Search Results - (( developing prediction means algorithm ) OR ( java application optimisation algorithm ))
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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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A Mobile Application For Stock Price Prediction
Published 2021“…The evaluation methods were Root Mean Square Error and Mean Absolute Error. The results show ARIMA has the least error among all five prediction algorithms. …”
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Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion
Published 2018“…The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.…”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat
Published 2021“…Recognition software achieved 87.14%, EPD algorithm achieved 73.57% and HMT algorithm achieved 74.30%) prediction accuracy with OTs. …”
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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. …”
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Widely linear dynamic quaternion valued least mean square algorithm for linear filtering
Published 2017“…In prediction setting the proposed algorithms showed 4dp to 8dp higher prediction gain than other algorithms. …”
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Published 2014“…Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. …”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms
Published 2021“…All optimisation algorithms in this study are quite competitive with each other in terms of prediction accuracy while RBF kernel is the best kernel type among the kernels in this study.…”
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Weather prediction system using ANN algorithm / Nur Afiqah Ahmad Sukri
Published 2024“…This research gives a thorough investigation into ANN-based weather prediction. The objective of the project is to develop a weather prediction system using artificial neural network (ANN) algorithms and to evaluate its performance and accuracy. …”
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Stock price monitoring system
Published 2024“…Consequently, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of the prediction algorithms. …”
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Computationally efficient single layer transformer convolutional encoder for accurate price prediction of agriculture commodities
Published 2025“…Predictions made with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) show that the STCE algorithm is better than other deep learning algorithms, even the traditional transformer encoder. …”
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Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm
Published 2023“…The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…In conclusion, hybrid DNN with the K-Means Clustering Algorithm is proven to resolve parameter estimations of the chaotic system by developing an accurate prediction model.…”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The significance of the selected input variable vectors is studied to analyze their effects on the prediction process. Another objective of the study is to develop and compare the prediction models for electrical energy demand of one day-ahead. …”
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Artificial neural network model for predicting windstorm intensity and the potential damages / Mohd Fatruz Bachok
Published 2019“…In addition, the mean square error (MSE) values for ANN model algorithms (pattern recognition tool) for 5 prediction processes are low from 0.00 to 0.0286 and errors from 0.00 to 0.0309. …”
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…On the Construction phase, the development of the prototype has been started. All the algorithm for the engine has been developed by using Java script language. …”
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