Aircraft maintenance system simulation mathematical model construction by Yu. I. Samulenkov, Ya. A. Filatova, A. D. Gruzd

“…The proposed simulation model can be used for the aircraft technical condition predictive estimate, aircraft gas turbine engines and functional systems.…”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…Recommender systems aim to accurately predict user preferences in order to provide potential items of interests. …”
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DNN-Based ADNMPC of an Industrial Pickling Cold-Rolled Titanium Process via Field Enhancement Heat Exchange by Biao Yang, Jinhui Peng, Wei Li, Youling Li, Huilong Luo, Zhuming Zhang, Shenghui Guo, Shimin Zhang, Hezhou Su, Yaming Shi

“…The identifier of the direct adaptive nonlinear model identification and the controller of the adaptive nonlinear model predictive control are designed based on series-parallel dynamic neural network training by RLS algorithm with variable incremental factor, gain, and forgetting factor. …”
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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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Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network by Nafisa Mehtaj, Sourav Banerjee

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A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation by Or Inbar, Omri Inbar, Ronen Reuveny, Michael J. Segel, Hayit Greenspan, Mickey Scheinowitz

“…The disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. …”
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Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations by Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran

“…Model robustness was evaluated using six additional machine learning algorithms. Results Here we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). …”
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Evaluating the Thermohydraulic Performance of Microchannel Gas Coolers: A Machine Learning Approach by Shehryar Ishaque, Naveed Ullah, Sanghun Choi, Man-Hoe Kim

“…Furthermore, advanced machine learning algorithms such as extreme gradient boosting (XGB), random forest (RF), support vector regression (SVR), k-nearest neighbors (KNNs), and artificial neural networks (ANNs) were employed to analyze their predictive capability. …”
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The Impact of Snow Cover on River Discharge Simulation: Insights from the Barandozchay River Basin by Haleh Hashemi, Hossein Rezaie, Keivan Khalili, Amin Amini

“…This study presents a comprehensive analysis aimed at predicting the discharge of the Barandozchay River using machine learning algorithms and meteorological data from both satellite and ground sources over the period from 2002 to 2022. …”
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Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform by Jianhui Wu, Lu Zhang, Sufeng Yin, Haidong Wang, Guoli Wang, Juxiang Yuan

“…The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. …”
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Image-Based Laser-Beam Diagnostics Using Statistical Analysis and Machine Learning Regression by Tayyab Imran, Muddasir Naeem

“…All parameters were derived using intensity-weighted algorithms and directional cross-sectional analysis to ensure accurate and consistent quantification of the beam’s dynamic behavior. …”
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Is visual inspection with acetic acid (VIA) a useful method of finding pre-invasive cervical cancer? by Maliheh Arab, Atefeh Moridi, Ghazaleh Fazli, Robabeh Ghodssi-Ghasemabadi, Maryam Maktabi, Samaneh Saraeian, Mahdie Sanati

“…The sensitivity, specificity and negative predictive value (NPV) of the VIA were 41.9%, 73.3%, and 98.2%, respectively. …”
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CareNexus: Integrated solutions for Healthcare Communication by Alexandru Ioan BULZAN

“…A secure authentication based on multiple factors and the use of encryption algorithms, an interface based on different user roles and a prediction system based on model training had been implemented. …”
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Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis by Yin-Cong Zhi, Simon Mpooya, Narcis B. Kabatereine, Betty Nabatte, Christopher K. Opio, Goylette F. Chami

“…Co-occurrence graphs were clinically uninformative with low predictive capacity. Graph learning algorithms with statistical assumptions, e.g. graphical lasso, enabled accurate and clinically valid multimorbidity representations. …”
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Financing Mechanisms and Preferences of Technology-Driven Small- and Medium-Sized Enterprises in the Digitalization Context by Jing Hu, Lianming Huang, Weifu Li, Hongyi Xu

“…Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…This improvement highlights the effectiveness of ensemble learning in integrating different algorithmic perspectives to enhance predictive performance. …”
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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

“…The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.…”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. …”
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Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis by Atta Taseh, Souri Sasanfar, Michelle Chan, Evan Sirls, Ara Nazarian, Kayhan Batmanghelich, Jonathan F Bean, Soheil Ashkani-Esfahani

“…The performances of the models were compared using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 ResultsA total of 1769 clinical notes were included in the final analysis for the fall occurrence task, and 783 clinical notes were analyzed for the fall mechanism classification task. …”
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Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence–Enabled ECGs by Scott Anjewierden, Donnchadh O'Sullivan, Kathryn E. Mangold, Grace Greason, Itzhak Zachi Attia, Francisco Lopez‐Jimenez, Paul A. Friedman, Samuel J. Asirvatham, Jason Anderson, Benjamin W. Eidem, Jonathan N. Johnson, Shisheer Havangi Prakash, Talha Niaz, Malini Madhavan

“…The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. …”
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