Compositing the Minimum NDVI for Daily Water Surface Mapping by Xingwang Fan, Yuanbo Liu, Guiping Wu, Xiaosong Zhao

“…This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. …”
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Surface reconstruction from interferometric ISAR data by Forrester, Neil T

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Characterization of transdermal drug delivery system using visible spectrophotometry and artificial neural network / Normaizira Hamidi by Hamidi, Normaizira

“…The ANN training was done by using a Multilayer feed forward neural network (MFNN) with Quasi-Newton learning algorithm. Selected absorbance spectra produced by visible spectrophotometry technique were utilized as inputs to the ANN model and the drug contents value as an output. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In addition, experiments prove that the ensemble algorithms select highly relevant features to feed the MLP comparing individual techniques in terms of classifier performance through lower false positive, higher accuracy, and better CPU time.…”
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Using Machine Learning to Develop and Validate an In-Hospital Mortality Prediction Model for Patients with Suspected Sepsis by Hsiao-Yun Chao, Chin-Chieh Wu, Avichandra Singh, Andrew Shedd, Jon Wolfshohl, Eric H. Chou, Yhu-Chering Huang, Kuan-Fu Chen

“…Among them, 18% fulfilled Sepsis-3 criteria, with a 28-day mortality rate of 8%. The wrapper algorithm selected 30 features, including disease severity scores, biochemical parameters, and conventional and few sepsis-related biomarkers. …”
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Peanut Frostbite Detection Method Based on Near Infrared Hyperspectral Imaging Technology by Cheng CUI, Cuiling LIU, Xiaorong SUN, Jingzhu WU

“…Among them, the VCPA-GA algorithm selected the least 7 feature wavelengths, accounting for only 3.125% of all wavelengths in the dataset. …”
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Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection by Hongzhuo Wu, Shiyu Du, Yiming Zhang, Quan Zhang, Kai Duan, Yanru Lin

“…In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset. The MTBGWO algorithm selects much smaller subset of features than other algorithms. …”
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Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data by Amin Ul Haq, Jian Ping Li, Jalaluddin Khan, Muhammad Hammad Memon, Shah Nazir, Sultan Ahmad, Ghufran Ahmad Khan, Amjad Ali

“…The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. …”
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Artificial intelligence in the immunodiagnostics of chronic periodontitis by Valery P. Mudrov

“…The random forest machine learning algorithm selected correlation ratios r 0.5 (both positive and negative) from a set of data for further analysis by the operator. …”
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Construction and validation of a novel senescence-related risk score can help predict the prognosis and tumor microenvironment of gastric cancer patients and determine that STK40 c... by Weijie Sun, Yihang Yuan, Jiaying Chen, Qun Bao, Mengsi Shang, Peng Sun, Haixia Peng

“…Single-cell RNA sequencing was utilized to investigate the expression patterns of key genes in different cell types.ResultsThrough the WGCNA algorithm and Lasso-Cox algorithm selected KL, SERPINE1, and STK40 as key genes for constructing the prognostic model. …”
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Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction by Lam, Remi Roger Alain Paul

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Aerosol retrieval experiments in the ESA Aerosol_cci project by Holzer-Popp, T, de Leeuw, G, Griesfeller, J, Martynenko, D, Klueser, L, Bevan, S, Davies, W, Ducos, F, Deuze, J, Graigner, R, Heckel, A, von Hoyningen-Huene, W, Kolmonen, P, Litvinov, P, North, P, Poulsen, C, Ramon, D, Siddans, R, Sogacheva, L, Tanre, D, Thomas, G, Vountas, M, Descloitres, J, Griesfeller, J, Kinne, S

“…Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. …”

Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma by Qiyi Hu, Guojie Wang, Xiaoyi Song, Jingjing Wan, Man Li, Fan Zhang, Qingling Chen, Xiaoling Cao, Shaolin Li, Ying Wang

“…The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. …”
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Recurrence Risk Evaluation in Patients with Papillary Thyroid Carcinoma: Multicenter Machine Learning Evaluation of Lymph Node Variables by Sung-Woo Jang, Jae-Hyun Park, Hae-Rim Kim, Hyeong-Ju Kwon, Yu-Mi Lee, Suck-Joon Hong, Jong-Ho Yoon

“…Since these factors showed a nonlinear association with the hazard ratio of recurrence-free survival (RFS) rates, we calculated their optimal cutoff values using the K-means clustering algorithm, selecting 0.2 cm and 1.1 cm for the maximal diameter of metastatic LN foci, 4 and 13 for the number of metastatic LN, and 0.28 and 0.58 for the metastatic LN ratio. …”
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Exploring a multiparameter MRI–based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma by Li Liu, Ling Zhao, Yang Jing, Dan Li, Hua Linghu, Haiyan Wang, Linyi Zhou, Yuan Fang, Yongmei Li

“…The handcrafted radiomic features were extracted, and then were selected using variance threshold algorithm, SelectKBest algorithm, and least absolute shrinkage and selection operator. …”
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Analysis and Construction of a Molecular Diagnosis Model of Drug-Resistant Epilepsy Based on Bioinformatics by Tenghui Han, Zhenyu Wu, Jun Zhu, Jun Zhu, Yao Kou, Jipeng Li, Yanchun Deng

“…Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. …”
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Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images by Changsai Zhang, Yuan Yi, Lijuan Wang, Xuewei Zhang, Shuo Chen, Zaixing Su, Shuxia Zhang, Yong Xue

“…Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. …”
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Optimized Extraction Method of Fruit Planting Distribution Based on Spectral and Radar Data Fusion of Key Time Phase by Guobing Zhao, Lei Wang, Jianghua Zheng, Nigela Tuerxun, Wanqiang Han, Liang Liu

“…The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. …”
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Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments by Patrick M. Carry, Tim Vigers, Lauren A. Vanderlinden, Carson Keeter, Fran Dong, Teresa Buckner, Elizabeth Litkowski, Ivana Yang, Jill M. Norris, Katerina Kechris

“…Abstract Background We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. …”
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A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals by Mustafa Küçükakarsu, Ahmet Reşit Kavsaoğlu, Fayadh Alenezi, Adi Alhudhaif, Raghad Alwadie, Kemal Polat

“…Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. …”
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