Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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Impact Of Inspection Errors On The Performance Measures Of A General: Repeat Inspection Plan by Duffuaa, S. O.

“…The impact of the errors is studied by conducting sensitivity analysis on the errors utilizing computer software which implements an algorithm that determines the optimal parameters of the model of the plan. …”
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Mobile Energy Storage Systems for Benefit Maximization in Resilient Smart Grids by Alshaal, Dima

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An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition by Abboud, Ralph

“…Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. …”
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A lightweight adaptive compression scheme for energy-efficient mobile-to-mobile file sharing applications by Sharafeddine, Sanaa

“…Moreover, we derive an empirical energy model that analytically quantifies the energy consumed during data transmission as a function of the signal strength level and during data compression as a function of the data size. …”
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Approximate XML structure validation based on document–grammar tree similarity by Tekli, Joe

“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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Image-CNN Based Process Control of Profile by Zeinab, Zeinab Jihad

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Approximate XML structure validation technical report by Tekli, Joe

“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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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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Application of Data Mining to Predict and Diagnose Diabetic Retinopathy by Haniyeh, Maryam

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Reinforcement Learning-Based School Energy Management System by Yassine Chemingui (18891757)

“…The performance is evaluated on a school model simulated environment considering thermal comfort, CO2 levels, and energy consumption. …”

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.…”

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

“…Detection tasks (accuracy 96.1 %, sensitivity 98.5 %) outperformed subtyping tasks (accuracy 87.3 %, sensitivity 91.3 %). Models analyzing images at the architectural level yielded higher accuracy (94.7 %), sensitivity (94.1 %), and specificity (98.2 %) compared to cellular-level analysis. …”

Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes by Muhammad Mohsin Khan (22150360)

“…The dataset comprised 535 samples across seven MRS severity levels and was validated using 5-fold cross-validation and diverse subgroups to ensure robust model performance across various scenarios. …”

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

“…To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. …”

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

“…Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. …”

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. …”

An efficient approach for textual data classification using deep learning by Abdullah Alqahtani (7128143)

“…Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. …”