Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams حسب Eduardo Feo-Flushing (23276023)

"…Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. …"

CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments حسب Ishaq Ansari (22047902)

"…<p dir="ltr">In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. …"

STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization حسب Muhammad Salman Khan (7202543)

"…In this paper, a novel speech separation algorithm is proposed that integrates the twin-delayed deep deterministic (TD3) policy gradient reinforcement learning (RL) agent with the expectation maximization (EM) algorithm for clustering the spatial cues of individual sources separated on azimuth. …"

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT حسب Dhananjay Bisen (19482454)

Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification حسب Rajendra Babu Chikkala (22330876)

"…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …"

GenDE: A CRF-Based Data Extractor حسب Kayed, Mohammed

"…Moreover, the system solves the problem of automatic data extraction from modern JavaScript sites in which data/schema are attached (on the client side) in a JSON format. …"
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Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks حسب Hassan, Yasmin Adel

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MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network حسب Sakib Mahmud (15302404)

"…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …"

Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction حسب Syed Mohammad (21075689)

"…It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. …"

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study حسب Ayman Hassan (14426412)

"…The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.…"

Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation حسب Zakaria Shams Siam (22048001)

Use data Mining Techniques to Predict Users’ Engagement on the Social Network Posts in The Period Before, During and After Ramadan حسب AL RAWASHDEH, HANEEN MOHAMMAD

"…Analyzing the content, type and the best time of posting can effectively impact and benefit a business. Data mining (also referred to as Data Knowledge Discovery) is the practice of examining a dataset to establish the hidden patterns and knowledge in order to represent the results in an understandable format. …"
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Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach حسب Arshad Ali Khan (23152516)

"…To mitigate overfitting, we implemented dropout layers, batch normalization, and early stopping, significantly enhancing the model’s generalization capability. Specifically, three different open-access datasets were combined into a single training dataset, capturing extensive temporal, spatial, and environmental variability. …"

Geographical Area Network—Structural Health Monitoring Utility Computing Model حسب Hasan Tariq (18131842)

"…Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. …"

Large-scale annotation dataset for fetal head biometry in ultrasound images حسب Mahmood Alzubaidi (15740693)

"…This broad range of formats ensures adaptability for various computer vision tasks, such as classification, segmentation, and object detection. …"

Performance comparison of variable-stepsize IMEX SBDF methods on advection-diffusion-reaction models حسب Raed Ali Mara'Beh (17337892)

"…<p>Advection-diffusion-reaction (ADR) models describe transport mechanisms in fluid or solid media. …"

Performance comparison of variable-stepsize IMEX SBDF methods on advection-diffusion-reaction models حسب Raed Ali Ayesh Marabeh (21142247)

"…<p dir="ltr">Advection-diffusion-reaction (ADR) models describe transport mechanisms in fluid or solid media. …"

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends حسب Abdellatif M. Sadeq (16931841)

"…In addition, the possible minimum and maximum values of responses at the corresponding operating parameters are found using a genetic algorithm (GA) approach. Model 1 could capture the computational fluid dynamics (CFD) outputs with high precision at different flame radiuses and time instants with a maximum absolute error percentage of 5.46%. …"

An Alternating Projection Framework for Elementwise Masked Nonlinear Matrix Decomposition حسب Kulathil, Muhammed Noshin Poovan

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Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis حسب Sami Elzeiny (16891521)

"…The developed model successfully identified stress instances using IBI-BVP spatial domain images with an average accuracy of 98.10% with a convolutional neural network (CNN) and 99.18% using the average pixel intensity of these images with the extra trees classifier. …"