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

“…This is achieved by predicting subsequent tokens, such as keywords, variable names, types, function names, operators, and more. Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. …”

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. …”

Singularly perturbed nonlinear ODEs and interior point optimizationalgorithms by Ezzine, J.

“…In addition, this connection is used to show that the logarithmic barrier function is indeed, in some sense, optimum…”
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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. …”

A quadratic kernel for 3-set packing by Abu-Khzam, Faisal N.

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The architecture of a highly reconfigurable RISC dataflow array processor by Sait, Sadiq M.

“…The array can be programmed to execute arbitrary algorithms in both static and dynamic manner. The processor array is modelled at the behavioural level in VHDl. …”
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Nouveaux points de coupure et primitives pour les préoccupations de renforcement de sécurité by Mourad, Azzam

“…The two proposed primitives are called exportParameter and importParameter and are used to pass parameters between two pointcuts. They allow to analyze a program’s call graph in order to determine how to change function signatures for the passing of parameters associated with a given security hardening. …”
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New aspect-oriented constructs for security hardening concerns by Mourad, Azzam

“…The two proposed primitives are called ExportParameter and ImportParameter and are used to pass parameters between two pointcuts. They allow to analyze a program's call graph in order to determine how to change function signatures for passing the parameters associated with a given security hardening. …”
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Machine Learning Model for a Sustainable Drilling Process by Alsaidi, Ola

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Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks by Abbas, Nadine

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ELISSAR. (c1997) by Nasreddine, Diana M.

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DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins by Samir Brahim, Belhaouari

“…Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. …”
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IoT-Based Sustainable Parking Lot by Binmahfooz, Abdullah

“…The access control system employs a combination of vehicle detection and plate recognition algorithms to identify and authenticate vehicles entering and exiting the parking lot. …”
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Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia by Hanan Ehtewish (17149825)

“…This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). …”

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces by Uzair Sajjad (19646296)

“…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …”