A Machine Learning Approach to Predicting Diabetes Complications by Jian, Yazan Khaled

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Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases by Muhammad Ali Muzammil (17910611)

“…To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. …”

Exploring the System Dynamics of Covid-19 in Emergency Medical Services by Ali, Muhammad

“…The predictive analysis yielded a model of response times for emergency missions through machine learning, specifically using a random forest algorithm. …”
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Multigrid solvers in reconfigurable hardware by Haraty, Ramzi A.

“…We compare our findings with a C++ version of each algorithm. The obtained results show better performance when compared to existing software versions.…”
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The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”

Precision nutrition: A systematic literature review by Daniel Kirk (17302798)

“…Models were through the use of four groups of approaches and 23 evaluation metrics. …”

Vehicular-OBUs-As-On-Demand-Fogs by Sami, Hani

“…Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. …”
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A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT by Harun Surej Ilango (17545728)

“…The performance of the models is measured using the metrics accuracy, precision, recall, F1 score, detection time per flow, and ROC curves. …”

Machine Learning-Based Approach for EV Charging Behavior by Shahriar, Sakib

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Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea by Issa, Leila

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

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Prototype project management tool (PPMT) with cocomo calibration. (c1998) by Haddad, Ibrahim F.

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Optimal supercharge scheduling of electric vehicles by Atallah, Ribal F.

“…Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. …”
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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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PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits by Hind Almerekhi (7434776)

“…Before finding toxicity triggers, we built and evaluated various machine learning models to detect toxicity from Reddit comments. Subsequently, we used our best-performing model, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model that achieved an area under the receiver operating characteristic curve (AUC) score of 0.983 to detect toxicity. …”

Optimal Routing Protocol in Multimedia Wireless Sensor Networks by Al-Zurba, Hiba

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Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review by Loay A. Salman (14150322)

“…All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.…”

A geographic information system method to generate long term regional solar radiation resource maps: enhancing decision-making by Sachin Jain (19161721)

“…Digital surface model data is used as an auxiliary variable to further scale down to 30 m resolution. …”

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions by Chakarajamula Hussaian Basha (21633167)

“…The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. …”

Neural network-based failure rate prediction for De Havilland Dash-8 tires by Al-Garni, Ahmed Z.

“…An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the twolayered feed-forward back-propagation algorithm as a learning rule is developed. …”
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