Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar (16904526)

“…<p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”

Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…Hence, detecting any act of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. …”
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Automated systems for diagnosis of dysgraphia in children: a survey and novel framework by Jayakanth Kunhoth (14158908)

“…The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. …”

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review by Chakraborty, Sanjoy

“…Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. …”
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Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter by Yahia, Mohamed

“…The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability that calls for efficient speckle filtering algorithms to preprocess radar imagery. Since the introduction of the Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. …”
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Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review by Daksh Dave (17949239)

“…Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. …”

FoGMatch by Arisdakessian, Sarhad

“…In this context, the notion of fog computing has been projected to furnish data analytics and decision-making closer to the IoT devices. …”
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Classification automatique et stratégie d'annotation appliquées à un concept philosophique: la dimension psychologique du concept de LANGAGE dans l'oeuvre de Bergson 1 by Alrahabi, Al Moatasem

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Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study by Muhammad Atif Butt (10849980)

“…Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. …”

Predicting Android Malware Using Evolution Networks by Chahine, Joy

“…This issue requires the development of efficient solutions in order to keep up with the continuous evolution of malware. …”
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Iterative Methods for the Solution of a Steady State Biofilter Model by Salih, Mubarak Sahal

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Decision-level fusion for single-view gait recognition with various carrying and clothing conditions by Al-Tayyan, Amer

“…Such a system is needed in access control applications whereby a single view is imposed by the system setup. The gait data is firstly processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two new gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). …”
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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects by Mohamed Massaoudi (16888710)

“…This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. …”

Using artificial bee colony to optimize software quality estimation models. (c2015) by Abou Assi, Tatiana Antoine

“…We validate our technique on data describing maintainability and reliability of classes in an Object-Oriented system. …”
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Cardiovascular health research priorities in the United Arab Emirates by Ghader, Nariman

“…The top research priority areas were: development of evidence-based, customized algorithms for CVD prevention and in-hospital emergency interventions; the availability, accessibility, and affordability of CVD treatment and rehabilitation; identification of relationships between CVDs, lifestyle factors, and mental health; efficacy and constraints in the management of cardiac emergencies; and epidemiological studies that trace CVD in the UAE. …”
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Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning by Zeinab, Samira Jihad

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Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging by Maricar, Mohammed Farook

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Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices by Aya Hasan Alkhereibi (17151070)

“…This study aims to classify the highest 50 global smart cities based on key livability and technology indices, using advanced <u>machine learning</u> (ML) models to assess city performance comprehensively. …”

A comparative analysis to forecast carbon dioxide emissions by Md. Omer Faruque (17545671)

“…This leads to the second step, which involves formulating the multivariate time series CO₂ emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). …”

Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning by Far, Reza Davoodi

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