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

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Conceptual Skills in Leading Change: A Competence Approach to Public Sector Leadership by JASIM, ARIF FADHEL AHMED

“…This study concentrates specifically on conceptual skills involved in leading change in public sector organisations. Leaders’ capacity to think about abstract and complex ideas has long been acknowledged as essential to leadership tasks such as planning and analysis; however, because conceptualisation is often ambiguous and difficult to understand, many frameworks of leadership and change lack clarity on the actual significance of leaders’ conceptual skills when leading change. …”
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Control and Optimization of Membrane Biological Reactor Processes by Abachi, Noor Ali

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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. …”

Operations management of outpatient chemotherapy process: An optimization-oriented comprehensive review by Majed Hadid (17148364)

“…Therefore, outpatient chemotherapy process (OCP) optimization has attracted the attention of operations management scholars. …”

Personal Data Monetisation Strategy: Systematic Review and a Case Study of UAE by Al Falasi, Reem Jr

“…However, currently, there is a lack of a practical governmental data strategy or related processes and platforms. …”
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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

“…Lack of readiness in ministry for abrupt change that online education might cause in pedagogy and instruction surfaced, leading to strategical resistance for full-fledged online programs. …”
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