White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization by Riaz Ahmad, Muhammad Awais, Nabeela Kausar, Tallha Akram

“…This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. …”
Get full text

Machine learning algorithms applied to wildfire data in California's central valley by Kassandra Hernandez, Aaron B. Hoskins

“…This topic is relevant since California has seen an increase in wildfires with an increase in annual forest burned areas to +172 % from 1996 to 2021 (ABC 2024). The algorithms selected were based on previous research that conducted similar studies. …”
Get full text

Bayesian adaptive algorithms for locating HIV mobile testing services by Gregg S. Gonsalves, J. Tyler Copple, Tyler Johnson, A. David Paltiel, Joshua L. Warren

“…Over 180 days, search algorithms selected a zone in which to conduct a fixed number of HIV tests. …”
Get full text

Development and Testing of a Clear-Sky Data Selection Algorithm for FY-3C/D Microwave Temperature Sounder-2 by Zeyi Niu, Xiaolei Zou, Peter Sawin Ray

“…Applications of this algorithm to FY-3C MWTS-2 and MetOp-B AMSU-A lead to the following conclusions: (i) more than 70% (95%) of the clear-sky (cloudy) data points are successfully identified from both AMSU-A and MWTS-2 observations; (ii) the algorithm-selected clear-sky data points were located in clear-sky areas in the GOES-15 imager, and (iii) the bias-removed differences between observations and model simulations of MWTS-2 channel 1 well reveals the eye, the eyewall, and the spiral rainband structure of Super Typhoon Halong (2014).…”
Get full text

A Hybrid Model for Temperature Prediction in a Sheep House by Dachun Feng, Bing Zhou, Shahbaz Gul Hassan, Longqin Xu, Tonglai Liu, Liang Cao, Shuangyin Liu, Jianjun Guo

“…The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. …”
Get full text

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification by Alejandro Lopez-Rincon, Lucero Mendoza-Maldonado, Marlet Martinez-Archundia, Alexander Schönhuth, Aletta D. Kraneveld, Johan Garssen, Alberto Tonda

“…Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. …”
Get full text

Plasma Metabolomics and Machine Learning-Driven Novel Diagnostic Signature for Non-Alcoholic Steatohepatitis by Moongi Ji, Yunju Jo, Seung Joon Choi, Seong Min Kim, Kyoung Kon Kim, Byung-Chul Oh, Dongryeol Ryu, Man-Jeong Paik, Dae Ho Lee

“…Then, the recursive partitioning and regression tree algorithm selected three metabolites (glutamic acid, isocitric acid, and aspartic acid) from these eight metabolites. …”
Get full text

The Minnesota Haptic Function Test by Jessica Holst-Wolf, Yu-Ting Tseng, Yu-Ting Tseng, Jürgen Konczak

“…A Bayesian-based adaptive algorithm selected presented stimulus pairs based on a subject’s previous responses, which ensured fast convergence toward a threshold. …”
Get full text

Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits by Yujie Weng, Pengfei Ning

“…The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. …”
Get full text

Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection by Aliaa El-Gawady, Mohamed A. Makhlouf, BenBella S. Tawfik, Hamed Nassar

“…Exploring the eight subsets, the algorithm selects the best one to describe AD, and also the best ML model to predict the disease using this subset. …”
Get full text

A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data by Jun Liu, Jingyun Liu, Huan Xie, Dan Ye, Peinan Li

“…Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. …”
Get full text

Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration by Xiao Yang, Yang Lu, Hang Zhou, Hai-Tao Jiang, Lei Chu

“…SERPINA1, ORM2, FGG and COL1A1 were identified through PRO-seq as the hub proteins involved in regulating IDD. ML algorithms selected ten hub genes, including IBSP, COL6A2, MMP2, SERPINA1, ACAN, FBLN7, LAMB2, TTLL7, COL9A3, and THBS4 in bRNA-seq. …”
Get full text

A method for heavy metal estimation in mining regions based on SMA-PCC-RF and reflectance spectroscopy by Yueyue Wang, Ruiqing Niu, Ming Hao, Guo Lin, Yingxu Xiao, Huaidan Zhang, Bangjie Fu

Get full text

OPERA models for predicting physicochemical properties and environmental fate endpoints by Kamel Mansouri, Chris M. Grulke, Richard S. Judson, Antony J. Williams

“…A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). …”
Get full text

Evaluation of HIV-1 rapid tests and identification of alternative testing algorithms for use in Uganda by Pontiano Kaleebu, Paul Kato Kitandwe, Tom Lutalo, Aminah Kigozi, Christine Watera, Mary Bridget Nanteza, Peter Hughes, Joshua Musinguzi, Alex Opio, Robert Downing, Edward Katongole Mbidde

“…In the second phase, the three best algorithms selected in phase I were used at the point of care for purposes of quality control using finger stick whole blood. …”
Get full text

Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data by Robert J O’Shea, Sophia Tsoka, Gary JR Cook, Vicky Goh

“…The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, L 0 L 1 penalisation and L 0 L 2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation. Methods: Five large ( n 4000 ) genomic datasets were extracted from Gene Expression Omnibus. …”
Get full text

Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1 by M. Goudar, J. C. S. Anema, R. Kumar, T. Borsdorff, J. Landgraf

“…After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. …”
Get full text

Evolution and impact of bias in human and machine learning algorithm interaction. by Wenlong Sun, Olfa Nasraoui, Patrick Shafto

“…Our goal is to study two sources of bias that interact: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). …”
Get full text

High-Performance Image Acquisition and Processing for Stereoscopic Diagnostic Systems with the Application of Graphical Processing Units by Piotr Perek, Aleksander Mielczarek, Dariusz Makowski

“…The main steps of the proposed solution are uncalibrated rectification and disparity map estimation. The algorithms selected and implemented for the needs of this system do not require knowledge of intrinsic and extrinsic camera parameters. …”
Get full text

Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson’s disease by Kenneth H. Louie, Matthew N. Petrucci, Logan L. Grado, Chiahao Lu, Paul J. Tuite, Andrew G. Lamperski, Colum D. MacKinnon, Scott E. Cooper, Theoden I. Netoff

“…Rigidity was measured, in 5Hz increments, between 10–185Hz (total 30–36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. …”
Get full text