Search Results - (( yield prediction using algorithm ) OR ( based optimization approach algorithm ))*

Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method by Marlan Z.M., Jamaludin K.R., Harudin N.

“…Thus, this study proposed an Opposition-based Learning Binary Bat Algorithm as the feature selection technique in the T-method. …”

Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

“…s orthogonal array is used as a variable selection approach in optimizing the predictive model. …”

Optimization of stiffened panel fatigue life by using finite element analysis by Mazlan, Shahan

“…The multi-objective genetic algorithm which selects the design points based on Pareto optimal design combined with the adaptive multi-objective algorithm method which uses an optimal space-filling was shown to be efficient for time limitation and budget. …”
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BCLH2Pro: a novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes by Tuntiwongwat, Thanadol, Thammawiset, Sippawit, Srinophakun, Thongchai Rohitatisha, Ngamcharussrivichai, Chawalit, Sukpancharoen, Somboon

“…A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H2 yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. …”
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Real-time classification improvement of Indonesian sign system letters (SIBI) using K-Nearest Neighbor algorithm by Dhewa, Oktaf Agni, Utama, Safitri Yuliana, Nasuha, Aris, Gunawan, Teddy Surya, Pratama, Gilang Nugraha Putu

“…This research addresses this issue with an improved method incorporating linguistic features and contextual information. A novel approach is introduced to enhance SIBI character predictions using the K-Nearest Neighbor (K-NN) algorithm. …”
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Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique by Al-Sulttani, Ali Omran Muhsin, Ahsan, Amimul, Hanoon, Ammar Nasiri, Rahman, Ataur, Nik Daud, Nik Norsyahariati, Idrus, Syazwani

“…This was achieved by solving an optimization problem using the particle swarm optimization (PSO) algorithm in which the optimal yields were determined by estimating the optimal values of the unknown C and nparameters. …”
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Opposition- based simulated kalman filters and their application in system identification by Kamil Zakwan, Mohd Azmi

“…Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. …”
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SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration by Arshad, U., Taqvi, S.A.A., Buang, A., Awad, A.

“…Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. …”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm by Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.

“…Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. …”

Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm by W., Safiei, Rahman, M. M., M.Y., Ali

“…The optimization process parameters of end milling were obtained using response surface methodology, mathematical models and the MOGA-II approach. …”
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Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

“…Similarly, the model performance was also influenced by the nature of the optimization algorithms. The MLPNN models displayed better predictive performance compared to the RBFNN models. …”
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Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction by Nasser, Abdullah, H.M. Ghanem, Waheed Ali, H.Y. Saad, Abdul-Malik, Hamed Abdul-Qawy, Antar Shaddad, A. Ghaleb, Sanaa A, Mohammed Alduais, Nayef Abdulwahab, Din, Fakhrud, Ghetas, Mohamed

“…To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). …”
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Modeling And Optimization Of Lipase-Catalyzed Synthesis Of Adipate Esters Using Response Surface Methodology And Artificial Neural Network by Langroodi, Naz Chaibakhsh

“…A high percentage yield (>96.0%) using optimum conditions was obtained using a minimum amount of enzyme, which matched well with the predicted values. …”
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Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat by Chia , Yu Huat

“…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. In addition, Coati Optimization algorithm, Particle Swarm Opimisation (PSO) and Bayesian Optimsiation (BO) are integrated to identify optimal parameters and minimize settlement during twin tunnel excavation and GBT with the optimisation algorithm has shown consistent capability identifying the least SS induced by twin tunnels Keyword: …”
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Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms by Kanthasamy, R., Almatrafi, E., Ali, I., Hussain Sait, H., Zwawi, M., Abnisa, F., Choe Peng, L., Victor Ayodele, B.

“…The artificial neural network-based algorithms outperformed the SVM and GPR as indicated by the R2 > 0.9 and low predictive errors. …”
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Artificial Neural Network Algorithms to Predict the Compressive Strength of Fly Ash-Based Geopolymers by Siaw Hui, Teo

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CFD-based optimization of base pressure behavior on suddenly expanded flows at supersonic Mach numbers by Jaimon, Dennis Quadros, Khan, Sher Afghan, Prashanth, T.

“…Furthermore, to assess the right range of conditions for maximizing base pressure, the genetic algorithm (GA), desirability function approach (DFA), and particle swarm optimization (PSO) techniques were implemented. …”
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Optimization of coded signals based on wavelet neural network by Ahmed, Mustafa Sami

“…When compared with other existing methods, WNN yields better PSR, low Mean Square Error (MSE), less noise, range resolution ability and Doppler shift performance than the previous and some traditional algorithms like auto correlation function (ACF) algorithm.…”
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An improved artificial neural network based model for prediction of late onset heart failure by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…The MLP model was used to optimize the predicting algorithm based on the conjugate gradients descent method. …”
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Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging by M. Altharan, Yahya, Shamsudin, Shazarel, Lajis, Mohd Amri, Sami Al-Alimi, Sami Al-Alimi, Yusuf, Nur Kamilah Y, Mohammed Alduais, Nayef Abdulwahab, Atef M. Ghaleb, Atef M. Ghaleb, Wenbin Zhou, Wenbin Zhou

“…RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. …”
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