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

“…The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. …”

Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

“…Graph neural networks lower prediction error by up to thirty percent relative to descriptor-driven QSAR models for structurally diverse inhibitors. …”

Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms by Md Ferdous Wahid (13485799)

“…A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). …”

Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter by Mena S. ElMenshawy (17983807)

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)

“…It is based on model predictive power control with space vector modulation. …”

A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS by Zerguine, Azzedine

“…The second algorithm consists of a mixed normalized LMF (XE-NLMF) algorithm which is normalized by the mixed signal and error powers. …”
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High speed multi-stage code search algorithm in CELP by Elshafei, M.

“…An efficient multistage algorithm for code search in the code excited linear prediction (CELP) methods of speech coding is described. …”
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AI-Based Methods for Predicting Required Insulin Doses for Diabetic Patients by Azar, Danielle

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

“…The study results show that Random Forest outperformed all other tested algorithms. It recorded the best values in all evaluation metrics: an average mean absolute error of 0.13, mean absolute percentage error of 0.6, root-mean-square error of 0.28 and R-squared value of 0.89.…”

Convergence and steady-state analysis of the normalized least mean fourth algorithm by Zerguine, Azzedine

“…The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm.…”

Convergence and steady-state analysis of the normalized least mean fourth algorithm by Zerguine, Azzedine

“…The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm.…”
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Estimating Construction Project Duration Using a Machine Learning Algorithm by Nasr, Joshua

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Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012) by Bitar, Mandy

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Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression by Alaa Abd-Alrazaq (17430900)

“…<p dir="ltr">Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. …”

Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning by Zeinab, Samira Jihad

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Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining by Mohammadjafar Hadad (21142499)

“…To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. …”

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting by Mohamed Massaoudi (16888710)

“…First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …”

Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS by Iman Faridmehr (14150616)

“…Several statistical metrics, including the root mean square error and the coefficient of variation, revealed that the generalized model achieved the most reliable and accurate prediction of the concrete beam’s deflection in comparison with international standards and other models. …”

Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm by Yan Cao (482880)

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

“…Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. …”