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. …”

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP by Liangliang Wang (580395)

“…This study aimed to distinguish patients with HCC having distinct tumour immune microenvironment (TIME) features and construct an immune-related gene signature (IRGs) to assess prognosis and provide a basis for personalised therapies.…”

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

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. …”

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. …”

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…This work mainly focuses on the following aspects: (1) On the basis of the IPC-MB and DPI, we presented a novel feature selection method called the improved MB discovery algorithm (IMBDA), which can accurately identify direct and indirect regulatory genes when inferring networks. (2) Isolated genes were properly processed by the IDS to optimize the network structure. (3) The performance of IMBDANET was assessed with extensive experiments. …”

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm by Interdis Sci Comp Life Sci (7335308)

“…This work mainly focuses on the following aspects:</p><p dir="ltr"> (1) On the basis of the IPC-MB and DPI, we presented a novel feature selection method called the improved MB discovery algorithm (IMBDA), which can accurately identify direct and indirect regulatory genes when inferring networks.…”

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. …”

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. …”

Breast cancer specific subnetwork. by Bengi Ruken Yavuz (21983004)

Generalized Tensor Decomposition With Features on Multiple Modes by Jiaxin Hu (1327875)

“…An efficient alternating optimization algorithm with provable spectral initialization is further developed. …”