Search Results - (( simulation estimation method algorithm ) OR ( parameter machine learning algorithm ))

Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

“…Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

“…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”

Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…The detailed parametric analysis exhibits the competency of the proposed algorithm to explain the rheological features. Monte-Carlo simulation is performed by propagating uncertainty to investigate the dominant parameters affecting simulated results. …”
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Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques by Anifowose, Fatai Adesina

“…The algorithms were implemented with optimized tuning parameters and validated with real-life porosity and permeability datasets obtained from diverse and heterogeneous petroleum reservoirs after they have passed on testing them with a benchmark dataset from the UCI Machine Learning Repository. …”
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Large-scale kinetic parameters estimation of metabolic model of escherichia coli by Azrag, M. A. K., Tuty Asmawaty, Abdul Kadir, Kabir, M. Nomani, Jaber, Aqeel S.

“…In this work, the PSO algorithm has been adopted to estimate the kinetic parameters by minimizing the errors of the large-scale of metabolic model response of E. coli with reference to real experimental data. …”
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Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

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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.

“…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”

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

“…Time series data, with its sequential dependencies presents a unique challenge for traditional machine learning methods such as Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT), which often struggle to capture temporal patterns effectively. …”
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The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Ahmad A., Abdullah F., Ahmad W.S.H.M.W.

Subjects: “…machine learning…”

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…Machine learning algorithms have widely been adopted recently to enhance the performance of IDSs. …”
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Modeling the effect of process parameters on CO2 methanation using machine learning algorithms by Azhar, Nabilah Husna

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Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining by Sivarao, Subramonian

“…The researchers conducted the prediction of laser machining quality, namely surface roughness with seven significant parameters to obtain singleton output using machine learning techniques based on Quick Back Propagation Algorithm. …”
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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.

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Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach / Muhammad Asyraf Janai by Muhammad Asyraf , Janai

“…The result of our experiment shows that Random Forest, a machine learning algorithm, produces the best fit to our dataset and hence able to make the most accurate prediction of device efficiency. …”
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Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review by Goh, Choon-Hian, Ferdowsi, Mahbuba, Gan, Ming Hong, Kwan, Ban-How, Lim, Wei Yin, Tee, Yee Kai, Rosli, Roshaslina, Tan, Maw Pin *

“…Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. …”
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Water Quality Parameter Prediction Using Machine Learning Method by Muhammad Shaiful Bin Abdul Halim

Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market by Hazirah Halul

“…Therefore, this study focused to contribute on evaluating different algorithm models such as traditional ML and deep learning models with big stock data of multiple parameters from selected companies in Bursa Malaysia. …”