Convolutional neural network for maxillary sinus segmentation based on the U-Net architecture at different planes in the Chinese population: a semantic segmentation study by Jiayi Chen

“…The best model was retained for predicting MS in the test set and calculating the model parameters. …”
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Mathematical solutions for coupled nonlinear equations based on bioconvection in MHD Casson nanofluid flow by Khalil Ur Rehman, Nosheen Fatima, Wasfi Shatanawi, Nabeela Kousar

“…ODEs were then numerically solved in MATLAB software using the built-in solver bvp4c algorithm. We established an artificial neural network (ANN) model, incorporating Tan-Sig and Purelin transfer functions, to enhance the accuracy of predicting skin friction coefficient (SFC) values along the surface. …”
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Analysis and Application of Grey-Markov Chain Model in Tax Forecasting by Huidi Zhang, Yimao Chen

“…The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. …”
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Comprehensive analysis of IRF8-related genes and immune characteristics in lupus nephritis by Zhibin Yu, Chenghui Zheng, Yilun Wang

“…To bridge our findings with potential therapeutic implications, we used the drug-gene interaction database to predict drugs targeting the identified genes.ResultsTwenty co-differentially expressed genes (DEGs) were identified, with IRF8 exhibiting significant expression differences and potential as a biomarker. …”
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Exploration of the Prognostic Markers of Multiple Myeloma Based on Cuproptosis‐Related Genes by Xiao‐Han Gao, Jun Yuan, Xiao‐Xia Zhang, Rui‐Cang Wang, Jie Yang, Yan Li, Jie Li

“…Conclusion As a result of this investigation, a set of six biomarkers associated with cuproptosis (PARP1, EDEM3, SEC23A, RSL24D1, TTC37, and SRP72) were screened to provide a basis for predicting the prognosis of MM.…”
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Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…The trained model achieved an accuracy of 94.95% in correctly identifying bifurcation points. Based on predicted bifurcation points and blood vessel segments, we use the Graph-Based Radiomics Feature Extraction Algorithm (Graph-BRFExtract) to extract the adjacency matrix. …”
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Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India. by Gowri Uday, Bethan V Purse, Douglas I Kelley, Abi Vanak, Abhishek Samrat, Anusha Chaudhary, Mujeeb Rahman, France F Gerard

“…Backscatter thresholds were identified manually, and our Bayesian algorithm found these thresholds were very likely (>97%). …”
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Application of Internet marketing tools for management decision-making by logistics enterprises by O.A., T.O., O.P., H.Yu.

“…The direct influence of the size of the advertising budget on the predicted indicators of targeted advertising has been proven. …”
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GEOECOLOGICAL DIAGNOSTICS OF THE REGIONS IN THE CENTRALBLACK SOIL REGION by S. A. Kurolap, N. V. Yakovenko, V. I. Fedotov, V. B. Mikhno, L. N. Kostyleva

“…The authors propose an algorithm to perform geoecological diagnostics of the large region. …”
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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…For this reason, the suggested OMVMD-GRKR model serves as a valuable framework for predicting IWQI parameters.…”
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The Application of Artificial Digital Models in X-Ray Computed Tomography (CT) of the Core in Solving the Problem of Binarization of the Void Space of Reservoir Rocks by O. A. Melkishev, Y. V. Savitsky, S. V. Galkin

“…The use of the proposed model for predicting boundary values on obtained tomograms demonstrated a high degree of consistency with actual data.…”
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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in‐situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. …”
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Definition of complex roots of the fourth degree algebraic equation by V. M. Ilyin

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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…Our machine learning approach established an activity threshold, above which divergent wavefront patterns—termed “significant sources”—predicted AF recurrence. This threshold was validated in 85 prospective patients from the published FLOW-AF trial. …”
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Digital processing of images of nano-dimensional elements on nanostructured material surface using the ImageJ software by H. T. Dinh, N. V. Lushpa, K. V. Chernyakova, I. A. Vrublevsky

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EXTRAPOLATION BY ATTRACTION METHOD FOR IDENTIFICATION OF TRACKS AND PLOTS IN SYSTEM OF SECONDARY RADAR INFORMATION PROCESSING by U. A. Aparovich, R. V. Bondarenko

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A large-scale ENIGMA multisite replication study of brain age in depression by Laura K.M. Han, Richard Dinga, Ramona Leenings, Tim Hahn, James H. Cole, Lyubomir I. Aftanas, Alyssa R. Amod, Bianca Besteher, Romain Colle, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Konstantin V. Danilenko, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Roberto Goya-Maldonado, Nynke A. Groenewold, Paul Hamilton, Naho Ichikawa, Jonathan C. Ipser, Eri Itai, Sheri-Michelle Koopowitz, Meng Li, Go Okada, Yasumasa Okamoto, Olga S. Churikova, Evgeny A. Osipov, Brenda W.J.H. Penninx, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Matthew D. Sacchet, Hotaka Shinzato, Kang Sim, Dan J. Stein, Aslihan Uyar-Demir, Dick J. Veltman, Lianne Schmaal

“…Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. …”
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AN INTERDISCIPLINARY APPROACH TO TREATMENT OF TEMPOROMANDIBULAR PAIN-DYSFUNCTION SYNDROME AGAINST THE BACKDROP OF CERVICAL OSTEOCHONDROSIS. by V.L. Melnik, V.K. Shevchenko

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FACE DETECTION ON THE FRAME IN VIDEO SEQUENCE IN THE BIOMETRIC MONITORING ACCESS CONTROL SYSTEM by I. I. Frolov

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REAL TIME OBJECT MONITORING USING ADAPTIVE CORRELATION FILTER by A. M. Polonevitch, M. P. Revotjuk

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