Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort by Mohamed Adil Shah Khoodoruth (14589828)

“…Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. The Random Forest model, a supervised machine learning algorithm, efficiently differentiated between cases and controls and between TRS and NTRS, with accuracies of 86.87 % and 88.41 %, respectively. …”

Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS by Iman Faridmehr (14150616)

“…<p>This study investigates a Novel Hybrid Informational model for the prediction of creep and shrinkage deflection of reinforced concrete (RC) beams containing different percentages of ground granulated blast furnace slag (GGBFS) at different ages, varying from 1 to 150 days. …”

Stochastic Geometry-Based Model for Dynamic Allocation of Metering Equipment in Spatio-Temporal Expanding Power Grids by Atat, Rachad

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Investigation of Forming a Framework to shortlist contractors in the tendering phase by DABASH, MOHANNAD SALAH

“…The model to shortlist contractors in the tendering phase was created using machine learning to enable more contractors to submit for a project without having to waste time and money on the tendering process; if they are compatible with the project, then they have a high chance of getting it by being short-listed for the project, which they can then submit their tender package for; this will also ensure that the best company gets the job for the client which will act as a great step towards improving the tendering in construction projects. …”
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Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control by Farhat Mahmood (15468854)

“…The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. …”

Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review by Hiba Alzoubi (18001609)

Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine by Shomope, Ibrahim

“…The type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. …”
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Correlation Clustering with Overlaps by Fakhereldine, Amin

“…In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. …”

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

“…<p dir="ltr">Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. …”

A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks by Sakib Mahmud (15302404)

“…We present a structured taxonomy covering value-based, policy-based, actor-critic, model-based, and advanced multi-agent and multi-objective approaches, and link algorithms to tasks such as dispatch, microgrid coordination, real-time pricing, load balancing, and demand–response. …”

Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…</p><h3>Methods</h3><p dir="ltr">Using Python® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. …”

Multi-Objective Task Allocation Via Multi-Agent Coalition Formation by Amer, Noha Tarek

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QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning by Md Nazmul Islam Shuzan (21842426)

“…We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. …”

Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series So... by Ashwin Muniyappan (19570051)

“…Finally, our proposed model validated (from real life data) the highly affected five states of COVID-19 and the Omicron variant. …”

Fragment based protein structure prediction. (c2013) by Terzian, Meghrig Ohanes

“…Backbone fragments selections extracted from the Robetta server are followed by side chain selections, extracted from the Dunbrack Library. …”
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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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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts by ALSHAMSI, SUROUR

“…In addition, it implies carrying out the training and testing process in each phase. Since the best model is obtained from training, each time it is performed for a given phase, the model is adjusted to detect new attacks. …”
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Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data by Arfan Ahmed (17541309)

“…</p><h3>Results</h3><p dir="ltr">We were able to estimate with high accuracy (RMSE range: 0.099 to 0.197) the relationship between glycemic metrics and features that can be derived from non-invasive WDs when utilizing AI models.</p><h3>Conclusion</h3><p dir="ltr">We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics.…”