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

“…In this paper, we contribute a new formulation of the proxy behavior derived from a model for approximate conformance checking. …”
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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). …”

Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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

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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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)

“…The best ML model and the best feature selection algorithm combination were fine-tuned to optimize its performance using hyperparameter optimization. …”

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

“…There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. …”
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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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Improved scatter search algorithm for predicting all-atoms protein structures using charmm22 energy model. (c2010) by Ghalayini, Iman Adnan

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Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence by Al Rayhi, Nasser

“…One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. …”
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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. …”