Search Results - (( waste prediction based algorithm ) OR ( java implementation learning algorithm ))
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Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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Design of smart waste bin and prediction algorithm for waste management in household area
Published 2018“…This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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ESS-IoT: The Smart Waste Management System for General Household
Published 2024“…On the other hand, the waste classification is implemented using two classification algorithms: Random Forest (RF) prediction model and Convolutional Neural Network (CNN) prediction model. …”
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Characterization of dumping soil and settlement prediction using Monte Carlo approach
Published 2013“…Dumping soil are characterize based on its characteristics such as Category I:soil like and non soil like, Category II: waste types and Category III: waste or soil. …”
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Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
Published 2023“…The artificial neural network-based algorithms outperformed the SVM and GPR as indicated by the R2 > 0.9 and low predictive errors. …”
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Neural network prediction for efficient waste management in Malaysia
Published 2018“…This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. …”
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Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations
Published 2022“…All the input variables significantly influence the predicted biohydrogen. However, waste glycerol has the most significant effects. …”
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Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach
Published 2023“…This research compares 3 algorithms for predicting the amount of waste trapped by GPT: Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression. …”
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Smart Routing For Solid Waste Collection
Published 2023“…The proposed solution is to implement a route optimization algorithm to predict the probability of each feasible route for the garbage truck collection. …”
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Final Year Project Report / IMRAD -
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods
Published 2023“…The study conducts an extensive investigation using ML algorithms to accurately predict the multiple stress creep recovery (MSCR) rutting parameters for the base and modified asphalt binders. …”
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