Search Results - (( model validation means algorithm ) OR ( based integration based algorithm ))*

A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

Hub Angle Control for A Single Link Flexible Manipulator Based on Cuckoo Search Algorithm by aiman Azrael, Shaiful Nahar Sukri, Siti Sarah Zahidah, Nazri, Muhamad Sukri, Hadi, Annisa, Jamali, Hanim, Mohd Yatim, Intan Zaurah, Mat Darus

“…Then, the performance of proposed algorithms was validated based on three robustness methods known as mean squared error, pole zero diagram stability and correlation tests. …”
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Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

Neural network algorithm-based fall detection modelling by Mohd Yusoff, Ainul Husna, Koh, Cheng Zhi, Ngadimon, Khairulnizam, Md Salleh, Salihatun

“…However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. …”
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Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM by Ahmed Alsarori, Ahmed Mohammed, Mohd Herwan, Sulaiman

“…The EMA-based model achieved superior results, with a Mean Absolute Error (MAE) of 0.018, Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.024, and a coefficient of determination (R²) of 0.98 for risk prediction. …”
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

“…In conclusion, SURE(IFGLS)-Autometrics and SURE(EM)-Autometrics can be used as models selection algorithms. Additionally, both algorithms are suitable in improving performance of automated models selection procedures. …”
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Optimized speed controller for induction motor drive using quantum lightning search algorithm by Ali J.A., Hannan M.A., Mohamed A.

Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim by Mohamad Kasim, Adibah I’zzah

“…A simulation-based methodology was adopted, leveraging MATLABO/Simulink to model a power grid and generate synthetic PQD waveforms. …”
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ANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluation by Hannan M.A., Abdolrasol M.G., Mohamed R., Al-Shetwi A., Ker P., Begum R., Muttaqi K.

Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”

Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models by Mohd Aris, M.N., Daud, H., Mohd Noh, K.A., Dass, S.C.

“…Current practice for processing marine CSEM responses utilizes meshes-based algorithms. The ad hoc algorithms require high computational time to solve the integrals and linear equations. …”
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A Quantum Lightning Search Algorithm-Based Fuzzy Speed Controller for Induction Motor Drive by Hannan M.A., Ali J.A., Hussain A., Hasim F.H., Amirulddin U.A.U., Uddin M.N., Blaabjerg F.

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. …”
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Development of soft computing prediction model for the influent physicochemical characteristics of sewage treatment plants / Mozafar Ansari by Mozafar , Ansari

“…Sugeno fuzzy inference system (FIS) algorithm was used to model influent parameter, and the FIS parameters were adjusted by ANFIS, integrated Genetic algorithms, GA-FIS, and integrated particle swarm optimisation, PSO-FIS, algorithms. …”
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Quaternion-based dynamic algorithm for random generation of solid 4D cylindrical curves in RVE modeling by Hamat, Sanusi, Ishak, Mohamad Ridzwan, Kelly, Piaras, Salit, Mohd Sapuan, Yidris, Noorfaizal, Ali, Syamir Alihan Showkat, Hussin, Mohd Sabri, Mohd Dawi, Mohd Syedi Imran

“…A quaternion‐based dynamic algorithm is developed to populate Representative Volume Elements (RVEs) with solid 4D cylindrical fibers, combining spatial centerline coordinates (x,y,z) and quaternion‐encoded orientation. …”
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An improved design for cellular manufacturing system associating scheduling decisions by Subhaa, R., Jawahar, N., Ponnambalam, S. G.

“…The proposed model belongs to the class of NP-hard problems. A hybrid heuristic (HH) that has “Simulated Annealing Algorithm (SAA) embedded with Genetic Algorithm (GA)” is proposed. …”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region by Anees, Shoaib Ahmad, Mehmood, Kaleem, Khan, Waseem Razzaq, Sajjad, Muhammad, Alahmadi, Tahani Awad, Alharbi, Sulaiman Ali, Luo, Mi

“…This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Intelligent algorithm based modeling of renewable and green energy resources for microgrid optimization by Khamis, Alias

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An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications by MOhammed Alduais, Nayef Abdulwahab

“…EDCD aims to save the energy consumption of the IoT sensor board with multiple sensors by means of reducing the number of transmission packets, if no significant change is reported by the payload sensing block; second is proposed a validity of the measuring sensor reading at node level (VSNL) algorithm. …”
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