Search Results - (( parameter optimization method algorithm ) OR ( using vector error algorithm ))

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah

“…The proposed estimation approach simplifies the typical trial-and-error method. During optimization, within 100 iterations, the chewing count is reduced by 12.9% from its first iteration. …”
Get full text
Get full text

Short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine by Yusof, Yuhanis, Ahmad, Farzana Kabir, Kamaruddin, Siti Sakira, Omar, Mohd Hasbullah, Mohamed, Athraa Jasim

“…The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions.This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning.In this study, a new method that integrates Firefly algorithm (FA) with Least Squares Support Vector Machine (LSSVM) is proposed for short term traffic speed forecasting, which is later termed as FA-LSSVM.In particular, the Firefly algorithm which has the advantage in global search is used to optimize the hyper-parameters of LSSVM for efficient data training. …”
Get full text
Get full text
Get full text

A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom

“…The proposed estimation approach simplifies the typical trial-and-error method. During optimization, within 100 iterations, the chewing count is reduced by 12.9% from its first iteration. …”
Get full text
Get full text
Get full text

Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization by Yahya, Zainor Ridzuan

“…For each proposed soft computing technique, parameter tuning is done as an essential study. The sum of squares error (SSE) is used as an objective function. …”
Get full text
Get full text

Committee neural networks with fuzzy genetic algorithm. by Jafari , S.A., Mashohor , Syamsiah, Varnamkhasti, M. Jalali

“…The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions.…”
Get full text
Get full text
Get full text

Eccentricity Optimization of NGB System by using Multi-Objective Genetic Algorithm by Yazdi, H.M., Ramli Sulong, N.H.

“…In this study, a new method for designing a particular braced system by using multi-objective genetic algorithm is proposed. …”
Get full text
Get full text

Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation by Kumar, A., Ridha, S., Ganet, T., Vasant, P., Ilyas, S.U.

“…The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm. © 2020 by the authors.…”
Get full text
Get full text

Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework by Aidil Zulkafli, Nadhirah, Elyca Anak Bundak, Caceja, Amiruddin Abd Rahman, Mohd, Chin Yap, Chi, Chong, Kok-Keong, Tee Tan, Sin

“…This novel study demonstrated that support vector regression (SVR) integrated with the particle swarm optimization (PSO) model is an alternative method to predict the device performance in future organic solar cells.…”
Get full text
Get full text
Get full text

Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method by Olubosede, Olusayo, Abd Rahman, Mohd Amiruddin, Alqahtani, Abdullah, Souiyah, Miloud, Latif, Mouftahou B., Oke, Wasiu Adeyemi, Aldhafferi, Nahier, Owolabi, Taoreed O.

“…This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. …”
Get full text
Get full text

Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu by Abdul Mannan, Dadu

“…The position of reference currents is used to detect the voltage vectors surrounding the reference voltage vector in every sampling period. …”
Get full text
Get full text
Get full text

Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method by Akomolafe, Oluwatobi, Owolabi, Taoreed O., Abd Rahman, Mohd Amiruddin, Awang Kechik, Mohd Mustafa, Mohd Yasin, Mohd Najib, Souiyah, Miloud

“…This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. …”
Get full text
Get full text

An advanced scheme based on Artificial Intelligence technique for solving nonlinear Riccati systems by Admon, Mohd Rashid, Senu, Norazak, Ahmadian, Ali, Abdul Majid, Zanariah

“…This work aims to implement a feedforward ANN with two hidden layers to solve nonlinear systems based on the fractional Riccati differential equation (FRDE). The network parameters are trained using the Adam optimization method with the aid of automatic differentiation. …”
Get full text
Get full text

Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization by Nur Alia Shahira, Mohd Zaidi, Zuriani, Mustaffa, Muhammad Arif, Mohamad

“…The study introduces a hybrid model that integrates TCN with Artificial Fish Swarm Algorithm (AFSA), a bio-inspired optimization technique designed to fine-tune TCN parameters. …”
Get full text
Get full text
Get full text

Hybrid indoor positioning utilizing multipath- assisted fingerprint and geometric estimation for single base station systems by Manap, Zahariah

“…When compared to a simple fingerprinting method, the median error for the hybrid method is observed to be 4.06 cm, which is substantially lower than the 538 cm median error of the fingerprinting method. …”
Get full text
Get full text
Get full text

Industrial implementation of machine learning forecasting algorithms for large-scale Solar Heat for Industrial Process (SHIP) Systems by Manu, Abdurrahman, Haja Mohideen, Ahmad Jazlan, Akhter, Muhammad Naveed, Ahmad Huzri, Muhammad Danial, Mohd Ibrahim, Azhar, Mohd Zaki, Hasan Firdaus, Sreeram, Victor

Get full text
Get full text
Get full text

Multivariate Based Analysis of Methane Adsorption Correlated to Toc and Mineralogy Impact from Different Shale Fabrics by Irfan, S.A., Azli, N.M., Abdulkareem, F.A., Padmanabhan, E.

“…The variation of adsorption on shale fabric has been conducted using different machine learning approaches. The multivariate analysis is carried out using the partial least square (PLS) method and support vector regression (SVR), along with random forest regression method. …”
Get full text
Get full text

Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah

“…To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. …”
Get full text
Get full text
Get full text

Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

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.