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Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Published 2014“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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Conference or Workshop Item -
2
Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
Published 2013“…Hence, the developed Lambda-Max ANFIS can be used for forecasting purposes with high prediction accuracy and the system can be used as an alternative to laboratory prediction on the physical properties of degradable plastics. …”
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Thesis -
3
M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589)
“…Hence, the WeSuSFTS algorithm succeeds to improve the MH-ELD forecasting accuracy.…”
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Monograph -
4
Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting
Published 2021“…The WeSuSFTS algorithm uses the min-max operator for fuzzy reasoning and average rule defuzzification which make the process simpler. …”
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Thesis -
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis
