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Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules
Glavatskikh M., Madzhidov T., Solov'ev V., Marcou G., Horvath D., Graton J., Le Questel J., Varnek A.
set of 598 organic compounds. Developed models were then challenged to make predictions
ПРОГНОЗНАЯ АНАЛИТИКА В УПРАВЛЕНИИ ЧЕЛОВЕЧЕСКИМ КАПИТАЛОМ ПРОМЫШЛЕННЫХ КОМПАНИЙ
Ожиганов Э.Н., Строева М.С.
and significance of HR analytics, as an emerging discipline, the core of which is the concept of predictive
EndoNet: A Model for the Automatic Calculation of H-Score on Histological Slides
Ushakov E., Naumov A., Fomberg V., Vishnyakova P., Asaturova A., Badlaeva A., Tregubova A., Karpulevich E., Sukhikh G., Fatkhudinov T.
of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780
Operational Efficiency of Computer Networks under Conditions of Malicious Cyber Activity
Zegzhda P.D., Anisimov V.G., Anisimov E.G., Saurenko T.N., Los’ V.P.
Abstract: In this paper, we propose a model for predicting the dynamics of a generalized efficiency
Predicting the operational efficiency of high-pressure roller crushers
Ivashchuk, O. A., Ivashchuk, O. D., Fedorov, V. I., Shtana, A. I.
Models based on regression analysis for predicting the operational efficiency of high
Exploring alternate coupling inputs of a data-driven model for optimum daily streamflow prediction in calibrated SWAT-BiLSTM rainfall-runoff modeling
Ahmad Khalil, Iqbal Mudassar, Tariq Muhammad Atiq Ur Rehman, Khan Afed Ullah, Nadeem Abdullah, Chen Jinlei, Usanova Kseniia, Almujibah Hamad, Alyami Hashem, Abid Muhammad
, physically based prediction models are prone to substantial uncertainties due to complex processes
Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model
Al-Tekreeti Khalid Fahmi Watban, Reza Kashyzadeh Kazem, Ghorbani Siamak
the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency
On statistical analysis and prediction of sap flow density for smart urban tree monitoring
Safargalieva A., Kochetkova I., Makeeva E., Shorgin S.
to predict the sap flow parameter using auto-regressive moving average and linear modeling. The results
The creation of a regression model of the Earth's pole motion with a feature of dynamic prediction
Khairutdinova A.N., Mubarakshina R.R., Andreev A.O., Nefedyev Y.A., Demina N.Y.
problem of predicting the Earth's pole motion. Using regression modelling, we form a complex model
Self-health analysis with two step histogram based procedure using machine learning
Kumar S.A., Kumar H., Dutt V., Soni H.
Machine learning is the critical tool in the future for prediction in the real-time to analyze

Страница 8 из 5344

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