Search Results - (( developing forecasting _ algorithm ) OR ( java optimization path algorithm ))

Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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SecPath: Energy efficient path reconstruction in wireless sensor network using iterative smoothing by Abd, Wamidh Jwdat

“…This work uses iterative smoothing algorithm to find an alternative path with less distance and energy consumption. …”
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Energy efficient path reconstruction in wireless sensor network using iPath by Hasan, Sazlinah, Abd, Wamidh Jwdat, Ariffin, Ahmad Alauddin

“…This work uses iterative boosting algorithm to find an alternative path with less distance and energy consumption. …”
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Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

“…Core functionalities such as path planning, autonomous movement, voice feedback, and app-to-robot communication have been thoroughly tested and optimized. …”
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Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

“…Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) were used.The algorithm was developed in JAVA using up to date forecasting process such as data partition, several error measures and rolling process.Successfully, the results of the algorithm tally with the results of SPSS and Excel.This automatic forecasting will not just benefit forecaster but also end users who do not have in depth knowledge about forecasting techniques.…”
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Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting by HASSAN, SAIMA

“…The aggregation algorithms were employed on the forecasts obtained from all individual NN models as well as on a number of the best forecasts obtained from the best NN models. …”
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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach by Pourdaryaei, Alireza, Mokhlis, Hazlie, Illias, Hazlee Azil, Kaboli, S. Hr. Aghay, Ahmad, Shameem

“…Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. …”
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Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting by Afan H.A., Allawi M.F., El-Shafie A., Yaseen Z.M., Ahmed A.N., Malek M.A., Koting S.B., Salih S.Q., Mohtar W.H.M.W., Lai S.H., Sefelnasr A., Sherif M., El-Shafie A.

Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

“…The results demonstrated the superiority of the IABO-trained algorithm in avoiding local minima, convergence speed, and accuracy compared to the benchmarking (BP and PSO) algorithms in water level forecasting tasks.…”
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M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589) by Mansor, Rosnalini, Mat Kasim, Maznah, Othman, Mahmod, Zaini, Bahtiar Jamili

“…Hence, the WeSuSFTS algorithm succeeds to improve the MH-ELD forecasting accuracy.…”
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Box-jenkins and genetic algorithm hybrid model for electricity forecasting system by Mahpol, Khairil Asmani

“…The investigation is simulated using Intelligent Electricity Forecasting System (IEFS) developed in this research which written in Borland Delphi 7.0 programming.…”
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Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow by Osman A., Afan H.A., Allawi M.F., Jaafar O., Noureldin A., Hamzah F.M., Ahmed A.N., El-shafie A.

Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm by Haruna, Chiroma, Khan, Abdullah, Abubakar, Adamu, Saudi, Younes, Hamza, Mukhtar Fatihu, Shuiba, Liyana, Gital, Abdulsalam, Herawan, Tutut

“…Many meta-heuristic algorithms have been proposed in literature for the optimization of Neural Network (NN) to build a forecasting model. …”
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Multi-horizon ternary time series forecasting by Htike@Muhammad Yusof, Zaw Zaw

“…This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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Development of hybrid algorithm of residual bootstrap artificial neural network based on sukuk volatility forecast model. by Nurul Hila Zainuddin

“…Development of hybrid algorithm of residual bootstrap artificial neural network based on sukuk volatility forecast model. by Nurul Hila Zainuddin…”
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Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm by Tikhamarine Y., Souag-Gamane D., Najah Ahmed A., Kisi O., El-Shafie A.

Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting by Rosnalini, Mansor

“…Besides, different characteristics of each moving holiday and existence of a great number of irregularities in the load data also contribute to the forecasting inaccuracy and uncertainty. Fuzzy time series (FTS) algorithm is able to overcome moving holiday electricity load demand (MH-ELD) forecasting problem, but the FTS algorithm lacks final model interpretation, less interpretability of fuzzy logical relationship strength, and does not provide a complete FTS forecasting process. …”
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Long term energy demand forecasting based on hybrid, optimization: Comparative study by Musa, Wahab, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization.To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence.Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results.Averages error between two models was adopt to select the proper model for future projection of energy demand.…”
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