Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…Wearable devices (WDs) make use of sensors historically reserved for hospital settings. …”

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. …”

Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort by Mohamed Adil Shah Khoodoruth (14589828)

“…Key measurements included complete blood count, lipid panel, HbA1c, and ferritin levels. Our findings indicated elevated body mass index (BMI) and triglyceride (TG) levels in both patient groups compared to controls. …”

Data Redundancy Management in Connected Environments by Mansour, Elio

“…To address these limitations, we propose a framework for data redundancy management at the device level, denoted DRMF. We describe its modules, and clustering-based algorithms. …”
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Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review by Yazeed Yasin Ghadi (16667109)

“…Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. …”

Triage decisions for ICU admission by Blanch, Lluis

“…The team made recommendations and concluded that triage should be led by intensivists considering input from nurses, emergency medicine professionals, hospitalists, surgeons, and allied professionals. Triage algorithms and protocols can be useful but can never supplant the role of skilled intensivists basing their decisions on input from multidisciplinary teams. …”
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Data redundancy management for leaf-edges in connected environments by Mansour, Elio

“…To address these limitations, we propose here DRMF: Data Redundancy Management for leaF-edges allowing to identify and remove data redundancies in connected environments at the device level. DRMF considers both static and mobile edge devices, and provides two algorithms for temporal and spatio-temporal redundancy detection. …”
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Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper by Mutaz I. Othman (21186827)

“…</p><h3>Methods</h3><p dir="ltr">A discursive approach was used to optimise nurse–patient assignments and the impact of ML models. …”

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

“…The technology stack was also enhanced with three new algorithms and five protocols. The proposed solution was optimized using the iterative four-objective cycle based on previous primary phase results. …”
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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. …”

Design and analysis of efficient and secure elliptic curve cryptoprocessors. by Al-Somani, Turki Faisal

“…Elliptic Curve Cryptosystems have attracted many researchers and have been included in many standards such as IEEE, ANSI, NIST, SEC and WTLS. The ability to use smaller keys and computationally more efficient algorithms compared with earlier public key cryptosystems such as RSA and ElGamal are two main reasons why elliptic curve cryptosystems are becoming more popular. …”
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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. …”

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

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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. …”

Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by Chakar, Joseph

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Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection by Ahmad Yaser Alhaddad (7017434)

“…We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. …”