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. …”

Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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Localizing-ground Transmitters Using Airborne Antenna Array by Daffalla, Mirghani Moutaman

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

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Get full text
Get full text
Get full text

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. …”

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…</p><h3>Objective</h3><p dir="ltr">This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.…”