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Sulfur oxidative coupling of methane process development and its modeling via machine learning
Published 2022“…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. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
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Heart Disease Prediction using Machine Learning and Deep Learning Approaches
Published 2025“…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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MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model
Published 2025“…We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand forecasting. …”
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Using Mobile Technology for Coordinating Educational Plans and Supporting Decision Making Through Reinforcement Learning in Inclusive Settings
Published 2021“…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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An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study
Published 2024“…</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. …”
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Comparative Study of Deep Learning Models Versus Machine Learning Models for Wind Turbine Intelligent Health Diagnosis Systems
Published 2023“…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
Published 2022“…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. …”
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Hate speech detection with ADHAR: a multi-dialectal hate speech corpus in Arabic
Published 2024“…We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. …”
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Positive Unlabelled Learning to Recognize Dishes as Named Entity
Published 2019“…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
Published 2023“…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
Published 2018“…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
Published 2016“…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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Exploring Second Language Writing Steps and Strategies that EFL Students with Different Language Proficiency Levels Used in the Writing Process
Published 2016“…Good language learners know how to write step-by-step and use several writing strategies, which both can be evident in their writing process. The study also revealed that any lack in the writing steps and strategies can negatively influence the writing process of L2 participants. …”
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Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques
Published 2023“…: Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. …”
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Improving Quality and Operational Performance of Service Organizations: An Empirical Analysis Using Repeated Cross-Sectional Data of U.S. Firms
Published 2022“…In addition, whether quality management practices can provide sustainable quality results for service organizations is overlooked in the literature, primarily due to the lack of availability of reliable and valid data. …”