An Infrastructure-Assisted Workload Scheduling for Computational Resources Exploitation in the Fog-Enabled Vehicular Network by Sorkhoh, Ibrahim

“…A Dantzig–Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solve...…”
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Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier by Syed Ali Jafar Zaidi (19563178)

“…<div><p>Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. …”

Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context by SALIM, MAHA JAWDAT

“…These platforms enhance academic performance by fostering collaborative learning environments and generating extensive data from every user interaction. Machine learning algorithms can process large and complex datasets to identify patterns and trends that may not be immediately apparent. …”
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A hybrid approach for XML similarity by Tekli, Joe

“…Various algorithms for comparing hierarchically structured data, e.g. …”
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Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization by Yassine Himeur (14158821)

“…Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. …”

Exploring Digital Competitiveness through Bayesian Belief Networks by Qazi, Abroon

“…The methodology involves constructing BBN models using data from the IMD Digital Competitiveness Ranking 2023 for 64 countries. …”
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Reconstruction and simulation of neocortical microcircuitry by Khazen, Georges

“…The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. …”
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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series by Ahmed Ben Said (14158926)

“…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …”

CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth, Kunhoth

“…Among the task-specific models, the SVM trained on word data achieved the highest accuracy of 91.7%. …”
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Anonymizing multimedia documents by Haraty, Ramzi

“…We provide a sanitizing MD∗-algorithm to enforce de-linkability along with a utility function to evaluate the utility of multimedia documents that is preserved after the sanitizing process. …”
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Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics.…”

Structural alignment. (c2006) by Kazma, Layal

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Active distribution network type identification method of high proportion new energy power system based on source-load matching by Qinlin, Shi

“…Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. …”
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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth Kunhoth (14158908)

“…Among the task-specific models, the SVM trained on word data achieved the highest accuracy of 91.7%. …”

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

“…We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. …”

An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation by Abdul Rahman, Sawsan

“…Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants' selection, and a deductive rule-based model for traffic condition estimation. …”
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FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

“…The performed experiments show that 60% of flow packets are a good compromise that ensures high performance in the least processing time. The results of the conducted experiments indicate that random forest outperforms other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. …”
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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

“…The model’s hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). …”

Generic metadata representation framework for social-based event detection, description, and linkage by Abebe, Minale A.

“…SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. …”
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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review by Alaa Abd-alrazaq (17058018)

“…The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. …”