Search Results - (( yield prediction model algorithm ) OR ( wave optimization ((bat algorithm) OR (_ algorithm)) ))*
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Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction
Published 2021“…Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. …”
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Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems
Published 2024“…The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. …”
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Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS
Published 2024“…And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). …”
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Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Published 2024“…These results suggest that the new T-method prediction model is better in predicting the output even when only 4 features incorporated in the model…”
Conference Paper -
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Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester
Published 2010“…A multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model. …”
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Hybrid indoor positioning utilizing multipath- assisted fingerprint and geometric estimation for single base station systems
Published 2025“…The results show that Coarse K-Nearest Neighbours is the optimal classifier that predicts MS regions at an exceptionally high accuracy, while Support Vector Machine Kernel is the optimal classifier that perfectly predicts the reflection surfaces. …”
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Thesis -
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elopment of Neural Network Model for Predicting Crucial Product Properties or Yield for Optimisation of Refinery Operation
Published 2005“…The objectives of this project are to develop a framework for the application of neural network modeling in predicting refinery product yield and properties, to develop neural network model for three case studies (predicting crude distillation yield, diesel pour point and hydrocracker total gasoline yield) and to evaluate the suitability of using neural networkmodelingfor predicting refinery product yield and properties. …”
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Final Year Project -
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow
Published 2023“…Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. …”
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Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation
Published 2011“…Therefore the use of genetic algorithm (GA) optimization technique which is one of nonlinear optimization methods is an appropriate choice to solve surface wave inversion problem having high nonlinearity and multimodality. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models
Published 2019“…This research presents the development of a GA and SW as a variables selection method in ANN and NARX models for predicting oil palm yield and output energy. …”
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Predicting customer churn in telecommunication service provider industry using Random Forest / Wan Muhammad Naqib Zafran Wan Roslan
Published 2023“…This project addresses the challenge of customer churn in the Telecommunications Service Provider (TSP) industry by focusing on the Random Forest algorithm for predictive modeling. This study aims to throughly explore the Random Forest algorithm, develop a robust Random Forest customer churn predictive model, and evaluate its performance in predicting customer churn within the internet service provider sector. …”
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Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
Published 2022“…The esterification reaction conditions predicted by ANN showed to be potential for modeling and predicting FAME yield with an extremely well precision of 97.06%.…”
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An Intelligent Hybrid Model Using CNN and RNN for Crop Yield Prediction
Published 2023“…In this study, an intelligent hybrid model using CNN and RNN for crop yield prediction is proposed. …”
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Final Year Project Report / IMRAD -
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Optimizing the efficiency of Oscillating Water Column (OWC) wave energy converter using genetic algorithm
Published 2015“…This paper, describes a method to maximize the pneumatic system efficiency using optimization technique based on Genetic algorithm. This method involves an extraction of maximum incident wave energy corresponding to the wave height, determining of the best deep water length and maximizing the applied damping ratio which can lead to an increase in the pneumatic system efficiency. …”
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Bat algorithm and neural network for monthly streamflow prediction
Published 2023“…Therefore, it can be concluded that, proposed hybrid model yields better performances as compared to BPNN model for monthly streamflow prediction. � 2018 Author(s).…”
Conference Paper -
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Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture
Published 2022“…Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. …”
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Proceedings -
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Development of genetic algorithm for optimization of yield models in oil palm production
Published 2018“…Across the optimization, procedures obtained the best Two Factor Interaction (2FI) models to achieve the best model of oil palm productivity prediction with a value of R2 of 0.948, mean squared error of 0.022, and the model P-value of < 0.0001 in Sabah. …”
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