Search Results - Hyper-parameter based optimization

LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar

Get full text
Get full text
Get full text

Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines by Zuriani, Mustaffa, M. H., Sulaiman

“…In this regard, this study proposes a hybridization of LSSVM with a new Swarm Intelligence (SI) algorithm namely, Grey Wolf Optimizer (GWO). With such hybridization, the hyper-parameters of interest are automatically optimized by the GWO. …”
Get full text
Get full text
Get full text

Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines by Zuriani, Mustaffa, Mohd Herwan, Sulaiman

Get full text
Get full text
Get full text

Application of LSSVM by ABC in energy commodity price forecasting by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

“…The importance of the hyper parameters selection for a kernel-based algorithm, viz.Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature.In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM.The mABC contributes in the exploitation process of the artificial bees and is based on Levy mutation.Realized in crude oil price forecasting, the performance of mABC-LSSVM is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSPE) and compared against the standard ABC-LSSVM and LSSVM optimized by Genetic Algorithm. …”
Get full text
Get full text
Get full text

Training LSSVM with GWO for Price Forecasting by Zuriani, Mustaffa, M. H., Sulaiman, M. N. M., Kahar

“…In this study, a great deal of attention was paid in determining LSSVM’s hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said hyper parameters. …”
Get full text
Get full text
Get full text
Get full text

Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data by Mezaal, Al-Karawi Mustafa Ridha, Pradhan, Biswajeet, Sameen, Maher Ibrahim, Mohd Shafri, Helmi Zulhaidi, Md Yusoff, Zainuddin

“…The hyper-parameters of network architecture were defined based on a systematic grid search. …”
Get full text
Get full text
Get full text

Either crop or pad the input volume: What is beneficial for Convolutional Neural Network? by Al-Saggaf, U.M., Botalb, A., Moinuddin, M., Alfakeh, S.A., Ali, S.S.A., Boon, T.T.

“…What makes the CNN's huge hyper-parameters space optimization harder is that there is no universal robust theory supporting it, and any work flow proposed so far in literature is based on heuristics that are just rules of thumb and only depend on the dataset and problem at hand. …”
Get full text
Get full text

Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification by Adamu, Shamsuddeen, Alhussian, Hitham, Aziz, Norshakirah, Abdulkadir, Said Jadid, Alwadin, Ayed, Abdullahi, Mujaheed, Garba, Aliyu

Get full text
Get full text

Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process by Chong K.L., Huang Y.F., Koo C.H., Sherif M., Ahmed A.N., El-Shafie A.

Subjects:

Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Muttaqi K.M.

A review on optimization of least squares support vector machine for time series forecasting by Yusof, Yuhanis, Mustaffa, Zuriani

“…In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.…”
Get full text
Get full text
Get full text

Open phase fault-tolerant support vector machine predictive power control for six-phase induction generator WECS by Hamoudi Y., Abdolrasol M.G.M., Amimeur H., Hassaini F., Ker P.J., Ustun T.S.

Lévy mutation in artificial bee colony algorithm for gasoline price prediction by Mustaffa, Zuriani, Yusof, Yuhanis

“…The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price.…”
Get full text
Get full text
Get full text

Forecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenarios by Ibrahim K.S.M.H., Huang Y.F., Ahmed A.N., Koo C.H., El-Shafie A.

Gasoline price forecasting: An application of LSSVM with improved ABC by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

“…Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.…”
Get full text
Get full text
Get full text

Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine by Ahmed, Marzia, Mohd Herwan, Sulaiman, Hassan, Md Maruf, Rahaman, Md Atikur, Mohammad, Amin

“…This study presents a novel approach for optimal allocation forecasting of energy consumption in a power distribution company, utilizing the Least Squares Support Vector Machine (LSSVM) optimized by novel variants of the Barnacle Mating Optimizer (BMO) such as the new Gooseneck Barnacle Optimizer and Selective Opposition-based constrained BMO. …”
Get full text
Get full text
Get full text

Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
Get full text
Get full text
Get full text

Evolving spiking neural network: A comprehensive survey of its variants and their results by IBAD, T., KADIR, S.J.A., AZIZ, N.B.A.

“…The review paper is summed up by giving a conclusion of the optimized eSNN model's fundamentals and providing thinkable future directions that can be explored in the current works on the Hyper-parameter optimization of eSNN. …”
Get full text
Get full text

WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE by Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Teng, Yeow Haur

“…Hyper-parameter tuning was conducted to determine the optimal configuration of WONN using the GFDL-CM3 Global Circulation Model (GCM) under the RCP4.5 scenario. …”
Get full text
Get full text
Get full text

Forecasting model based on LSSVM and ABC for natural resource commodity by Yusof, Yuhanis, Kamaruddin, Siti Sakira, Husni, Husniza, Ku-Mahamud, Ku Ruhana, Mustaffa, Zuriani

“…Reliable forecast of the price of natural resource commodity is of interest for a wide range of applications.This includes generating macroeconomic projections and in assessing macroeconomic risks.Various approaches have been introduced in developing the required forecasting models.In this paper, a forecasting model based on an optimized Least Squares Support Vector Machine is proposed.The determination of hyper-parameters is performed using a nature inspired algorithm, Artificial Bee Colony.The proposed forecasting model is realized is gold price forecasting.The undertaken experiments showed that the prediction accuracy and Mean Absolute Percentage Error produced by the proposed model is better compared on the one produced using Least Squares Support Vector Machine as an individual.…”
Get full text
Get full text
Get full text