Novel algorithms for accurate DNA base-calling by Mohammed, Omniyah Gul

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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

CNN and HEVC Video Coding Features for Static Video Summarization by Issa, Obada

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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

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Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid (22047578)

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net by Sania Gul (18272227)

“…We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. …”

Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…This study aimed to utilise an extensive dataset, which included the period of the COVID-19 pandemic, in a modern Middle Eastern Emergency Medical Service to comprehend and predict the behaviour of non-transport decisions, a major multi-variable factor in pre-hospital emergency medicine. </p><h3>Methods</h3><p dir="ltr">Using Python® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. …”

Pre-production movie rating prediction using machine learning. (c2017) by Gerges, Firas Abdallah

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Pre-training on Multi-modal for Improved Persona Detection Using Multi Datasets by Abdulla, Salwa

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Fault-Tolerant Network Topologies for Datacenters by Attia, Heba Mahmoud Helal

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Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management by Islam, Eljilany

“…From the hospital's perspective, a cost-benefit analysis was conducted based on a 1-year decision-analytic follow-up model of the economic implications of using a pharmacogenetic algorithm vs standard of care in pre-operative warfarin management in the Hamad Medical Corporation, Qatar. …”
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Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management by Islam Eljilany (14151588)

“…From the hospital's perspective, a cost-benefit analysis was conducted based on a 1-year decision-analytic follow-up model of the economic implications of using a pharmacogenetic algorithm vs standard of care in pre-operative warfarin management in the Hamad Medical Corporation, Qatar. …”

Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

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Cortical EEG Source Localization of Focal Epilepsy by Siyam, Wisal Elfatih Mohamed

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Automatic Video Summarization Using HEVC and CNN Features by Issa, Obada

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Application of Data Mining to Predict and Diagnose Diabetic Retinopathy by Haniyeh, Maryam

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Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

“…The technique proposed for large problems is based on a pre-mapping graph contraction heuristic algorithm, which results in a smaller search space. …”
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Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data by Arshad, Muhammad Arbab

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Defense against adversarial attacks: robust and efficient compressed optimized neural networks by Insaf Kraidia (19198012)

“…It also introduces complexity to deter potential attackers, enhancing model accuracy in adversarial settings. This study compresses the generative pre-trained transformer (GPT) by 65%, saving time and memory without causing performance loss. …”