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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A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation by Iman M. Hosseini Naveh (16891482)

“…The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). …”

DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications by Danya A. Saifaldeen (19498705)

“…The system comprises four VLC access points with light fixtures, reinforced by a mirror array sheet, and a mmWave access point with antennas, supported by a reflecting unit sheet. …”

High-Accurate Parameter Identification of PEMFC Using Advanced Multi-Trial Vector-Based Sine Cosine Meta-Heuristic Algorithm by Badreddine Kanouni (23073244)

“…This paper introduces a novel multi-trial vector-based sine cosine algorithm (MTV-SCA) for the identification of seven unknown parameters of PEMFCs. …”

H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling by Peixoto, Eduardo

“…Then, two types of mode mapping algorithms are proposed. In the first solution, a single H.264/AVC coding parameter is used to determine the outgoing HEVC partitions using dynamic thresholding. …”
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Upgraded SemIndex Prototype Supporting Intelligent Database Keyword Queries through Disambiguation, Query as You Type, and Parallel Search Algorithms by Tekli, Joe

“…Various weighting functions and intelligent search algorithms have been developed for that purpose and will be presented here. …”
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An Adapted Load-Balancing implementation for Sharded Blockchains by Daou, David Halim

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Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. …”

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review by Tareq Foqha (17947778)

“…It covers a wide range of techniques, including conventional and deterministic methods, metaheuristic algorithms, and hybrid approaches. …”

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems by Agushaka, Jeffrey O.

“…This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. …”
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VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems by Hisham A. Kholidy (18891802)

“…However, NNGE algorithm tends to produce rules that test a large number of input features. …”

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)

“…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). …”

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. …”

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

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Minimizing using BBO and DFO methods by El Zeghondy, Jean

“…In order to apply this algorithm to real life problems, two numerical methods need to be developed: A numerical method to solve nonlinear differential equations, and a numerical method to nd the minimum of an objective function (given only numerically) in fi nite dimensional spaces. …”
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Face Recognition in Uncontrolled Indoor Environment by Abuqaaud, Kamal Adel

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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. …”

Artificial intelligence in respiratory care by Manjush Karthika (18384907)

“…Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties. AI algorithms are widely studied in areas like mechanical ventilation, sleep medicine, lung ultrasound, and pulmonary function diagnostics and the results are found to be promising. …”

R-CONV++: uncovering privacy vulnerabilities through analytical gradient inversion attacks by Tamer Ahmed Eltaras (22565414)

“…The first algorithm presents a novel data leakage method that efficiently exploits convolutional layer gradients, demonstrating that even with non-fully invertible activation functions, such as ReLU, training samples can be analytically reconstructed directly from gradients without the need to reconstruct intermediate layer outputs. …”