Search Results - (( model validation cell algorithm ) OR ( using optimization method algorithm ))*

An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

“…Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. …”
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Characterization of PV panel and global optimization of its model parameters using genetic algorithm by Ismail M.S., Moghavvemi M., Mahlia T.M.I.

“…The Manufacturer's Data Sheet information is used as a basis for the purpose of parameter optimization, with an average absolute error fitness function formulated; and a numerical iterative method used to solve the voltage-current relation of the PV module. …”

Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy by Prakas Gopal , Samy

“…Leveraging state-of-the-art Multi-Objective Optimization algorithms, the research delves into the intricate dynamics of tumour and effector cell interactions within the context of chemotherapy. …”
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Application of system identification method coupled with evolutionary algorithms for the optimization of power consumption in a pem fuel cell propulsion system / Suhadiyana Hanapi by Hanapi, Suhadiyana

“…Secondly, in this research, an empirical dynamic model of the PEM fuel cell system in vehicles was analyzed and validated using the system identification method……”
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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. …”

Parameter extraction of photovoltaic module using hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T.

Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…The model reliability describes the second sub-objective, which is to determine the feasible operating window of the MEC using multiple-objective optimization based on the nonlinear convex method using gradient descent algorithm as the objective function in maximizing the percentage efficiency of the MEC after validating the mathematical model. …”
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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.

Inter-cell and intra-cell facility layout models under different demand environments in cellular manufacturing systems by Ariafar, Shahram

“…In order to validate the models several cases are generated and demonstrated by two methods, Lingo 12.0 optimization software and an enumeration algorithm which is developed in C/C++. …”
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PREDICTIVE MAXIMUM POWER POINT TRACKING (MPPT) ALGORITHM FOR PERMANENT EXCHANGE MEMBRANE FUEL CELL (PEMFC) by MOHD RIZZWAN, MINGGU

“…The focus of this project is to show the power characteristics extracted from the PEMFC system by using predictive control. A comparison of PEMFC performances based on the proposed technique with other existing MPPT algorithms will be done to validate the algorithm performance. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

Intelligent algorithm based modeling of renewable and green energy resources for microgrid optimization by Khamis, Alias

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A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout by Mohd Daud, Kauthar, Mohamad, Mohd Saberi, Zakaria, Zalmiyah, Hassan, Rohayanti, Ali Shah, Zuraini, Deris, Safa'ai, Ibrahim, Zuwairie, Napis, Suhaimi, Sinnott, Richard O.

“…The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. …”
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A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout by Kauthar, Mohd Daud, Mohd Saberi, Mohamad, Zalmiyah, Zakaria, Rohayanti, Hassan, Zuraini, Ali Shah, Safaai, Deris, Zuwairie, Ibrahim, Suhaimi, Napis, Sinnott, Richard O.

“…The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. …”
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Comparison of optimization-modelling methods for metabolites production in escherichia coli by Lee, M. K., Mohd Saberi, Mohamad, Choon, Yee Wen, Kauthar, Mohd Daud, Nurul Athirah, Nasarudin, Mohd Arfian, Ismail, Zuwairie, Ibrahim, Suhaimi, Napis, Sinnott, Richard O.

“…Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method by Sabir, O., Ya, T.M.Y.S.T.

“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). This new scheme is applied on staggered grid using implicit finite difference methods in order to achieve second order accuracy. …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method by Sabir, O., Ya, T.M.Y.S.T.

“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). This new scheme is applied on staggered grid using implicit finite difference methods in order to achieve second order accuracy. …”
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Blood cell classification using deep learning by Liaw, Mun Kin

“…The learning rate was fine-tuned using the Tuner library from Keras to find the optimal value that generates a well-established model. …”
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Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification by Azzoni, Livio, Foulkes, Andrea S., Liu, Yan, Johnson, Margaret, Smith, Collette, Kamarulzaman, Adeeba, Montaner, Julio, Mounzer, Karam, Saag, Michael, Cahn, Pedro, Cesar, Carina, Krolewiecki, Alejandro, Sanne, Ian, Montaner, Luis J.

“…The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/ml). …”
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