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Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model by Mohammed Adam, Kunna Azrag

“…However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. …”
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Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

“…Finally, the slice sampler algorithm was employed to estimate the re- turn and risk values of investment in Malaysian gold market.…”
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Scale-invariant and adaptive-search template matching for monocular visual odometry in low-textured environment by Aqel, Mohammad O. A.

“…The maximum allowable vehicle speed for the developed VO is up to 6.3 m/s.In short, the developed VO system, as well as the proposed algorithms and techniques, were successfully implemented, tested, and validated using real time kinematic GPS (RTK-GPS) with 2 cm positioning accuracy. …”
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Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali by Jarinah , Mohd Ali

“…Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. …”
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Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea by Sim, Doreen Ying Ying, Teh, Chee Siong, Ahmad Izuanuddin, Ismail

“…After comparing and contrasting this developed algorithm with the algorithm without being augmented by AA, i.e., Boosted post-Pruned Decision Tree (Boosted poP-DT), and the classical boosted decision tree algorithm, i.e., Boosted DT, there is a stepwise improvement shown when comparison proceeds from Boosted DT to Boosted poP-DT and to Boosted AApoP-DT.…”
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Multi-objective optimization of stand-alone hybrid renewable energy system by genetic algorithm by Nejad, Mohsen Fadaee

“…HOGA, as a new effective tool for multi-objective optimization by evolutionary algorithm is used in this research. HOGA (Hybrid Optimization by Genetic Algorithms) is developed by Dr.Lopez from Zaragoza university in Spain. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Retinal image blood vessel extraction and quantification with Euclidean distance transform approach by Kuryati, Kipli, Mohammed Enamul, Hoque, Lik, Thai Lim, Tengku Mohd Afendi, Zulcaffle, Siti Kudnie, Sahari, Muhammad Hamdi, Mahmood

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Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers by Uraibi, Hassan S.

“…We develop Robust Forward Selection algorithm based on RFCH correlation coefficient (RFS.RFCH) because FS.Winso is not robust to multivariate outliers. …”
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Robust estimation methods for fixed effect panel data model having block-concentrated outliers by Abu Bakar @ Harun, Nor Mazlina

“…To rectify this problem, the RWGM with RDF and RWGM with DRGP are developed by integrating the RDF and existing Diagnostic Robust Generalized Potential (DRGP); respectively, into the algorithm of GM-estimator. …”
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Outdoor marker-less tracking and pose calculating system for mobile augmented reality by Rehman, Ullah Khan

“…This research investigates and re-designs the “speed up robust feature” (SURF) algorithm for Smartphones to enable it to recognize the real world objects. …”
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Robust Kernel Density Function Estimation by Dadkhah, Kourosh

“…To remedy this problem, Kim and Scott (2008) proposed an Iteratively Re-weighted Least Squares (IRWLS) algorithm for Robust Kernel Density Estimation (RKDE). …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

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

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The nonlinear elastic and viscoelastic passive properties of left ventricular papillary muscle of a guinea pig heart by Hassan, Mohamed Mohsen Abdel-Naeim, Hamdi, M., Noma, A.

“…However, an optimization algorithm is developed, based on clinical intact heart measurements, to estimate and re-correct the material parameters in order to get the in vivo mechanical properties, needed for very accurate bio-simulation and for the development of new materials for the artificial heart.…”
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Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis by Kumar K P., Alruqi M., Hanafi H.A., Sharma P., Wanatasanappan V.V.

“…Finally, regression analysis was employed to establish correlations for estimating Nu and friction factor values. Prognostic models were developed using two sophisticated machine learning algorithms, XGBoost and Gradient Boosting Regression (GBR). …”

Energy, Vibration And Sound Research Group (e-VIBS) School Of Science And Technology Universiti Malaysia Sabah : Bioacoustics Signal Modeling Using Time-Frequency Distribution by Jedol Dayou, Ng, Chee Han, Ho, Chong Mun, Abdul Hamid Ahmad, Mohd Noh Dalimin, Sithi V. Muniandy

“…This research is carried out to develop new bioacoustics species identification method with the improvement in the identification accuracy. …”
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Hospital readmission risk prediction of COVID-19 patients using machine learning / Loo Wei Kit by Loo , Wei Kit

“…Ultimately, a novel Slime Mold Algorithm (SMA) integrated hybrid predictive model was developed. …”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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