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1441
Using of the complex alloy for microalloyed gray cast iron
Published 2003-08-01Get full text
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1442
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1443
Modern Possibilities of Using Acotiamide in the Treatment of Functional Dyspepsia
Published 2024-03-01Get full text
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1444
Progress of research on aviation kerosene preparation using bioethanol
Published 2025-03-01Get full text
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1445
USING ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT PROCESSES
Published 2020-09-01Get full text
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1446
Optical control of sphingolipid biosynthesis using photoswitchable sphingosines
Published 2025-01-01Get full text
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1447
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1448
Forecasting consumption of electric energy by using wavelet transform
Published 2021-06-01Get full text
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1449
Reversible data hiding in encrypted images using recompression
Published 2015-09-01Get full text
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1450
Characterization of Extremal Unicyclic Graphs Using F-Coindex
Published 2021-01-01Get full text
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1451
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1452
Robotic assembly of modular concrete shells using falsework
Published 2025-03-01Get full text
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1453
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1454
Security of the deployment of SDN-based IoT using blockchain
Published 2020-12-01Get full text
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1455
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1456
Bathymetry estimation for coastal regions using self-attention
Published 2025-01-01Get full text
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1457
Solar Flare Forecasting Using Hybrid Neural Networks
Published 2025-01-01“…Therefore, it is crucial to establish highly accurate solar flare prediction models to enable humans to anticipate solar flare eruptions in advance, thereby reducing human and economic losses. In this paper, we utilized the solar active region (AR) magnetogram provided by the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager and the associated feature parameters of the magnetic field; specifically, the feature vectors of the magnetic field’s spatial structure characteristics and the magnetic field feature parameters are fused to predict solar flares. …”
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1459
Analysis of Climatic Model Using Fractional Optimal Control
Published 2023-01-01Get full text
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1460
Predicting breast cancer recurrence using deep learning
Published 2025-01-01“…This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel approach designed to predict breast cancer recurrence with high accuracy and interpretability. Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). …”
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