Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“… This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights the intricate process involved in selecting the most suitable ML algorithm for predicting specific medical conditions, emphasizing the critical role of real-world data in testing and validation. …”
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Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

“…Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. …”
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GNSS signal-to-noise snow depth inversion based on robust empirical mode decomposition by Dengao Li, Xinyu Luo, Jumin Zhao, Fanming Wu, Hairong Jiang, Danyang Shi

“…The proposed algorithm is validated using the data from the U.S. …”
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Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges by Chenzuo Ye, Hao Wu, Takashi Oguchi, Yuting Tang, Xiangjun Pei, Yufeng Wu

“…In contrast, data-driven models, primarily developed using machine learning and statistical algorithms, often provide acceptable predictive accuracy in assessing landslide susceptibility. …”
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Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) by Norazman Shahar, Muhammad Amir As'Ari, Tan Tian Swee, Nurul Fathia Ghazali

“…Results demonstrated that RNCA-MRMR could establish an efficient algorithm that can satisfy the model validation tests with significant advantages over feature number and predictive accuracy at 93.5%, 93.7%, and 94.5% for two, three, and all four sensors respectively. …”
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System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…They showed that the predicted error on the validation data was 0.18758. …”
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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…The multimodal model contained age, IL-6, and the 2 radiomics features, and the optimal model was from LightGBM algorithm. The optimal multimodal model had the highest AUC value, accuracy, sensitivity, and negative predictive value compared with the optimal clinical or radiomics models, and its’ favorable performance was also verified in the external test dataset (accuracy = 0.745, sensitivity = 0.900). …”
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Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays. by Thomas Sanford, Paul H Chung, Ariel Reinish, Vladimir Valera, Ramaprasad Srinivasan, W Marston Linehan, Gennady Bratslavsky

“…These signatures were organized into an algorithm to sub-classify renal neoplasms. The use of these signatures according to our algorithm was validated on several independent datasets.…”
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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. …”
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Constitutive Model for Hot Deformation Behavior of Fe-Mn-Cr-Based Alloys: Physical Model, ANN Model, Model Optimization, Parameter Evaluation and Calibration by Jie Xu, Chaoyang Sun, Huijun Liang, Lingyun Qian, Chunhui Wang

“…The parameters and architecture of the ANN model are then systematically optimized using optimization algorithms to enhance training efficiency and prediction accuracy. …”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. …”
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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

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Oil Commodity Movement Estimation: Analysis with Gaussian Process and Data Science by Mulue Gebreslasie, Indranil SenGupta

“…In this study, Gaussian process (GP) regression is used to normalize observed commodity data and produce predictions at densely interpolated time intervals. …”
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Synergizing Attribute-Guided Latent Space Exploration (AGLSE) with Classical Molecular Simulations to Design Potent Pep-Magnet Peptide Inhibitors to Abrogate SARS-CoV-2 Host Cell E... by Farhan Ullah, Aobo Xiao, Shahid Ullah, Na Yang, Min Lei, Liang Chen, Sheng Wang

“…Utilizing an antiviral peptide as a model biomolecule, we trained a generative deep learning algorithm on a database of known antiviral peptides to design novel peptide sequences with antiviral activity. …”
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Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial by Céline Fernandez, Marion Cremoni, Laurent Bailly, Kevin Zorzi, Vesna Brglez, Barbara Seitz-Polski, Maxime Teisseyre, Alexandre Destere, Sylvain Benito

“…We have previously developed a machine learning algorithm to predict the risk of underdosing. We have retrospectively shown that patients with a high risk of underdosing required higher doses of rituximab to achieve remission. …”
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

“…Purpose: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. …”
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Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality by Fernando García-García, Dae-Jin Lee, Mónica Nieves-Ermecheo, Olaia Bronte, Pedro Pablo España, José María Quintana, Rosario Menéndez, Antoni Torres, Luis Alberto Ruiz Iturriaga, Isabel Urrutia, COVID-19 & Air Pollution Working Group

“…We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. …”
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Simultaneous multislice cardiac multimapping based on locally low-rank and sparsity constraints by Yixin Emu, Yinyin Chen, Zhuo Chen, Juan Gao, Jianmin Yuan, Hongfei Lu, Hang Jin, Chenxi Hu

“…To mitigate image artifacts and noise caused by the ill-conditioning, a reconstruction algorithm based on locally low-rank and sparsity (LLRS) constraints was developed. …”
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Global digital elevation model (GDEM) product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data by B. Li, B. Li, B. Li, B. Li, H. Xie, H. Xie, S. Liu, Z. Ye, Z. Hong, Q. Weng, Q. Weng, Q. Weng, Y. Sun, Q. Xu, X. Tong

“…The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, and the geographical layout of the satellite laser altimeter data. …”
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