Adaptive temperature control of a reverse flow process by using reinforcement learning approach by A. Binid (22046054)

“…Additionally, a second algorithm is presented to enhance the implementability of the reinforcement learning algorithm from a practical perspective. …”

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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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). …”

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis by Alaa Abd-Alrazaq (17430900)

“…We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. …”

A Robust Deep Learning Approach for Distribution System State Estimation with Distributed Generation by Kfouri, Ronald

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Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba, Sulaiman

“…Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. …”
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Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba Sulaiman (17734065)

“…</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Medical Informatics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ijmedinf.2025.105840" target="_blank">https://dx.doi.org/10.1016/j.ijmedinf.2025.105840</a></p>…”

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning by Mohamed Massaoudi (16888710)

“…Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Access Journal of Power and Energy<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/oajpe.2025.3535709" target="_blank">https://dx.doi.org/10.1109/oajpe.2025.3535709</a></p>…”

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects by Mohamed Massaoudi (16888710)

“…In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. …”

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

“…It investigates the gain in training time and the performance in terms of accuracy when clustering-based deep learning modeling is employed for STLF. A k-Medoid based algorithm is employed for clustering whereas the forecasting models are generated for different clusters of load profiles. …”

Block constrained pressure residual preconditioning for two-phase flow in porous media by mixed hybrid finite elements by Stefano Nardean (14151900)

“…<p dir="ltr">This work proposes an original preconditioner that couples the Constrained Pressure Residual (CPR) method with block preconditioning for the efficient solution of the linearized systems of equations arising from fully implicit multiphase flow models. …”

Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…Unsupervised machine learning is a powerful technique for performing clustering, which involves identifying patterns or similarities within a dataset and grouping them into distinct clusters or subgroups. …”
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Animal migration optimization algorithm: novel optimizer, analysis, and applications by Abualigah, Laith

“…A new heuristic optimization algorithm was proposed in 2013 called the animal migration optimization (AMO) algorithm. …”
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Using genetic algorithms to optimize software quality estimation models by Azar, Danielle

“…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …”
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Development of a deep learning-based group contribution framework for targeted design of ionic liquids by Sadah Mohammed (18192859)

“…<p dir="ltr">In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). …”

On the P-type learning control by Saab, Samer S.

“…Sufficient conditions for the robustness and convergence of P-type learning control algorithms for a class of time-varying, nonlinear systems are presented. …”
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A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata by Siddiqui, Ansah Juned

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Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”

Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering by Saadia Jamil (22045946)

“…A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. …”

Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning by Mohamed Elhadary (16329082)

“…With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. …”