Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms by Liqiang Zhang, Qingxia Li, Haitao Qiu, Qingjun Zhang, Yixin Gao, Rong Jin, Rui Wang, Huan Zhang, Zhongkai Wen, Jian Zhang

“…Through simulation analysis and evaluation of RFI detection algorithms, lookup tables for algorithm selection, detection rate, and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario. …”
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Statistical power for cluster analysis by Edwin S. Dalmaijer, Camilla L. Nord, Duncan E. Astle

“…While guidelines exist for algorithm selection and outcome evaluation, there are no firmly established ways of computing a priori statistical power for cluster analysis. …”
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Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning by Jingyue Wu, Stephanie S. Singleton, Urnisha Bhuiyan, Lori Krammer, Lori Krammer, Raja Mazumder, Raja Mazumder

“…Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. …”
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Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity by Álvaro Torres-Martos, Mireia Bustos-Aibar, Alberto Ramírez-Mena, Sofía Cámara-Sánchez, Augusto Anguita-Ruiz, Rafael Alcalá, Concepción M. Aguilera, Jesús Alcalá-Fdez

“…Currently, many of the available approaches that use machine learning on omics data for predictive purposes make mistakes in several of the following key steps: experimental design, feature selection, data pre-processing, and algorithm selection. For this reason, we propose the current work as a guideline on how to confront the main challenges inherent to multi-omics human data. …”
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A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures by Claudia C. Tusell-Rey, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez, Yenny Villuendas-Rey, Ricardo Tejeida-Padilla, Carmen F. Rey Benguría

“…In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. …”
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Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach by Junwei Ma, Sheng Jiang, Zhiyang Liu, Zhiyuan Ren, Dongze Lei, Chunhai Tan, Haixiang Guo

“…The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. …”
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Exploring Tree Species Classification in Subtropical Regions with a Modified Hierarchy-Based Classifier Using High Spatial Resolution Multisensor Data by Xiandie Jiang, Shuai Zhao, Yaoliang Chen, Dengsheng Lu

“…Major steps to create an MHBC include automatic determination of classification tree structures based on the Z-score algorithm, selection and optimization of variables for each node, and classification using the optimized model. …”
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A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems by Ajay Kaushik, Hamed S. Al-Raweshidy

“…The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. …”
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Assessing Epileptogenicity Using Phase-Locked High Frequency Oscillations: A Systematic Comparison of Methods by Mojtaba Bandarabadi, Mojtaba Bandarabadi, Heidemarie Gast, Christian Rummel, Claudio Bassetti, Claudio Bassetti, Antoine Adamantidis, Antoine Adamantidis, Kaspar Schindler, Frederic Zubler

“…These results confirm that quantitative analysis of HFOs can boost presurgical evaluation and indicate the paramount importance of algorithm selection for clinical applications.…”
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New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production by Vuong Van Pham, Ebrahim Fathi, Fatemeh Belyadi

“…In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. …”
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Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance by Xiao Xiao, Tung X. Trinh, Zayakhuu Gerelkhuu, Eunyong Ha, Tae Hyun Yoon

“…These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. …”
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Precision autonomous underwater navigation by Bingham, Brian S. (Brian Steven), 1973-

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Automatic clustering for unsupervised risk diagnosis of vehicle driving for smart road by Shi, Xiupeng, Wong, Yiik Diew, Chai, Chen, Li, Michael Zhi Feng, Chen, Tianyi, Zeng, Zeng

“…This study proposes a domain-specific automatic clustering (termed AutoCluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, a series of risk indicator features are constructed to represent temporal-spatial and kinematical risk exposures. …”
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Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery by Jonathan V. Solórzano, Jean François Mas, Yan Gao, José Alberto Gallardo-Cruz

“…In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. …”
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Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia by Chaoqing Huang, Bin Chen, Chuanzhun Sun, Yuan Wang, Junye Zhang, Huan Yang, Shengbiao Wu, Peiyue Tu, MinhThu Nguyen, Song Hong, Chao He

“…In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. …”
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Optimization and Design of a Flexible Droop Nose Leading Edge Morphing Wing Based on a Novel Black Widow Optimization (B.W.O.) Algorithm—Part II by Musavir Bashir, Simon Longtin-Martel, Nicola Zonzini, Ruxandra Mihaela Botez, Alessandro Ceruti, Tony Wong

“…The lift-to-drag ratio was used as the fitness function, and the impact of the choice of optimization algorithm selection on the fitness function was evaluated. …”
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Uncertainty in parameterizing a flux‐based model of vegetation carbon phenology using ecosystem respiration by Yuan Gong, Christina L. Staudhammer, Susanne Wiesner, Yinlong Zhang, Jeffery B. Cannon, Gregory Starr

“…We determined the impact of algorithm selection on estimating key biological dates related to plant community carbon dynamics (e.g., start, end, and length of physiologically active season, specifically Re), characterized the model's response to extreme weather events, and compared estimates to those derived via remotely sensed greenness from the enhanced vegetation index (EVI). …”
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SNR (Signal-To-Noise Ratio) Impact on Water Constituent Retrieval from Simulated Images of Optically Complex Amazon Lakes by Daniel S. F. Jorge, Claudio C. F. Barbosa, Lino A. S. De Carvalho, Adriana G. Affonso, Felipe De L. Lobo, Evlyn M. L. De M. Novo

“…However, the number and position of OLI bands restrict Chl-a retrieval. Sensor and algorithm selection need a comprehensive analysis of key factors such as sensor design, in situ conditions, water brightness (Rrs), and model equations before being applied for inland water studies.…”
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Algorithm development for individualized precision feeding of supplemental top dresses to influence feed efficiency of dairy cattle by V.C. Souza, D.M. Liebe, T.P. Price, M.D. Ellett, T.C. Davis, C.B. Gleason, K.M. Daniels, R.R. White

“…This study yielded 2 candidate approaches for efficiency-focused, individualized feeding recommendations. Refinement of algorithm selection, development, and training approaches are needed to maximize production parameters through individualized feeding.…”
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Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data by Chao Zhang, Tongtong Song, Runhe Shi, Zhengyang Hou, Nan Wu, Han Zhang, Wei Zhuo

“…Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. …”
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