Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. …”

Portable Electromagnetic Surface Profiling System by Al Homssi, Bassel Mohamad

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LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

“…The main aim was to predict the initial leukemia disease. Machine learning algorithms such as decision tree (DT), naive bayes (NB), random forest (RF), gradient boosting machine (GBM), linear regression (LinR), support vector machine (SVM), and novel approach based on the combination of Logistic Regression (LR), DT and SVM named as ensemble LDSVM model. …”

Conceptual Design and Analysis of Concentrated Photovoltaic Thermal (CPVT) Solar Collectors with Different Configurations by Sharaf, Omar Z.

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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar (22046597)

“…We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”

Decision-level Gait Fusion for Human Identification at a Distance by Al-Tayyan, Amer

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Condenser capacity and hyperbolic perimeterImage 1 by Mohamed M.S., Nasser

“…We study the conformal capacity by using novel computational algorithms based on implementations of the fast multipole method, and analytic techniques. …”
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Deep and transfer learning for building occupancy detection: A review and comparative analysis by Aya Nabil Sayed (17317006)

“…Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. …”

Dynamic Cyber Resilience of Interdependent Critical Information Infrastructures by JUMA, MAZEN GHAZI

“…The technology stack of the proposed solution was also implemented with four algorithms and eight protocols. The evaluation results of the proposed solution were compared to the results of standard solutions under different cyberattack scenarios using quantitative research methods involving computing simulations, emulation experiments, and analytical modeling. …”
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Correlation Clustering via 2-Club Clustering with Vertex Splitting by Thoumi, Sergio

“…In this thesis, we study two novel problems: 2-CLUB CLUSTER VERTEX SPLITTING (2CCVS) and 2-CLUB CLUSTER EDITING WITH VERTEX SPLITTING (2CCEDVS). 2-Clubs alleviate the strictness of cliques and vertex splitting allows for a vertex to be in multiple clusters, overcoming two main limitations of the currently studied models. …”
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Condenser capacity and hyperbolic perimeter by Mohamed M.S. Nasser (16931772)

“…<p dir="ltr">We study the conformal capacity by using novel computational algorithms based on implementations of the fast multipole method, and analytic techniques. …”

Adaptive Federated Learning Architecture To Mitigate Non-IID Through Multi-Objective GA-Based Efficient Client Selection by Ajaj, Mohamad

“…Existing solutions still have several constraints leading to suboptimal model performance and slow convergence. In this paper, we propose a novel approach that incorporates genetic algorithms with an enhanced client selection strategy, utilizing client metadata rather than raw data. …”
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Intelligent Bilateral Client Selection in Federated Learning Using Game Theory by Wehbi, Osama

“…Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. …”
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Machine Learning Techniques for Pharmaceutical Bioinformatics by SULTAN, AHMED ATTA AHMED

“…In this matrix, each drug is represented by a vector of attributes from all other drugs. A predictive model is developed to predict drug indication as well as to predict new DDIs using multiple machine learning algorithms. …”
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Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations by Mueen Uddin (4903510)

“…This research paper explores GAI in enhancing cybersecurity by leveraging AI Models and algorithms. GAI can autonomously address common security issues, detect novel threats, and augment human intervention in critical security aspects. …”

Approximate XML structure validation based on document–grammar tree similarity by Tekli, Joe

“…Our approach exploits the concept of tree edit distance, introducing a novel edit distance recurrence and dedicated algorithms to effectively compare XML documents and grammar structures, modeled as ordered labeled trees. …”
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Approximate XML structure validation technical report by Tekli, Joe

“…Our approach exploits the concept of tree edit distance, introducing a novel edit distance recurrence and dedicated algorithms to effectively compare XML documents and grammar structures, modeled as ordered labeled trees. …”
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A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass by Uzma Nawaz (21980708)

“…By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”

Single-channel speech denoising by masking the colored spectrograms by Sania Gul (18272227)

“…<p>Speech denoising (SD) covers the algorithms that remove the background noise from the target speech and thus improve its quality and intelligibility. …”

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort by Sergio Márquez-Sánchez (19437985)

“…However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. …”