Bar chart comparing the average fitness values of various feature selection algorithms. by Doaa Sami Khafaga (21463870)

Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. by Doaa Sami Khafaga (21463870)

The confidence curve of a binary image. by Dingming Yang (8505990)

The confidence change of the binary image after iteration. by Dingming Yang (8505990)

Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…”

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Event-driven data flow processing. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”