Search Results - (( java application testing algorithm ) OR ( yield prediction problems algorithm ))
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Rice yield prediction - a comparison between enhanced back propagation learning algorithms
Published 2004“…In this study, we examine the performance of four enhanced BP algorithms to predict rice yield in MADA plantation area in Kedah, Malaysia. …”
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Rice Yield prediction - a comparison between Enchanced Back Propagation Learning Algorithms
Published 2009“…In this study, we examine the performance of four enhanced BP algorithms to predict rice yield in MAD A plantation area in Kedah, Malaysia. …”
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A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
Published 2021“…Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. …”
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Predictive Analytics in Genetic Engineering as an Optimization Problem
Published 2024“…The nature of the problem requires that a stochastic optimization algorithm be applied in the metaheuristic search rather than using a deterministic or mathematical approach. …”
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RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Final Year Project -
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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An Intelligent Hybrid Model Using CNN and RNN for Crop Yield Prediction
Published 2023“…CNN is a popular learning model used in predicting crop yield due to its high performance in feature extraction. …”
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Final Year Project Report / IMRAD -
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique
Published 2017“…The proposed model is based on the determination of the best values for the unknown constant (C) and the exponent (n) for the Nusselt number expression used to formulate the equation for the estimation of the hourly yield of a solar still (HYSS). This was achieved by solving an optimization problem using the particle swarm optimization (PSO) algorithm in which the optimal yields were determined by estimating the optimal values of the unknown C and nparameters. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…Meanwhile, Kmeans clustering algorithm has also been reported has widely known for solving most unsupervised classification problems. …”
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Implementation of (AES) Advanced Encryption Standard algorithm in communication application
Published 2014“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Undergraduates Project Papers -
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Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network
Published 2009“…One such method is machine learning, which involves computer algorithm to capture hidden knowledge from data. In this research, a problem solving scenario for a metal cutting industry which faces some problems in determining the end product quality of Manganese Molybdenum (Mn-Mo) pressure vessel plate is investigated. …”
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A Pioneering Approach to Predicting the Shear Strength of RC Beams by Employing Artificial Intelligence Techniques
Published 2024“…The outcomes demonstrated that both methods exhibited favourable predictive capabilities. Nevertheless, the ANFIS architecture proposed, which incorporates a hybrid learning algorithm, outperformed the multilayer feedforward ANN that utilizes the backpropagation algorithm. …”
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A Pioneering Approach to Predicting the Shear Strength of RC Beams by Employing Artificial Intelligence Techniques
Published 2024“…The outcomes demonstrated that both methods exhibited favourable predictive capabilities. Nevertheless, the ANFIS architecture proposed, which incorporates a hybrid learning algorithm, outperformed the multilayer feedforward ANN that utilizes the backpropagation algorithm. …”
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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…Comparing machine learning algorithms yields insights. The ElasticNet model predicts building damage grade with 92.56 test accuracy and 92.67 train accuracy. …”
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Classification System for Heart Disease Using Bayesian Classifier
Published 2007“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. In this system a Bayesian algorithm was used in order to implement the system. …”
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An improved multilayer perceptron based on wavelet approach for physical time series prediction
Published 2014“…Meanwhile, Neural Networks (NN) has been successfully applied in many problems in the domain of time series prediction. …”
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