Estimating Homophily in Social Networks Using Dyadic Predictions by George Berry (5807582)

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Academic networking. (c2012) by Jammal, Belal

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An ANP Approach for Selecting a Sustainable Learning Technology Intervention for Development by Raji, Maryam

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A Thesis in the Use of Computer Assisted Language Learning in a Whole Language Contest by Ward, Jason

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A lightweight neural network with multiscale feature enhancement for liver CT segmentation by Mohammed Yusuf Ansari (16904523)

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning by Mohamed Massaoudi (16888710)

“…This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. …”

Formal Verification- and AI/ML-Assisted Radio Resource Allocation for Open RAN Compliant 5G/6G Networks by Tariq Mumtaz (10861635)

“…By leveraging the Open Radio Access Network (RAN) architecture, the framework enables flexible and efficient management of radio resources to meet the competing demands of services offered by the fifth/sixth generation (5G/6G) of wireless networks. …”

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

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Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting by Panja, Madhurima

“…While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. …”
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Low-Light Homomorphic Filtering Network for integrating image enhancement and classification by Al Sobbahi, Rayan

“…In this paper, we propose a new LLI enhancement model titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning and is designed for seamless integration into classification models. …”
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When geoscience meets generative AI and large language models: Foundations, trends, and future challenges by Hadid, Abdenour

“…This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. …”
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A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification by Md. Mosarrof Hossen (21401603)

“…In this study, we present RWC-Net (recyclable waste classification network), a novel deep learning model designed for the classification of six distinct waste categories using the TrashNet dataset of 2,527 images of waste. …”

Artificial neural networks for predicting the performance of novice CAD users based on their profiled technical attributes by Ammouri, A.H.

“…This paper utilizes Artificial Neural Networks (ANN) to forecast the mechanical CAD performance of novice trainees involved in formal training. …”
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Deep learning for brain electron microscopy segmentation: Advances, challenges, and future directions in connectomics and ultrastructure analysis by Uzair Shah (15740699)

Fluoroscopic 3D Images Generation Using A GAN Method by Alshrbaji, Mohammad Nabeel Mohammad

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An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control by Mahmoud Dahmani (18810247)

Spectrum Occupancy Measurements and Cognitive Radio System Implementation by Kiftaro, Firas Ahmed

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COVID-19 infection localization and severity grading from chest X-ray images by Anas M. Tahir (16870077)

“…An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). …”

Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration by Snigdha Mohanty (16904919)

Hydrogen Sulfide (H₂S) Sensor: A Concept of Physical Versus Virtual Sensing by Ahmed Alsarraj (16876014)

“…The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H₂S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H₂S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H₂S; and 5) the proposed model is a highly sensitive and reliable H₂S gas sensing scheme with the ability to detect the gas instantaneously. …”