Convergence analysis of the variable weight mixed-norm LMS-LMFadaptive algorithm by Zerguine, A.

“…In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares-least mean fourth) adaptive algorithm is derived. The proposed algorithm minimizes an objective function defined as a weighted sum of the LMS and LMF cost functions where the weighting factor is time varying and adapts itself so as to allow the algorithm to keep track of the variations in the environment. …”
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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

“…<h3 dir="ltr">Purpose</h3><p dir="ltr">This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. …”

Computational evluation of protein energy functions by Mansour, Nashat

“…In this project, we carry out a computational evaluation of putative protein energy functions. …”
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Cross entropy error function in neural networks by Nasr, G.E.

“…The ANN is implemented using the cross entropy error function in the training stage. …”
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From Collatz Conjecture to chaos and hash function by Masrat Rasool (17807813)

“…By incorporating the Collatz process and carefully considering key-controlled variables, the proposed model aims to offer enhanced security properties while meeting the necessary criteria for a reliable and effective hashing mechanism. The effectiveness and dependability of the proposed hash function are evaluated by comparing it with two well-known hash algorithms, namely SHA-3 and SHA-2, as well as several other Chaos-based hash algorithms. …”

GATS: A Novel Hybrid Algorithm for Multiobjective Cell Placement in VLSI Circuit Design by Sait, Sadiq M.

“…This paper addresses the optimization of cell placement step in VLSI circuit design [1]. A novel hybrid algorithm is proposed for performance and low power driven VLSI standard cell placement. …”
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A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks by Saad, Walid

“…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV by Zulkifli, Muhammad

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Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning by Moussa, Mostafa Mohamed

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Bridge Structural Health Monitoring Using Mobile Sensor Networks by Amin, Fouad

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StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features by Muhammad Arif (769250)

“…Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. …”

An improved kernelization algorithm for r-Set Packing by Abu-Khzam, Faisal N.

“…Such parameterized reductions are known as kernelization algorithms, and a reduced instance is called a problem kernel. …”
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Scatter Search algorithm for Protein Structure Prediction by Mansour, Nashat

“…Given the protein's sequence of Amino Acids (AAs), our algorithm produces a 3D structure that aims to minimise the energy function associated with the structure. …”
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LINE SEARCH TECHNIQUES FOR THE LOGARITHMIC BARRIER FUNCTION IN QUADRATIC-PROGRAMMING by Bendaya, M.

“…In this paper, we propose a line-search procedure for the logarithmic barrier function in the context of an interior point algorithm for convex quadratic programming. …”
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Neural Networks as a Convex Problem by Khalil, Baha

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Intelligent Bilateral Client Selection in Federated Learning Using Game Theory by Wehbi, Osama

“…To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. …”
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A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities by Mahdi Mokhtarzadeh (11593310)

“…To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. …”

AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics by Naila Rabbani (291722)

“…In this review, I describe the utility of AGEomics biomarkers and provide evidence why these are close to the phenotype of a condition or disease compared to other metabolites and metabolomic approaches and how to train and test algorithms for clinical diagnostic and screening applications with high accuracy, sensitivity and specificity using machine learning approaches.…”

Iterative Methods for the Solution of a Steady State Biofilter Model by Salih, Mubarak Sahal

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Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants by Mohannad N. Khandakji (13885434)

“…<h3>Background</h3><p dir="ltr">Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. …”