Correlation Clustering via s-Club Cluster Edge Deletion by Makarem, Norma

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Exploring Semi-Supervised Learning Algorithms for Camera Trap Images by Sajun, Ali Reza

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Time-varying volatility model equipped with regime switching factor: valuation of option price written on energy futures by Leduc, Guillaume

“…To determine the parameters of the regime switching model and identify when economic states change, we employ the EM algorithm, utilizing real gas futures price data. We validate our closed-form solution for the option pricing through simulations employing the generalized antithetic variates Monte-Carlo technique. …”
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A fast exact sequential algorithm for the partial digest problem by Mostafa M. Abbas (17058093)

“…Two types of simulated data, random and Zhang, are used to measure the efficiency of the algorithm. …”

Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach by Sarif, Bambang

“…The algorithm is tested using 200 randomly generated 2-variable 4-valued functions. …”
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Simulated annealing and genetic algorithms for exam scheduling. (c1997) by Tarhini, Abbas A.

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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…Among these, K-means is widely used for efficiently solving clustering problems. …”
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Second-order conic programming for data envelopment analysis models by Mourad, Nahia

“…Data envelopment analysis (DEA) is a widely used benchmarking technique. …”
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A Scatter search algorithm for exam scheduling. (c2006) by Isahakian, Vatche Raffi

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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth, Kunhoth

“…Soft voting and hard voting strategies are employed to ensemble these CNN models. The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. …”
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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth Kunhoth (14158908)

“…Soft voting and hard voting strategies are employed to ensemble these CNN models. The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. …”

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

“…Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. …”

Data mining approach to predict student's selection of program majors by SIDDARTHA, SHARMILA

“…The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …”
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An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study by Ayman Hassan (14426412)

“…</p><h3>Methods</h3><p dir="ltr">Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. …”

Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review by Daksh Dave (17949239)

Load Partitioning for Matrix-Matrix Multiplication on a Cluster of CPUGPU Nodes Using the Divisible Load Paradigm by Elhiny, Lamees

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Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques by Arifa Zahir (20748764)

“…The DinoLite Microscope’s high-resolution images are used to measure the length and width of wheat anthers. …”

A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008) by Korkmaz, Rita

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An image processing and genetic algorithm-based approach for the detection of melanoma in patients by Tokajian, Sima

“…The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.…”
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An ant colony optimization algorithm to improve software quality prediction models by Azar, D.

“…We use an ant colony optimization algorithm in the adaptation process. …”
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