Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis by Hassan, Ali

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Label dependency modeling in Multi-Label Naïve Bayes through input space expansion by PKA Chitra (21749216)

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Machine Learning Model for a Sustainable Drilling Process by Alsaidi, Ola

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Predicting COVID-19 in the UAE Using Machine Learning by Sankalpa, Donthi

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Exploring Machine Learning Models to Predict Harmonized System Code by AL Taheri, Fatma

“…In this study, six machine learning-based models have been implemented to determine the ability of detecting the HS Code based on the user’s input description, where the highest achieved accuracy is 76.3% using linear support vector machine model.…”
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A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks by Kanthavel Radhakrishnan (17542101)

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Low-Complexity Machine Learning-based Behavioral Modeling of Power Amplifiers by Aziz, Mohammad Rabih

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Using Machine Learning Models for Inpatient Bed Demand Forecasting in the UAE Hospitals by Sheiko, Rahaf Abdulla

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Developing a UAE-Based Disputes Prediction Model using Machine Learning by Abu Laila, Ibrahim Wasef Subhi

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Machine learning-driven identification and predictive mapping of clogging regimes in porous media by Ahmed Elrahmani (17128837)

“…This study develops a unified, machine learning–based framework to identify, characterize, and predict clogging behavior using dimensionless parameters representing pore structure, hydrodynamics, and particle–surface interactions. …”

Exploring machine learning approaches for biohydrogen production through dark fermentation in wastewater by Ibrahim Shomope (22928773)

“…However, optimizing biohydrogen yields remains challenging due to the complexity of biological interactions and environmental factors. Machine learning (ML) offers a data-driven approach to predict and enhance hydrogen production efficiency. …”

Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models by Abu Zitar, Raed

“…The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. …”
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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. …”

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions by Iftikhar Ahmad (2793085)

“…<p dir="ltr">Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. …”

Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

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Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning by Ahmad K. Sleiti (15955149)

“…<p dir="ltr">This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). …”

Learning-Based Spectrum Sensing and Access for Cognitive Radio Systems by Shehabeldin, Menatalla Diaaeldin

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Machine learning-aided prediction of COD removal in the electrocoagulation process using a super learner model by Mhd Taisir Albaba (20601071)

“…<p dir="ltr">A new predictive machine learning stacking model was developed to examine chemical oxygen demand (COD) removal efficiency in electrocoagulation. …”

Temporal Machine Learning Payload Prediction for DJI Matrice 100 Quadcopter Drone Based on Tracking Data by Kashkash, Mariam

“…The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. …”
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