Optimal selection of the forgetting matrix into an iterative learning control algorithm by Saab, Samer S.

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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms by Humna Khan (17541972)

“…The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields.…”

Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise by Saab, Samer S.

“…Arbitrary high precision output tracking is one of the most desirable control objectives found in industrial applications regardless of measurement errors. The main purpose of this paper is to supply to the iterative learning control (ILC) designer guidelines to select the corresponding learning gain in order to achieve this control objective. …”
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R² = 0.05 to 0.99 in a representative thiophene set. …”

Exploiting Sparsity in Amplify-and-Forward Broadband Multiple Relay Selection by Ala Gouissem (7006463)

“…In particular, by separating all the subcarriers or some subcarrier groups from each other and by optimizing the selection and beamforming vector(s) using OMP algorithm, a higher level of frequency diversity can be achieved. …”

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms by Ghulam Mustafa (458105)

Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm by Khadija Attouri (18024307)

A genetic approach to the selection of the variable structurecontroller feedback gains by Al-Duwaish, H.N.

“…Contrary to the trial and error selection of the variable structure feedback gains reported in the literature, the selection in the present work is done using genetic algorithms. …”
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Combinatorial method for bandwidth selection in wind speed kernel density estimation by El Dakkak, Omar

“…In this study, a non-parametric combinatorial method is implemented for obtaining an accurate non-parametric kernel density estimation (KDE)-based statistical model of wind speed, in which the selection of the bandwidth parameter is optimised concerning mean integrated absolute error (L 1 error ) between the true and hypothesised densities. …”
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An Alternating Projection Framework for Elementwise Masked Nonlinear Matrix Decomposition by Kulathil, Muhammed Noshin Poovan

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Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter by Yahia, Mohamed

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Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

“…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …”

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model by Md. Nazmul Islam Shuzan (16888827)

“…Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. …”

Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems by Mahmood, Lubna Syeda

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A flexible genetic algorithm-fuzzy regression approach for forecasting: The case of bitumen consumption by Azadeh, Ali

“…Design/methodology/approach In the proposed approach, the parameter tuning process is performed on all parameters of genetic algorithm (GA), and the finest coefficients with minimum errors are identified. …”
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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study by Mutasim Baba, Fuad

“…Another new metric is introduced, 1 ◦C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 ◦C during the cali bration period, to select the best solutions from the Pareto Front solutions. 0.5 ◦C Percentage Error criterion is also used for the level of accuracy the model can achieve. …”
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Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms by Md Ferdous Wahid (13485799)

“…The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. …”

Learn while tracking. (c2013) by Toukhtarian, Raffi

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AI-Aided Robotic Wide-Range Water Quality Monitoring System by Awwad, Ameen

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Real-Time Implementation of High Performance Control Scheme for Grid-Tied PV System for Power Quality Enhancement Based on MPPC-SVM Optimized by PSO Algorithm by Abdelbasset Krama (16870008)

“…A double-stage PV system is selected due to its flexibility in control, unlike single-stage strategies. …”