Efficient Approximate Conformance Checking Using Trie Data Structures by Awad, Ahmed

Subjects: “…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…”
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Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models by Asan G. A. Muthalif (16888818)

“…The validation of the algorithm is attained by comparing the resulting modified Bouc-Wen model behaviour using PSO against the same model's behaviour, identified using Genetic Algorithm (GA). …”

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms by Arafat Rahman (8065562)

“…A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. …”

Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique by J. Jayapradha (21323774)

Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms by Almahmood, Mothanna

“…Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. …”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

“…Distributed clustering algorithms, however, can fulfil this gap. They extract a classification model from the distributed objects even when they are in different sites and locations. …”
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A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model by Md. Nazmul Islam Shuzan (16888827)

“…Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. …”

Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain by Jun Zhu (84054)

“…In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012–31 March 2014 at three sites. …”

Variable Selection in Data Analysis: A Synthetic Data Toolkit by Mitra, Rohan

“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Capturing outline of fonts using genetic algorithm and splines by Sarfraz, M.

“…In order to obtain a good spline model from large measurement data, we frequently have to deal with knots as variables, which becomes a continuous, non-linear and multivariate optimization problem with many local optima. …”
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Using machine learning algorithm for detection of cyber-attacks in cyber physical systems by Almajed, Rasha

“…We have suggested the SFL-HMM together with HMS-ACO process as a method used for detection of the cyber attacks. A MATLAB simulation used to evaluate the new strategy, and the metrics obtained from that simulation are compared to those obtained from the older methods. …”
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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study by Mutasim Baba, Fuad

“…Maximum Absolute Difference (MAD), a new metric, that calculates the maximum absolute difference between simulated and measured hourly indoor temperatures, Root Mean Square Error (RMSE), Normalized Mean Bias Error (NMBE) were used as the evaluation criteria. Another new metric is introduced, 1 ◦C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 ◦C during the cali bration period, to select the best solutions from the Pareto Front solutions. 0.5 ◦C Percentage Error criterion is also used for the level of accuracy the model can achieve. …”
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An ant colony optimization algorithm to improve software quality prediction models by Azar, D.

“…Nonetheless, they can be derived from other measurable attributes. For this purpose, software quality prediction models have been extensively used. …”
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Predicting Dropouts among a Homogeneous Population using a Data Mining Approach by BILQUISE, GHAZALA

“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008) by Korkmaz, Rita

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Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

“…However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. …”

Improved scatter search algorithm for predicting all-atoms protein structures using charmm22 energy model. (c2010) by Ghalayini, Iman Adnan

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Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? by Saravanan Thirumuruganathan (11038038)

“…The resulting model achieves an 89% predictive accuracy using historical data. …”