Search Results - (( developing forecasting using algorithm ) OR ( java application using algorithm ))

Search alternatives:

Refine Results
  1. 1
    Automated time series forecasting

    Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

    Published 2011
    “…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.…”
    Get full text
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  2. 2
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  3. 3
    Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

    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

    Published 2019
    “…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. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  4. 4
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  5. 5
    Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

    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.

    Published 2023
    Article
    Save to List
    Saved in:
  6. 6
    Box-jenkins and genetic algorithm hybrid model for electricity forecasting system

    Box-jenkins and genetic algorithm hybrid model for electricity forecasting system by Mahpol, Khairil Asmani

    Published 2005
    “…The investigation is simulated using Intelligent Electricity Forecasting System (IEFS) developed in this research which written in Borland Delphi 7.0 programming.…”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

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

    Published 2019
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  8. 8
    A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm

    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

    Published 2016
    “…Many meta-heuristic algorithms have been proposed in literature for the optimization of Neural Network (NN) to build a forecasting model. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  9. 9
    Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting

    Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting by HASSAN, SAIMA

    Published 2013
    “…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. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
    Get full text
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  11. 11
    Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow

    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.

    Published 2023
    Article
    Save to List
    Saved in:
  12. 12
    Balanced Stochastic Realization Algorithm For Development Of Rainfall Model

    Balanced Stochastic Realization Algorithm For Development Of Rainfall Model by Azhari, Fahimy

    Published 2014
    “…In this research, Balanced Stochastic Realization (BSR) subspace algorithm is used to develop a rainfall model for Malaysia. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    Hybrid optimization approach to estimate random demand

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

    Published 2012
    “…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. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  14. 14
    Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

    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.

    Published 2023
    Article
    Save to List
    Saved in:
  15. 15
    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing

    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    “…Real-world optimizations, such as forecasting streamflow, are a complicated process that is highly non-linear and multi-modal, demanding the use of a suitable modeling tool, with an emphasis on artificial intelligence algorithms, to get befitting forecast results. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  16. 16
    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…ANN based STLF models commonly use back-propagation algorithm, which generally exhibits a slow and improper convergence that affects the forecast accuracy. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  17. 17
    M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589)

    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.…”
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  18. 18
    Rainfall-funoff modelling in batang layar and oya sub-catchments using pre-developed ann model for tinjar catchment

    Rainfall-funoff modelling in batang layar and oya sub-catchments using pre-developed ann model for tinjar catchment by Awangku Faizal Sallehin, Awangku Brahim

    Published 2009
    “…Artificial Neural Network (ANN) has been widely used to forecast Rainfall-Runoff relationships. Many ANN has been developed by experts in order to forecast RainfallRunoff relationships in certain catchment. …”
    Get full text
    Get full text
    Get full text
    Final Year Project Report / IMRAD
    Save to List
    Saved in:
  19. 19
    A big data prediction framework for weather forecast using MapReduce algorithm

    A big data prediction framework for weather forecast using MapReduce algorithm by Khalid, Adam, Mazlina, Abdul Majid, Fakherldin, Mohammed Adam Ibrahim, Jasni, Mohamad Zain

    Published 2017
    “…The process of weather forecasting is developing as the effect of advancement in technology right from the realization of increasing size of data, Weather forecasting was found to be based on big data. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  20. 20
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    “…Three deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are used to develop the prediction model. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: