An XML Document Comparison Framework by Tekli, Joe

“…As the Web continues to grow and evolve, more and more information is being placed in structurally rich documents, XML documents in particular, so as to improve the efficiency of similarity clustering, information retrieval and data management applications. Various algorithms for comparing hierarchically structured data, e.g., XML documents, have been proposed in the literature. …”
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Transformations for Variants of the Travelling Salesman Problem and Applications by Assaf, Mustafa Jamil

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Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine by Shomope, Ibrahim

“…The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. …”
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PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits by Hind Almerekhi (7434776)

“…Before finding toxicity triggers, we built and evaluated various machine learning models to detect toxicity from Reddit comments. Subsequently, we used our best-performing model, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model that achieved an area under the receiver operating characteristic curve (AUC) score of 0.983 to detect toxicity. …”

Evolutionary support vector regression for monitoring Poisson profiles by Ali Yeganeh (16624998)

“…The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. …”

Prototype project management tool (PPMT) with cocomo calibration. (c1998) by Haddad, Ibrahim F.

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Real-Time Implementation of GPS Aided Low Cost Strapdown Inertial Navigation System by Sahawneh, Laith Rasmi

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A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation by Khaled Dhibi (16891524)

“…In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. …”

Resilience analytics: coverage and robustness in multi-modal transportation networks by Abdelkader Baggag (14153040)

“…<p>A multi-modal transportation system of a city can be modeled as a multiplex network with different layers corresponding to different transportation modes. …”

An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids by Ifeoluwa Wole-Osho (14151315)

“…A polynomial correlation model, the adaptive neuro-fuzzy inference system model and an artificial neural network model optimised with three different learning algorithms. …”

Four quadrant robust quick response optimally efficient inverterfed induction motor drive by Islam, S.M.

“…An optimal efficiency calculator provides optimum flux and torque producing current, while a supervisory control eliminates the intricate boot-strap effect of the flux-torque loop. A model reference-based adaptive speed controller guarantees quick speed response and robustness of the drive system. …”
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Creating and detecting fake reviews of online products by Joni Salminen (7434770)

“…First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. …”

A family of minimum curvature variable-methods for unconstrained optimization. (c1998) by Obeid, Samir Y.

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Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study by Muhammad Atif Butt (10849980)

“…Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. …”

Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network by Monzure-Khoda Kazi (17191207)

“…These algorithms include random forest (RF) classification and artificial neural networks (ANN). …”

Condenser capacity and hyperbolic perimeterImage 1 by Mohamed M.S., Nasser

“…We study the conformal capacity by using novel computational algorithms based on implementations of the fast multipole method, and analytic techniques. …”
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Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer by Andrea Remo (447375)

“…</p><h3>Methods</h3><p dir="ltr">In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype. …”

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations by Md Mosarrof Hossen (21399056)

“…To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. Leveraging FL enables efficient model training across diverse end-user applications while preserving data privacy. …”

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …”

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

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