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. …”

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. …”

Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures by Iryna Haponchyk (19691701)

“…Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. …”

Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions by Khalil Khan (9333883)

“…Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. …”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

“…Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. …”

Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique by Iqbal Hassan (22155274)

“…The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. …”

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

“…</p><h3>Objective</h3><p dir="ltr">In this paper, we propose a machine learning–based approach for identifying research gaps through the analysis of scientific literature. …”