Parameter settings. by Yang Cao (53545)

“…<div><p>Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. …”

Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX by Umesh C. Sharma (10785063)

“…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …”

Data_Sheet_1_Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer.pdf by Maliheh Aramon (6557906)

“…The Digital Annealer's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. …”

Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

“…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”

Fig 12 - by Nisha Yadav (366131)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

Data_Sheet_3_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx by Pijush Das (3196647)

“…The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. …”

Data_Sheet_2_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx by Pijush Das (3196647)

“…The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. …”

Data_Sheet_1_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx by Pijush Das (3196647)

“…The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). …”

Data_Sheet_1_Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.docx by Xueyan Zhang (277449)

“…We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.</p>Methods<p>Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.…”

The Value of Dynamic Grip Force Modulation as a Potential Biomarkerfor Hand Function Recovery Following Stroke by Kirstin-Friederike Heise (7518953)

“…</p><p dir="ltr">We used a supervised machine learning algorithm (support vector machine, SVM, with k-fold cross-validation) for binary classification of groups (stroke versus control group), task conditions (uni- versus bimanual), and to quantify the active range of motion evaluated with upper extremity Fugl-Meyer Assessment (UEFMA) within the stroke group alone.…”

Data_Sheet_1_Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach.pdf by Md. Zahangir Alam (12056864)

“…To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%).…”

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph-CNNs trained gene expression data. by Halima Alachram (11570633)

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”