Search Results - (( error prediction using algorithm ) OR ( java application optimisation algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…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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Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

“…In some cases, the prediction errors of Taguchi ANN model was larger than 10 even with Levenberg Marquardt training algorithm. …”
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Artificial neural network model for predicting windstorm intensity and the potential damages / Mohd Fatruz Bachok by Bachok, Mohd Fatruz

“…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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Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction by Musa, Wahab, Ku Mahamud, Ku Ruhana, Salim, Sardi, Sediyono, Agung

“…This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. …”
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A Mobile Application For Stock Price Prediction by Choy, Yi Tou

“…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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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

“…Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. …”
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Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms by Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan

“…This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and support vector regressor (SVR). …”
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A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction by Yousif S.T., Ismail F.B., Al-Bazi A.

“…The PSO adopts a unique random number selection strategy, incorporating the K-Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. …”

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms by Rathakrishnan V., Bt. Beddu S., Ahmed A.N.

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

“…The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. …”

Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms by Chia, See Leng

“…The performance was benchmarked using root mean squared error (RMSE), mean absolute error (MAE), Coefficient of Determination (R2 ), mean absolute percentage error (MAPE) and Global Performance Index (GPI) as well as their time cost. …”
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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

Weather prediction system using ANN algorithm / Nur Afiqah Ahmad Sukri by Ahmad Sukri, Nur Afiqah

“…The ANN model consists of three layers with ReLU and softmax activations and is trained using the backpropagation algorithm. The performance of the model is evaluated using metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), precision, recall, F1-score, and accuracy. …”
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Enhanced long short-term memory with fireworks algorithm and mutation operator by Changqing Gong, Xinyao Wang, Abdullah Gani, Han Qi

“…Aiming at the problems of lower predictive accuracy and slower convergent speed of the existing prediction models, a prediction model based on fireworks algorithm (FWA) and long short-term memory (LSTM) is proposed to predict time-related data. …”
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A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction by Hammid, Ali Thaeer, M. H., Sulaiman, Awad, Omar I.

“…Furthermore, the performance of the suggested robust firefly algorithm model is better than previously mentioned models in terms of speed and accuracy of prediction.…”
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Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion by Nadeem, Khodabacchus Muhamad, Fowdur, Tulsi Pawan

“…This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. …”
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Artificial neural network modeling studies to predict the amount of carried weight by rail transportation system / Nur Syuhada Muhammat Pazil, Siti Nor Nadrah Muhamad and Hanis Sya... by Muhammat Pazil, Nur Syuhada, Muhamad, Siti Nor Nadrah, Nor Azahar, Hanis Syazana

“…The best algorithm is selected to predict the amount of carried weight by comparing the value of error measures of the three algorithms which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). …”
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Application of artificial neural network on the prediction of microbial population and species during spontaneous fermentation of garcinia mangostana pericarp / Mohd Fikri Hakim Ab... by Abdullah, Mohd Fikri Hakim, So’aib, Mohamad Sufian

“…The relative error means the percentage of incorrect predicted data. …”
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Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm by Hussain, Azham, Surendar, A., Clementking, A., Kanagarajan, Sujith, Ilyashenko, Lubov K.

“…Then, the performances of the proposed predicting models are checked using two error indices, namely coefficient correlation (R2) and root mean squared error (RMSE). …”
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