Search Results - "algorithm"

Mathematical model of a lightweight three-axle off-road vehicle construction for Arctic zone of Russia by I. E. Agureev, V. N. Bondarenko

“…The work uses numerical methods to solve the equations of the constructed model, which enables to gradually weaken the accepted assumptions and build more general calculation algorithms. The main integration method for ensuring the stability of solutions is the multi-step Adams method, which, with the correct choice of step, ensures the necessary stability of the solution over sufficiently long model times. …”
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Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology by A. Z. Kotarba, I. Wojciechowska

“…<p>Reliable deep-convective cloud (DCC) climatology relies heavily on accurate detection. Infrared-based algorithms play a critical role, as they are the only ones that can be applied to the 6.7 <span class="inline-formula">µ</span>m water vapour (WV) absorption band and the 11 <span class="inline-formula">µ</span>m infrared (IR) window band. …”
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Predicting soil organic matter using corrected field spectra and stacking ensemble learning by Yu Wang, Xuhui Yan, Rongyanting Huo, Longcai Zhao, Jie Peng, Yongsheng Hong, Jing Liu

“…The field prediction of SOM using spectra correction algorithms in conjunction with ensemble learning remains a significant and unresolved challenge. …”
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Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height by Yi Wu, Yu Chen, Chunhong Tian, Ting Yun, Mingyang Li

“…This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. …”
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Early Diabetic Retinopathy Detection from OCT Images Using Multifractal Analysis and Multi-Layer Perceptron Classification by Ahlem Aziz, Necmi Serkan Tezel, Seydi Kaçmaz, Youcef Attallah

“…<b>Results:</b> A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. …”
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Artificial intelligence in molecular and genomic prostate cancer diagnostics by A. O. Morozov, A. K. Bazarkin, S. V. Vovdenko, M. S. Taratkin, M. S. Balashova, D. V. Enikeev

“…They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.Objective. …”
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Testicular appendix (hydatid of Morgagni) torsion in adult urological management: evaluation of treatment outcomes by I. S. Shormanov, D. N. Shedrov, D. Y. Garova, A. I. Ryzhkov

“…Accordingly, there are no clear algorithms for the management of such patients.Objective. …”
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A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. by Melanie Wyld, Rachael Lisa Morton, Andrew Hayen, Kirsten Howard, Angela Claire Webster

“…The main limitations of this study were that treatment assignments were not random, that only transplant had longitudinal data available, and that we calculated EuroQol Group EQ-5D scores from SF-36 and SF-12 health survey data, and therefore the algorithms may not reflect EQ-5D scores measured directly.…”
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Continuity of specialized care upon transfer of adolescents with epilepsy from a pediatric to adult outpatient service by M. N. Sarzhina, S. G. Burd, M. B. Mironov, M. M. Gunchenko, T. T. Batysheva

“…It is important to carefully watch any possible replacement of antiepileptic drugs (AEDs), which may affect the further course of epilepsy.Aim: to study the algorithms and procedures for bringing patients with epilepsy from a pediatric to adult outpatient practice, and also to evaluate the efficacy of therapy with synonymously replaced AEDs during this period.Materials and methods. …”
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Comparison and Evaluation of Rain Gauge, CMORPH, TRMM PR and GPM DPR KuPR Precipitation Products over South China by Rui Wang, Huiping Li, Hao Huang, Liangliang Li

“…This study aims to provide valuable insights for enhancing precipitation retrieval algorithms and improving the applicability of remote sensing precipitation products.…”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. Subsequently, multivariate COX regression analysis was performed to identify independent risk factors for constructing the predictive nomogram. …”
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Cytomegalovirus Infection in Adolescents of Russian Federation: Results of Cross-Sectional Population Analysis of Seroprevalence by Ekaterina Yu. Dubonosova, Leyla S. Namazova-Baranova, Elena A. Vishneva, Nikolay A. Mayanskiy, Tatiana V. Kulichenko, Margarita A. Soloshenko

“…Clear understanding of epidemiology and determination of focus population groups is crucial for development of measures and algorithms of congenital CMVI prevention.Objective. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…Three (3) classification algorithms including Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained using the QoS dataset and then evaluated in order to determine the most effective model based on certain evaluation metrics – accuracy, precision, F1-Score and recall. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…Experimental results demonstrate that our model achieves an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn></mrow></msub></mrow></semantics></math></inline-formula> of 96.5% and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn><mo>:</mo><mn>0.95</mn></mrow></msub></mrow></semantics></math></inline-formula> of 81.5%, outperforming other mainstream detection models including the YOLO family of algorithms. These findings highlight the potential of SCCA-YOLO for high-precision lunar crater detection and provide valuable insights into future lunar surface analysis.…”
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Competence model scheming of the graduate as an assessment tool for the quality of education by E. N. Shaforostova, A. A. Valova

“…For reliable identification of the formation level of social and personal qualities, the obtained adapted version of R Ketell’s multifactorial questionnaire was used, in which the components are the formed groups of personal qualities. The algorithms of adaptive testing to assess the level of competence of students in the development of academic disciplines, as well as the assessment of existing social and personal qualities of students were developed.Conclusion. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…Furthermore, a fault diagnosis method based on CBAM-MobileNetV2 is proposed to achieve fault diagnosis of mine hoist main bearings under small sample data. At the algorithmic level, the Wasserstein distance metric is introduced to solve the problem of vanishing training gradients in generative adversarial networks. …”
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Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). …”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

“…We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. …”
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Pharmacological correction of hepatotoxicity induced by anti-tuberculosis drugs by D. S. Sukhanov, E. V. Timofeev, Y. S. Alekseeva, K. A. Nagornova

“…At the same time, there is no consistent understanding of their effectiveness, as well as consistent application algorithms, since a number of drugs have been studied only in experimental or small observational clinical studies. …”
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Construction and demolition waste material library based on vision systems dataZenodo by Maria Teresa Calcagni, Giovanni Salerno, Gloria Cosoli, Giuseppe Pandarese, Gian Marco Revel

“…In addition, the benefits of this resource for the scientific and industrial community are discussed, including the possibility of using the data to develop/fine-tune artificial intelligence (AI) algorithms capable of optimising sorting and recycling processes by recognition and discrimination among different types of CDW material using the aforementioned sensors. …”
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