Sulfur oxidative coupling of methane process development and its modeling via machine learning by Giovanni Scabbia (13751501)

“…The outcomes of the simulated process were used to design a data-driven modeling approach, based on machine learning methods, and to evaluate its interpolation accuracy. …”

A Robust Deep Learning Approach for Distribution System State Estimation with Distributed Generation by Kfouri, Ronald

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Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials by Abdul Wahab Ziaullah (16027076)

“…Among the mechanics of a BO is the use of a machine learning (ML) model that learns about the scope of the problem through data being acquired on the fly. …”

Using machine learning architecture to optimize and model the treatment process for saline water level analysis by Arumugam, Mahendran

“…This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. …”
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Detection of central serous retinopathy using deep learning through retinal images by Syed Ale Hassan (17785655)

Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management by Alhusin, Mohammed Omer Alamin

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Heart Disease Prediction using Machine Learning and Deep Learning Approaches by Bostani, Ali

“…This article performs a systematic literature review of the data mining, machine learning and deep learning approaches used for heart disease prediction, especially in proving the efficiency of the techniques in identifying the hidden patterns in the large health datasets for early diagnosis. …”
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Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand forecasting. …”

Using Mobile Technology for Coordinating Educational Plans and Supporting Decision Making Through Reinforcement Learning in Inclusive Settings by Siyam, Nur

“…The proposed work presents four significant contributions, namely identifying the key design principles to inform the design of a coordination mobile app for special education, developing and implementing the IEP-Connect mobile app, modelling the selection of a motivator as a Markov Decision Process (MDP), and proposing a Reinforcement Learning (RL) framework to recommend a motivator to be used with students with SEND in a given learning setting. …”
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SVM-based control chart for Quality 4.0 by Abdelwahid, Issra M.

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An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study by Ayman Hassan (14426412)

“…</p><h3>Methods</h3><p dir="ltr">Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. …”

Comparative Study of Deep Learning Models Versus Machine Learning Models for Wind Turbine Intelligent Health Diagnosis Systems by Rababaah, Aaron

“…Although there have been number of attempts in addressing this problem, number of gaps exist including the lack of detecting early signs of abnormal signals, deep learning (DL) solutions which are rare in this specific area of interest, the impact of number of features in convolution neural networks (CNNs) which was never studied, and the lack of comparative studies between DL versus conventional machine learning (ML) models for this problem of interest. …”
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Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems by Ahmad K. Sleiti (14778229)

“…The data-driven approach alone is not sufficient and a low-order physics based model should operate in tandem with the updated latest system parameters to allow interpretation and enhancing the results from the data-driven process. …”

Hate speech detection with ADHAR: a multi-dialectal hate speech corpus in Arabic by Anis Charfi (18697357)

“…We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. …”

Issues Related to Teachers’ Knowledge and Early Intervention Services Offered to Early Learners with Sensory Processing Disorder in the UAE by Sadoun, Haifroun M.B.

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Positive Unlabelled Learning to Recognize Dishes as Named Entity by TAREK, AIMAN

“…In this research, I focus on extracting food and dish names as a named entity. With the lack of labelled data, I try to overcome the cold start and avoid manual labelling by building a lookup table from a dictionary. …”
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Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

“…Data-driven approaches, such as machine learning (ML), particularly deep learning (DL), have shown promising results. …”
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Adoption and Implementation of Online Learning Systems in Lebanon: Prospects and Barriers by SHAYA, NESSRIN MOHAMMAD

“…This study aims at obtaining a finer-grained understanding of the primary prospects and barriers to adopt and implement online education systems in Lebanon, through investigating online learning readiness and acceptance among various key stakeholders in higher education. …”
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A Study of the Arabic and English Composing Processes of Emirati School Students: A Socio- Cognitive Approach by Albakri, Rehab Helmi

“…In addition, the study focused on the factors affecting the composing process such as gender, age and first language. The theoretical framework of the study was the socio-cognitive approach introduced by Atkinson (2002) which focuses on the interaction between the social and cognitive dimensions of language learning. …”
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