Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging by Maricar, Mohammed Farook

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Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling by Majed Hadid (17148364)

“…A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. …”

Optimal Dispatch of Mobile Energy Storage Unit to Support EV Charging Stations by Elmeligy, Mohamed Mostafa Abdelazim

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On the Provisioning of Ultra-Reliable Low-Latency Services in IoT Networks with Multipath Diversity by Sweidan, Zahraa

“…Simulation results are presented for both parts of the thesis to illustrate the effectiveness of the proposed solutions and algorithms in comparison with optimal solutions and baseline algorithms.…”
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Effective dispatch strategies assortment according to the effect of the operation for an islanded hybrid microgrid by Sk.A. Shezan (21323048)

“…In HOMER software, all the possible dispatch algorithms were analyzed, and the power system responses and reliability study were carried out using DIgSILENT PowerFactory. …”

Exploring the Dynamic Interplay of Deleterious Variants on the RAF1–RAP1A Binding in Cancer: Conformational Analysis, Binding Free Energy, and Essential Dynamics by Abbas Khan (5141000)

“…Hence, the current study focuses on the screening of clinically reported substitutions in the <i>RAF1</i> and <i>RAP1A</i> genes using predictive algorithms integrated with all‐atoms simulation, essential dynamics, and binding free energy methods. …”

Large language models for code completion: A systematic literature review by Rasha Ahmad Husein (19744756)

“…Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. While several research papers have focused on the use of LLMs for code completion, these studies are fragmented, and there is no systematic overview of the use of LLMs for code completion. …”

Copy number variations in the genome of the Qatari population by Khalid A. Fakhro (3158862)

“…Consistent with high consanguinity levels in the Bedouin subpopulation, we found an increased burden for homozygous deletions in this group. …”

Combinatorial method for bandwidth selection in wind speed kernel density estimation by El Dakkak, Omar

“…This goal calls for devising probabilistic models with adaptive algorithms that accurately fit wind speed distributions. …”
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Cooperative Caching Policy in Fog Computing for Connected Vehicles by Ghazleh, Ali

“…Furthermore, the results showed the proposed model's effectiveness compared to traditional algorithms.…”
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Just-in-time defect prediction for mobile applications: using shallow or deep learning? by Raymon van Dinter (10521952)

“…Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. …”

The Effects of Data Mining on Small Businesses in Dubai by AlMutawa, Rasha

“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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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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Four quadrant robust quick response optimally efficient inverterfed induction motor drive by Islam, S.M.

“…Simulation results with a 100-hp motor show that up to 80% savings in controllable losses are achievable at light load, and the motor can reach rated speed in just 750 ms. The control algorithms developed are readily implementable with present-day microprocessors…”
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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. …”

Prototype project management tool (PPMT) with cocomo calibration. (c1998) by Haddad, Ibrahim F.

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Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features by Mariam Bahameish (19255789)

“…However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. …”

Using C++ to Calculate SO(10) Tensor Couplings by Bhagwagar, Azadan

“…Model building in SO(10), which is the leading grand unification framework, often involves large Higgs representations and their couplings. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah (21399854)

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”

Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine by Majd A. AbuAlrob (22392505)

“…Ethical considerations, such as safeguarding patient privacy, ensuring data security, and mitigating algorithmic bias, underscore the importance of responsible AI integration. …”