Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…</p><h3>Objective</h3><p dir="ltr">This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters.…”

Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges by Haseeb Javed (18576340)

I Will Survive: An Event-driven Conformance Checking Approach Over Process Streams by Raun, Kristo

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A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting by Maissa Boukaf (22282279)

“…The digitalization of building energy forecasting systems, enhanced by Energy Digital Twin technology alongside IoT devices and advanced data-driven algorithms, offers substantial improvements in energy management and optimization, servicing, maintenance, and energy-efficient design. …”

Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur (712349)

“…The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. …”

Social spider optimization algorithm: survey and new applications by Abualigah, Laith

“…Based on gender, evolutionary factors simulate different behaviors usually found at their settlement based on the biological aspect. This survey studied the SSO and compared it with other PSO algorithms to find the best-performing algorithm based on a benchmark. …”
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A neural networks algorithm for data path synthesis by Harmanani, Haidar M.

“…The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. …”
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Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands by Peng, Wang

“…In order to mitigate the charging effects of electric vehicles on the hybrid AC–DC microgrid operation, some remotely switches are considered in the system which make it possible for changing the topology and power flow way. In order to model the uncertainty effects, a data-driven framework based on point estimate method and support vector machine is developed. …”
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Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions by Alaa Abd-alrazaq (17058018)

“…As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)–driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. …”

Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval by Mohammed Tahar Habib Kaib (21633176)

“…Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. …”

Optimization and control of solar-wind islanded hybrid microgrid by using heuristic and deterministic optimization algorithms and fuzzy logic controller by Sk. A., Shezan

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

“…Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. …”

Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. …”
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Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

“…As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. …”
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AI-based remaining useful life prediction and modelling of seawater desalination membranes by Al Ali, Fajer

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Modelling surface currents in the Eastern Levantine Mediterranean using surface drifters and satellite altimetry by Issa, Leila

“…The method relies on a variational assimilation approach where a velocity correction is continuously obtained by matching observed drifter positions with those predicted by a simple advection model. The background velocity used in the advection of the drifters is the aggregate of a geostrophic and a wind-driven component and the velocity correction is constrained to be divergence free. …”
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Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning by Zeinab, Samira Jihad

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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

“…<h3 dir="ltr">Purpose</h3><p dir="ltr">This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. The purpose of this work is to provide a systematic comparison of various nature-inspired algorithms across both low-cost analytical and high-fidelity simulation-driven design problems.…”

Performance Modeling of Rooftop PV Systems in Arid Climate, a Case Study for Qatar: Impact of Soiling Losses and Albedo Using PVsyst and SAM by Sachin Jain (19161721)

“…To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. …”