A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

“…For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. …”

Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

Various Faults Classification of Industrial Application of Induction Motors Using Supervised Machine Learning: A Comprehensive Review by Rehaan Hussain (22302742)

“…In current literature, there are a number of papers that address all these faults using different methods, and this paper compiles the information from the written works for ease of access. Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. …”

A hybrid approach for XML similarity by Tekli, Joe

“…Our approach comprises of an original edit distance operation cost model, introducing semantic relatedness of XML element/attribute labels, in traditional edit distance computations. A prototype has been developed to evaluate our model’s performance. …”
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Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

“…</p><h3>Methods</h3><p dir="ltr">We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. …”

A SEQUENCING PROBLEM IN THE WEAVING INDUSTRY by AlHaboubi, M.H.

“…The structure of the set-up cost is utilized to develop a polynomial time solution algorithm. The algorithm involves computing the shortest path of a network. …”
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A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network by KHAN, FIROZ

“…The preprocessing of data includes remove missing value records and remove columns that have a negligible impact. The feature selection uses Grey Wolf Optimisation and Binary Search algorithms for choosing the best features out of the dataset. …”
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Modelling And Analysis Of A Multi-Stage Order Quantity Model by Andijani, A. A.

“…An algorithm to determine the relationship between the lot sizes of the MSOQ model is also developed. …”
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Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems by Tarek Berghout (16905132)

Particle swarm optimization for multimachine power systemstabilizer design by Abido, A.A.

“…In this paper, a novel evolutionary algorithm based approach to optimal design of multimachine power system stabilizers (PSSs) is proposed. …”
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A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM by AlSultan, K.S.

“…In this paper, we develop a new algorithm for solving this problem based on a tabu search technique. …”
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Design and Optimization of a Planar Cable Robot by Kassem, Mohamad Hassan

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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System UsingA Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”

A Deployment Optimization Model for Wimax Base-Stations by Aboualmal, Abdulhadi Mahmoud

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A Framework for Predictive Modeling in Sustainable Projects by Labban, Tarek

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A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…The proposed approach makes advantage of data augmentation to generate newly synthesized images, which are subsequently processed using a watershed mask. …”

Precision nutrition: A systematic literature review by Daniel Kirk (17302798)

“…Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. …”