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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Process Mining over Unordered Event Streams by Awad, Ahmed

“…Specifically, we formalize the notion of out-of-order arrival of events, where an online analysis algorithm needs to process events in an order different from their generation. …”
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Fleet sizing of trucks for an inter-facility material handling system using closed queueing networks by Mohamed Amjath (17542512)

“…This study proposes an analytical method based on sequential quadratic programming (SQP) methodology coupled with a mean value analysis (MVA) algorithm to solve this NP-Hard problem. Furthermore, a discrete event simulation (DES) model is developed to validate the optimisation of non-dominant solutions. …”

Vibration suppression in a cantilever beam using a string-type vibration absorber by Issa, Jimmy S.

“…The finite element method is used to model the system and a reduced order model is obtained through modal reduction performed on both the string and the beam. …”
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Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation by Hariprasath Manoharan (14157966)

“…The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.…”

Active distribution network type identification method of high proportion new energy power system based on source-load matching by Qinlin, Shi

“…Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. …”
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Portable Electromagnetic Surface Profiling System by Al Homssi, Bassel Mohamad

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Joint computing, communication and cost-aware task offloading in D2D-enabled Het-MEC by Abbas, Nadine

“…Furthermore, we propose a low-complexity algorithm that generates high performance results and can be applied for large-scale networks. …”
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SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data by Tekli, Joe

“…Various weighting functions and a parallelized search algorithm have been developed for that purpose and are presented here. …”
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Social Network Analysis for Precise Friend Suggestion for Twitter by Associating Multiple Networks Using ML by Dharmendra Kumar Singh Singh (19517710)

“…<p dir="ltr">The main aim in this paper is to create a friend suggestion algorithm that can be used to recommend new friends to a user on Twitter when their existing friends and other details are given. …”

DAP: A dataset-agnostic predictor of neural network performance by Sui Paul Ang (18460605)

“…In this work, we investigate the feasibility of two tasks: (i) predicting a deep neural network’s performance accurately given only its architectural descriptor, and (ii) generalizing the predictor across different datasets without re-training. To this end, we propose a dataset-agnostic regression framework that uses a novel dual-LSTM model and a new dataset difficulty feature. …”

Energy-aware adaptive compression for mobile devices. (c2009) by Maddah, Rakan

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Application of Data Mining to Predict and Diagnose Diabetic Retinopathy by Haniyeh, Maryam

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Random vector functional link network: Recent developments, applications, and future directions by A.K. Malik (16003193)

“…Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. …”

KNNOR: An oversampling technique for imbalanced datasets by Ashhadul Islam (16869981)

“…However, if the training data is not balanced among different classes, the performance of ML models deteriorate heavily. …”

Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

“…FR codes consist of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. We model the problem as an integer linear programming problem that uses modified versions of the fractional repetition code by allowing different block sizes, and minimizes the recovery cost of all single node failure scenarios. …”
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Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. …”

Low Cost Autopilot Design Using Fuzzy Supervisory Control by Hadi, Mahmoud

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Thermodynamic Analysis and Optimization of Densely-Packed Receiver Assembly Components in High-Concentration CPVT Solar Collectors by Sharaf, Omar Z.

“…These components, namely multi-junction photovoltaic cells, segmented thermoelectric generators with interconnectors, and finned minichannel heat extractors, could be integrated to form CPVT receiver assemblies in a number of different configurations. Thermodynamic component-level analyses that avoid oversimplified as well as computationally-expensive modeling approaches and provide clear and robust simulation algorithms with reasonable accuracy are separately developed for the three addressed components. …”
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Defense against adversarial attacks: robust and efficient compressed optimized neural networks by Insaf Kraidia (19198012)

“…This enhancement significantly fortifies the model's resistance to adversarial attacks by introducing complexity into attackers' attempts to anticipate the model's prediction integration process. …”