Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

“…Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. …”

Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort by Mohamed Adil Shah Khoodoruth (14589828)

“…Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. The Random Forest model, a supervised machine learning algorithm, efficiently differentiated between cases and controls and between TRS and NTRS, with accuracies of 86.87 % and 88.41 %, respectively. …”

An efficient approach for textual data classification using deep learning by Abdullah Alqahtani (7128143)

“…Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. …”

Dynamic Cyber Resilience of Interdependent Critical Information Infrastructures by JUMA, MAZEN GHAZI

“…The technology stack of the proposed solution was also implemented with four algorithms and eight protocols. The evaluation results of the proposed solution were compared to the results of standard solutions under different cyberattack scenarios using quantitative research methods involving computing simulations, emulation experiments, and analytical modeling. …”
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Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network by Monzure-Khoda Kazi (17191207)

“…<p dir="ltr">This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. …”

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort by Sergio Márquez-Sánchez (19437985)

“…However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. …”

Software defect prediction. (c2019) by Moussa, Rebecca

“…One that focuses on predicting defect in software modules using a hybrid heuristic - a combination of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). We compare our approach to 9 well known machine learning techniques and results show the advantages of our model over the other techniques. …”
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QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning by Md Nazmul Islam Shuzan (21842426)

“…Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. …”

Dynamic multiple node failure recovery in distributed storage systems by Itani, May

“…The four problems are modeled using incidence matrices and solved heuristically. …”
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Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment by Surjeet Dalal (4906894)

“…The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). …”

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks by Yassine Himeur (14158821)

“…Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. …”

Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques by Hassan, Mahitab Alaaeldin

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Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

“…As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. …”
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Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. …”

Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%).…”

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

“…Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. …”
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Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by Chakar, Joseph

“…However, these algorithms often impose prohibitive levels of memory and computational overhead, especially in resource-constrained environments. …”
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Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining by Alqaryouti, Omar

“…Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. …”
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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%). …”

Design and analysis of efficient and secure elliptic curve cryptoprocessors. by Al-Somani, Turki Faisal

“…The proposed architectures have been modeled using VHDL and implemented on FPGA platform.…”
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