A cluster-based model for QoS-OLSR protocol by Otrok, Hadi

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Modeling and Control of a Thermally Driven MEMS Actuator for RF Applications by Estahbanati, Shahabeddin Vamegh

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SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data by Tekli, Joe

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Optimizing clopidogrel dose response by Saab, Yolande B.

“…The aim of the study is to investigate the cumulative effect of CYP2C19 gene polymorphisms and drug interactions that affects clopidogrel dosing, and apply it into a new clinical-pharmacogenetic algorithm that can be used by clinicians in optimizing clopidogrel-based treatment. …”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida (17947847)

“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”

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

“…The proposed scheme monitors the signal strength level during the file transfer process and compresses data blocks on-the-fly only whenever energy reduction gain is expected. …”
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A Robust Deep Learning Approach for Distribution System State Estimation with Distributed Generation by Kfouri, Ronald

“…Conventional methods, which are used to solve state estimation on the transmission level, require the grid to be observable. …”
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Assessment of Inventory and Transportation Collaboration in a Logistics Marketplace by Shahid, Saif Mohammad

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AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics by Naila Rabbani (291722)

“…AGEomics biomarkers have been used in diagnostic algorithms using machine learning methods. …”

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. The value in building a predictive model of response time lies in identifying the most influential predictors of response times such as team utilization, case severity, COVID-19 patients, and roadway distance. …”
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Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes by Muhammad Mohsin Khan (22150360)

“…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”

CAD TOOL FOR THE AUTOMATIC-GENERATION OF MICROPROGRAMS by Sait, Sadiq M.

“…Abstract A methodology for automatic synthesis of microprograms for digital systems modeled in the UAHPL register-transfer-level language is described. The algorithms used in the process of translation from UAHPL description to microprograms are also discussed. …”
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Virtual topologies for massively parallel computations. (c2015) by Jahed, Karim A.

“…Massively parallel computations that are based on such algorithms often suffer from a large communication overhead due to the exponential growth in the number of tasks generated at each search-tree level. …”
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The architecture of a highly reconfigurable RISC dataflow array processor by Sait, Sadiq M.

“…The array can be programmed to execute arbitrary algorithms in both static and dynamic manner. The processor array is modelled at the behavioural level in VHDl. …”
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Dynamic multiple node failure recovery in distributed storage systems by Itani, May

“…Our daily lives are getting more and more dependent on data centers and distributed storage systems in general, whether at the business or at the personal level. With the advent of fog computing, personal mobile devices in a given geographical area may also comprise a very dynamic distributed storage system. …”
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MoveSchedule by Zouein, Pierette

“…The layout construction algorithm that underlies MoveSchedule uses Constraint Satisfaction to find the set of all positions that meet the constraints on resources' positions and Linear Programming to find the optimal positions that minimize resource transportation and relocation costs. …”
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Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review by Loay A. Salman (14150322)

“…</p><h3>Conclusion</h3><p dir="ltr">This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. …”

Software defect prediction. (c2019) by Moussa, Rebecca

“…One that focuses on predicting defect in software modules using a hybrid heuristic - a combination of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). …”
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Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review by Hiba Alzoubi (18001609)

“…Models analyzing images at the architectural level yielded higher accuracy (94.7 %), sensitivity (94.1 %), and specificity (98.2 %) compared to cellular-level analysis. …”