The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review by Davood Dalil, MD, Sina Esmaeili, Ehsan Safaee, Sajad Asgari, Nooshin Kejani, MD

“…Eligible studies focused on the predictive accuracy of AI algorithms for VTE post arthroplasty and were assessed for quality using the Newcastle-Ottawa Scale. …”
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Planificación y optimización asistida por computadora de secuencias de ensamble mecánico. by L. L. Tomás García

“…The generated assembly sequences are preprocessed and optimized for the assembly Process Planning using Genetic Algorithms. This approach integrates the geometric and technological information of the assembly process, which allows reducing the number of elements and sequences to be processed with the consequent processing time and cost reduction.…”
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Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme by Xi Liu, Jinming Song, Zhiming Zhou, Yuting He, Shaochun Wu, Jin Yang, Zhonglu Ren

“…The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. …”
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Oxidative balance is associated with diabetic kidney disease and mortality in adults with diabetes mellitus: Insights from NHANES database and Mendelian randomization by Li Jiang, Jie Jian, Xulin Sai, Hongda Yu, Wanxian Liang, Xiai Wu

“…The physical activity was identified as the core variable predicting DKD risk by two machine learning algorithms. MR showed a potential correlated relationship between ROS and microalbuminuria in DKD. …”
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A Reproducible Method for Donor Site Computed Tomography Measurements in Abdominally Based Autologous Breast Reconstruction by Damini Tandon, MD, Arthur Sletten, MD, PhD, Austin Ha, MD, Gary B. Skolnick, BA, MBA, Paul Commean, BEE, Terence Myckatyn, MD

“…Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.…”
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Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases by Deyan Chen, Hong Zhao, Xia Zhang

“…Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. …”
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Spatiotemporal Monitoring of Cyanobacterial Blooms and Aquatic Vegetation in Jiangsu Province Using AI Earth Platform and Sentinel-2 MSI Data (2019–2024) by Xin Xie, Ting Song, Ge Liu, Tiantian Wang, Qi Yang

“…This system integrates phenology-based algorithms with Sentinel-2 MSI imagery, leveraging the AI Earth (AIE) platform developed by Alibaba DAMO Academy. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Critical structural and functional roles for the N-terminal insertion sequence in surfactant protein B analogs. by Frans J Walther, Alan J Waring, Jose M Hernandez-Juviel, Larry M Gordon, Zhengdong Wang, Chun-Ling Jung, Piotr Ruchala, Andrew P Clark, Wesley M Smith, Shantanu Sharma, Robert H Notter

“…Surface plasmon resonance (SPR), predictive aggregation algorithms, and molecular dynamics (MD) and docking simulations further suggested a preliminary model for dimeric Super Mini-B, in which monomers self-associate to form a dimer peptide with a "saposin-like" fold. …”
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Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model by Yongqiang Wang, Feng Liang, Shang Wang, Hang Chen, Qi Cao, Haisheng Fu, Zhenjiao Chen

“…Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset.…”
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Evaluating clinician acceptability of the prototype CanRisk tool for predicting risk of breast and ovarian cancer: A multi-methods study. by Stephanie Archer, Chantal Babb de Villiers, Fiona Scheibl, Tim Carver, Simon Hartley, Andrew Lee, Alex P Cunningham, Douglas F Easton, Jennifer G McIntosh, Jon Emery, Marc Tischkowitz, Antonis C Antoniou, Fiona M Walter

“…<h4>Background</h4>There is a growing focus on the development of multi-factorial cancer risk prediction algorithms alongside tools that operationalise them for clinical use. …”
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Second-Level Numerical Semigroups by David Llena, José Carlos Rosales

“…In this paper, we present some algorithms to compute all the second-level numerical semigroups with multiplicity, genus, and a Frobenius fixed number. …”
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Words high-frequency drying processes simulation of wooden tangent towers in a vacuum chamber by A. N. Kachanov, D. A. Korenkov, A. A. Revkov, V. V. Maksimov, O. V. Vorkunov

“…This model is characterized by the possibility of using simple algorithms for analyzing differential equation systems based on the finite differe nce method and requiring less initial data on the drying material properties. …”
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Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables. by Rocco J LaFaro, Suryanarayana Pothula, Keshar Paul Kubal, Mario Emil Inchiosa, Venu M Pothula, Stanley C Yuan, David A Maerz, Lucresia Montes, Stephen M Oleszkiewicz, Albert Yusupov, Richard Perline, Mario Anthony Inchiosa

“…Two additional predictive algorithms were studied, but they had lower prediction accuracies. …”
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Hybrid Modeling of an Induction Machine to Support Bearing Diagnostics by Praneet Amitabh, Dimitar Bozalakov, Frederik De Belie

“…The ultimate goal of this methodology is to generate extensive datasets encompassing diverse operating conditions that can be used further to estimate the health of the bearing and possibly be used for training predictive algorithms to estimate bearing RUL in motors. The proposed methodology is developed for the machine operating at 1000 and 1500 RPM and is validated for three different operating speeds.…”
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Interpretable machine learning models for predicting in-hospital mortality in patients with chronic critical illness and heart failure: A multicenter study by Min He, Yongqi Lin, Siyu Ren, Pengzhan Li, Guoqing Liu, Liangbo Hu, Xueshuang Bei, Lingyan Lei, Yue Wang, Qianghong Zhang, Xiaocong Zeng

“…Key predictive variables were identified through recursive feature elimination. A range of ML algorithms, including random forest, K-nearest neighbors, and support vector machine (SVM), were evaluated alongside four other models. …”
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Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma by Minxue Zhang, Lin Zhou, Yuying Zhao, Yanling Wang, Zhuobo Zhang, Zhan Liu

“…From these, two key diagnostic genes were identified using three machine learning algorithms: ADIPOQ and IL21R. A predictive neural network model was developed based on these genes, exhibiting strong diagnostic capability with a ROC value of 0.944, and further validated using a nomogram approach. …”
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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

“…Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. …”
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Modeling forest canopy structure and developing a stand health index using satellite remote sensing by Pulakesh Das, Parinaz Rahimzadeh-Bajgiran, William Livingston, Cameron D. McIntire, Aaron Bergdahl

“…The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. …”
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Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence by Kristóf Ágrez, Pál Vakli, Béla Weiss, Zoltán Vidnyánszky, Nóra Bunford

“…Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. …”
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