner to compute relative feature weights. …”
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3
Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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4
Development of predictive modeling and deep learning classification of taxi trip tolls
Published 2022“…In this work, let’s use the classification learner to create classification models, compare their performance, and export the findings for additional study. …”
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DEVELOPMENT OF PREDICTIVE MODELING AND DEEP LEARNING CLASSIFICATION OF TAXI TRIP TOLLS
Published 2023“…In this work, let�s use the classification learner to create classification models, compare their performance, and export the findings for additional study. …”
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6
Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…As for the NNE, a novel meta-learner based on the stochastic-enabled extreme learning machine integrated with whale optimisation algorithm (WOA-ELM) was developed and used in such an application for the first time. …”
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A direct ensemble classifier for learning imbalanced multiclass data
Published 2013“…The learning framework consists of ensemble learning and decision combiner model with general supervised learning algorithms as base learner. Feature selection is also applied in DECIML in order to increase the performance of the ensemble learning. …”
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