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

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Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms by Usman Ali (6586886)

“…The accuracy of these algorithms was assessed with standard statistical measures and ground truth data randomly collected in Prince Edward Island, Canada. …”

UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data by Behrouz Ahadzadeh (19757022)

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A Survey of Data Clustering Techniques by Sobeh, Salma

“…To effectively analyze and utilize this data, AI particularly machine learning, and deep learning, can provide a practical solution. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm by Nasr, Joshua

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Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization by Abu Zitar, Raed

Subjects: “…orthogonal learning (OL)…”
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A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015) by Wehbe, Gioia Wahib

“…Our algorithm first performs a feature selection step to define differentiable SNPs. …”
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Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

“…Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. …”

Exploring Semi-Supervised Learning Algorithms for Camera Trap Images by Sajun, Ali Reza

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Prediction of EV Charging Behavior Using Machine Learning by Shahriar, Sakib

“…Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. …”
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Predict Student Success and Performance factors by analyzing educational data using data mining techniques by ATIF, MUHAMMAD

“…The research study is performed as experimental analysis and develop models from nine machine learning algorithms including KNN, Naïve Bayes, SVM, Logistic regression, Decision Tree, Random forest, Adaboost, Bagging Classifier, and voting Classifier. …”
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Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review by Francisco Monteiro-Guerra (16864122)

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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. …”

Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms by Almahmood, Mothanna

“…Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. …”
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Application of Machine Learning Algorithms to Enhance Money Laundering and Financial Crime Detection by HAMDALLAH, KHALID WAJIH TURKI

“…In order to analyze the performance of machine learning algorithms, data was provided by a bank to be used for educational purposes and shall remain undisclosed. …”
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning by Far, Reza Davoodi

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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology by Senyuk, Mihail

“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
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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. …”