Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>Radial basis function kernel-support vector machine (RBF-SVM): </b>A more flexible version of SVM that uses a non-linear kernel to capture complex relationships in genomic data, improving classification accuracy.…”

Sample size determination for multidimensional parameters and the A-optimal subsampling in a big data linear regression model by Sheng Zhang (111330)

“…<p>To efficiently approximate the least squares estimator (LSE) in a Big Data linear regression model using a subsampling approach, optimal sampling distributions were derived by minimizing the trace norm of the covariance matrix of a smooth function of the subsampling LSE. An algorithm was developed that significantly reduces the computation time for the subsampling LSE compared to the full-sample LSE. …”

Echo Peak by Rocco De Marco (14146593)

“…Future enhancements could include the integration of machine learning for more refined classification or the extension of the detection algorithm to other cetacean species.…”

Table 1_Effectiveness of deep neural networks in hearing aids for improving signal-to-noise ratio, speech recognition, and listener preference in background noise.docx by Matthew B. Fitzgerald (11526808)

“…Recent advances in artificial intelligence and machine learning offer new opportunities for enhancing the signal-to-noise ratio (SNR) through adaptive signal processing. …”

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis by Alireza Enshaeian (20360253)

“…The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. …”

Dual-Level Parametrically Managed Neural Network Method for Learning a Potential Energy Surface for Efficient Dynamics by Suman Bhaumik (14094200)

“…The goal of the present work is to remedy this by a low-cost method for incorporating well understood features of potential energy surfaces into an efficient data-driven machine learning algorithm. Our focus is on regions where conventional surface fitting does not need large amounts of accurate data, in particular, geometries with large separations of subsystems–where it is well recognized that the potential should reach its asymptotic form–and geometries with very close atoms–where the potential should be repulsive enough to prevent trajectories from reaching classically inaccessible regions but need not be highly quantitative. …”

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

“…To address batch effects introduced during the merging process, the Empirical Bayes algorithm from the sva package (via the ComBat function) was applied. …”

Data Sheet 1_Aspects of zone-like identity and holotomographic tracking of human stem cell-derived liver sinusoidal endothelial cells.pdf by Mikel Amirola-Martinez (22449625)

“…</p>Results and discussion<p>Holotomography and developed machine learning-based algorithm for image processing allowed us to describe and monitor changes in intracellular pore-like structures over time. …”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …”

Study design and deep-learning model architecture. by Sehoon Park (1466026)

“…Conv, Convolutional layer; SepConv, Separable convolutional layer; MBConv, Mobile inverted bottleneck convolutional layer (numbers after MBConv indicate layer depth); k3/k5, kernel size 3 or 5; GAP, Global average pooling; FC, Fully connected layer; Swish, Swish activation function; DBP, Diastolic blood pressure, SBP, Systolic blood pressure; HR, Heart rate; DL-IVSS, A deep-learning algorithm leveraging time-series intraoperative vital sign signals; preOp ML, A machine learning model with 103 baseline characteristics.…”

Artificial Neural Network Model for Predicting the Viscosity of Crosslinked Polyacrylamide and Polyethylenimine Polymer Gel for Oilfield Water Control by S M Safayet Aziz (20832236)

“…The model was trained using the Levenberg-Marquardt algorithm. The hidden layer uses the tangent sigmoid (Tansig) activation function, and the output layer employs a linear (Purelin) activation function. …”

Comparison of Five Classification Models by Qiran Wang (22051883)

“…<p dir="ltr">The Support Vector Machine (SVM) constructs optimal separating hyperplanes with maximum margin and utilizes kernel functions to handle nonlinear spectral patterns. …”

Data Sheet 1_Artificial clicks (Porpoise ALert) affect acoustic monitoring of harbour porpoises and their echolocation behaviour.pdf by Joseph G. Schnitzler (12084395)

“…The function of these devices on echolocation behaviour remains therefore unclear, as it is not known whether they act solely as an alarm or rather as a deterrent.…”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

“…<p>Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. …”

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles by Peter S. Rice (11805875)

“…Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H_ads), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H_ads. …”

Data Sheet 1_Urinary lipid metabolites and progression of kidney disease in individuals with type 2 diabetes.pdf by Yu Xiao (150812)

“…Feature selection was performed using machine learning algorithms (random forest and Boruta) to identify potential biomarkers from the differentially expressed metabolites. …”

Table 1_Identification of NPM and non-mass breast cancer based on radiological features and radiomics.docx by Zhen Guo (68566)

“…The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 10-fold nested cross-validation selected 6 predictive features, and a support vector machine (SVM) model with a Radial Basis Function kernel was constructed. …”

Data Sheet 1_Exploring the shared gene signatures and mechanism among three autoimmune diseases by bulk RNA sequencing integrated with single-cell RNA sequencing analysis.docx by Xiaofang Liu (398453)

“…We used machine learning algorithms to select candidate biomarkers and evaluate their diagnostic value. …”

Image 1_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”

Image 2_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”