The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation by Getahun, Fekade

“…We particularly discuss multimedia primary horizontal fragmentation and focus on semantic-based textual predicates implication required as a pre-process in current fragmentation algorithms in order to partition multimedia data efficiently. …”
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STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization by Muhammad Salman Khan (7202543)

“…For stationary sources, the proposed system gives satisfactory performance in terms of quality, intelligibility, and separation speed, and generalizes well with the test data from a mismatched speech corpus. Its perceptual evaluation of speech quality (PESQ) score is 0.55 points better than a self-supervised learning (SSL) model and almost equivalent to the diffusion models at computational cost and training data which is many folds lesser than required by these algorithms. …”

Using artificial intelligence to improve body iron quantification: A scoping review by Abdulqadir J. Nashwan (11659453)

“…The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. …”

A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks by Deriche, Mohamed

“…Finally, a Multilayer Feedforward Neural Network (MFNN) with a backpropagation (BP) algorithm is used for character recognition. We discuss new features from the characters for training the NN. …”
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Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants by Hamad Al Shamsi, Jawaher

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LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications by Aldamani, Raghad

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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
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Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting by Abdelkader Baggag (16864140)

“…The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. …”

Prediction the performance of multistage moving bed biological process using artificial neural network (ANN) by Fares Almomani (12585685)

“…The effect of surface area loading rate (SALR), organic matters (OMs), nutrients (N & P), feed flow rate (Q_feed), hydraulic retention time (HRT), and internal recycle flow (IRF) on the performance of the ENR-BP to fulfil rigorous discharge limitations were evaluated. Experimental data was used to develop the appropriate architecture for the AAN using iterative steps of training and testing. …”

Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks by Sharma, Neetan

“…This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. …”
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Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization by Yassine Himeur (14158821)

“…While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. …”

Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM) by AL SHOUBAKI, HAMZA YOUNIS

“…We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. …”
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Information warfare. (c2015) by Sai, Mohamed El

“…Numerous damage assessment and recovery algorithms have been proposed by researchers. In this work we present an efficient lightweight detection and recovery algorithm that is based on the matrix approach and that can be used to recover from malicious attacks. …”
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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)

“…Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i.</p><h3>Conclusion</h3><p dir="ltr">XGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.…”

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles by Wadi, Ali

“…The proposed algorithms are initially deployed in a simulation environment, and then the experimental data sets are fed into the algorithms to validate their performance. …”
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An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure by Fahmy, Ahmed

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A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network by KHAN, FIROZ

“…The data is finally classified as either ransomware or goodware using the learning methodologies. …”
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Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by Sakib Mahmud (15302404)

“…In this study, we introduce the multimodal feature fusion for non-intrusive occupancy monitoring (MMF-NIOM) framework, which leverages both classical and deep machine learning algorithms to achieve state-of-the-art occupancy detection performance using smart meter data. …”

Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique by Iqbal Hassan (22155274)

“…In this study, we harnessed the capabilities of a four-channel, wearable EEG device that captured brain activity data during two distinct CL states: Baseline (representing a non-CL, resting state) and the Stroop Test (a CL-inducing state). …”

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

“…Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). …”