A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

“…Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. …”

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice by Turker Tuncer (16677966)

“…The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. …”

IoT-Based Sustainable Parking Lot by Binmahfooz, Abdullah

“…Moreover, a carbon emissions sensor at the gate is used to detect the generated emission rates by vehicles entering the parking lot. Furthermore, deep learning neural network was used to predict the congestion of the parking lot at any day or time. …”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV by Zulkifli, Muhammad

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Can AI Help in Screening Viral and COVID-19 Pneumonia? by Muhammad E. H. Chowdhury (14150526)

“…The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. …”

Cooperative Caching Policy in Fog Computing for Connected Vehicles by Ghazleh, Ali

“…In this thesis, we implemented cooperation between a Deep Reinforcement Learning (DRL) model and Federated Learning to improve caching in Connected Vehicles connected to fog nodes. …”
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Evolution Of Activation Functions for Neural Architecture Search by Nader, Andrew

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Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining by Mohammadjafar Hadad (21142499)

“…Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. …”

Artificial intelligence-based methods for fusion of electronic health records and imaging data by Farida Mohsen (16994682)

“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”

Single-channel speech denoising by masking the colored spectrograms by Sania Gul (18272227)

“…<p>Speech denoising (SD) covers the algorithms that remove the background noise from the target speech and thus improve its quality and intelligibility. …”

Automated skills assessment in open surgery: A scoping review by Hawa Hamza (17707224)

“…Most of the studies acquired data by capturing surgeon's hands (50 %, <i>n </i>= 20). About 35 % utilized deep learning algorithms, specifically convolutional neural networks (CNN) (<i>n </i>= 14). …”

Defense against adversarial attacks: robust and efficient compressed optimized neural networks by Insaf Kraidia (19198012)

Extreme Early Image Recognition Using Event-Based Vision by Abubakar Abubakar (18278998)

“…<p dir="ltr">While deep learning algorithms have advanced to a great extent, they are all designed for frame-based imagers that capture images at a high frame rate, which leads to a high storage requirement, heavy computations, and very high power consumption. …”

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE by Mathew, Elezabeth

“…Demand forecasting is done using deep learning techniques. Long short-term memory (LSTM) is used to find the demand forecasting of spend and quantity using time lags. …”
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A Multi-Channel Convolutional Neural Network approach to automate the citation screening process by Raymon van Dinter (10521952)

“…This study aims to automate the citation screening process using Deep Learning algorithms. With this, it is aimed to reduce the time and costs of the citation screening process and increase the precision and recall of the relevant primary studies. …”

Leveraging UAVs for Coverage in Cell-Free Vehicular Networks by Samir, Moataz

“…To address these challenges, we formulate the trajectories decisions making as a Markov decision process where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. …”
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SemIndex: Semantic-Aware Inverted Index by Chbeir, Richard

“…We also provide an extended query model and related processing algorithms with the help of SemIndex. …”
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A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”

Recursive Parameter Identification Of A Class Of Nonlinear Systems From Noisy Measurements by Emara-Shabaik, Husam

“…The model structure is made up of two linear dynamic elements separated by a nonlinear static one. The nonlinear element is assumed to be of the polynomial type with known order; The identification is based on input/output data where the output is contaminated with measurement noise. …”
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