Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier by Yassine Himeur (14158821)

“…Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …”

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models by Osama Bassam J. Rabie (21323741)

“…The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. …”

Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol by Santiago Hors-Fraile (5950823)

“…Patients’ feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. …”

Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model by Ramesh Sundar (19326046)

“…The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. …”

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

“…<p>Different aggregation levels of the electric grid's big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. …”

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

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

A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

“…Short-term CGM time-series data are processed using both novel image transformation approaches, as well as using conventional signal processing methods. …”

An integrated partitioning and synthesis system for dynamically reconfigurable Multi-FPGA architectures by Ouaiss, Iyad

“…This paper presents an overview of SPARCS and the various algorithms used in the system, along with a brief description of how a JPEG-like image compression algorithm is mapped to a Multi-FPGA board using SPARCS.…”
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Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

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Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

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Indoor Localization Schemes for Mobile Robots by Al-Jarrah, Islam Mohammad-Amin

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Lung nodule classification utilizing support vector machines by Mousa, W.A.H.

“…The SVM classifier is trained with features extracted from 30 nodule images and 20 non-nodule images, and is tested with features out of 16 nodule/non-nodule images. …”
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An effective design system for dynamically reconfigurable architectures by Ouaiss, I.

“…In this paper, we use the Joint Photographic Experts Group (JPEG) image compression algorithm as a design example to demonstrate the effectiveness of dynamic reconfiguration achieved using SPARCS. …”
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An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System by Alkhatib, Osama

“…Historical data of Dubai Toll Collection System is utilized to investigate multiple machine learning algorithms. Experiment is performed using Azure Machine Learning (ML) platform to test and assess the most efficient model that would predict the failure of system elements and predict the abnormality of the operation. …”
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Learning-Based Spectrum Sensing and Access for Cognitive Radio Systems by Shehabeldin, Menatalla Diaaeldin

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Comparative Study of Control Strategies for Under Actuated Manipulator by Abed, Joudeh Yasin

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Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d... by Tarik Elhadd (5480393)

“…The optimal XGBoost model prioritized age, gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i.…”

Rate Adaptation in Dynamic Adaptive Video Streaming Over HTTP by Abdelhafez, Nada

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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir (19170898)

“…A key requirement in these applications is minimizing the latency of data processing, particularly for time-sensitive tasks like image classification of IIoT device data. …”

A survey and comparison of wormhole routing techniques in a meshnetworks by Al-Tawil, K.M.

“…Although an extremely wide number of routing algorithms have been proposed and implemented in hardware and software, it is difficult for the designer of a multicomputer to choose the best routing algorithm given a particular architectural configuration. …”
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