Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study by Ahmed, Khaled Essam Hosny

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

“…<p>Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. …”

Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems by Mahmood, Lubna Syeda

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Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation by Tejedor, Alejandro

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Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE by Mathew, Elezabeth

“…Various descriptive, diagnostic, predictive, and prescriptive analysis is done on the e-procurement data. The deep learning model developed can perform thousands of routine and, repetitive tasks within a fairly short period compared to what it would take for a human being without any compromise on the quality of work. …”
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LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

“…However, they are not highly effective in improving results and are frequently employed by doctors for cancer diagnosis. This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. …”

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%). …”

Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d... by Tarik Elhadd (5480393)

“…Several machine learning techniques were trained to predict blood glucose levels in a regression framework utilising physical activity and contemporaneous blood glucose levels, comparing Ramadan to non-Ramadan days.…”

Modeling of permeability impairment dynamics in porous media: A machine learning approach by Ahmed, Elrahmani

“…A Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) numerical framework, employing a four-way coupling scheme, was used to generate the data for training and validation of the Machine Learning Model (MLM). …”
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A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies by Saurabh Tewari (22282594)

“…The proposed MCLA shows an enhancement of 2% in lithofacies accuracy and an approximately 4% increment in reliability compared with the top-performing Extra Tree classifier considered in this study.…”

Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study by Shahan Ali Memon (18812038)

“…</p><h3>Objective</h3><p dir="ltr">The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States.…”

Kalman Filtering and Bipartite Matching Based Super-Chained Tracker Model for Online Multi Object Tracking in Video Sequences by Shahzad Ahmad Qureshi (19517416)

“…In this article, we have proposed a Super Chained Tracker (SCT) model, which is convenient and online and provides better results when compared with existing MOT methods. The proposed model comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution. …”

A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact by Samer Gowid (15955152)

The Influence of Ambient Weather Parameters on the Prediction of an Electrical Power Production of a Combined Cycle Power Plant in the UAE by ALABDOULI, HAJER ALI

“…The analysis includes applying machine learning methods such as linear regression and artificial neural networks (ANNs) to develop a predictive power production model using different interactive computer programs such as Minitab, RStudio and Microsoft Excel. …”
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Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort by Fatima Qafoud (14778571)

“…Methods: This study used 12-lead ECG data from 13,827 participants. …”