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1
Application of computer vision algorithm in ceramic surface texture analysis and prediction
Published 2025-03-01Subjects: Get full text
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2
Ensemble machine learning model for forecasting wind farm generation
Published 2024-04-01Subjects: Get full text
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3
An efficient method to generate elliptic curves
Published 2001-01-01Subjects: “…public key cryptosystem;elliptic curve generation…”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network
Published 2025-05-01Subjects: Get full text
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6
Methodological proposal to generate interactive virtual walkthrough
Published 2013-11-01Get full text
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Technologies To Fully Generate A Synthetic Training Experience
Published 2025-05-01Get full text
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9
Combining the Cellular Automata and Marching Square to Generate Maps
Published 2025-04-01Subjects: Get full text
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10
How to generate popular post headlines on social media?
Published 2024-01-01Subjects: Get full text
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11
An Algorithm to Automatically Generate the Combinatorial Orbit Counting Equations.
Published 2016-01-01“…The first allows to generate the equations needed in an automatic way. …”
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Can large language models generate geospatial code?
Published 2025-08-01Subjects: “…Geospatial code generation…”
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14
Générations démographiques, générations familiales
Published 2004-06-01“…Demographers can help define the concept of generation, and specifically why people feel they belong to a group based on their age. …”
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Defect Diagnosis of Photovoltaic Module Visible Light Images Under Imbalanced Sample Conditions
Published 2025-01-01Subjects: Get full text
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17
PENERAPAN MODEL PEMBELAJARAN PEMBANGKIT ARGUMEN MENGGUNAKAN METODE SAINTIFIK UNTUK MENINGKATKAN KEMAMPUAN KOGNITIF DAN KETERAMPILAN BERARGUMENTASI SISWA
Published 2014-07-01Subjects: “…the generate argument instructional model…”
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Generate medical synthetic data based on generative adversarial network
Published 2022-03-01Subjects: “…generative adversarial network…”
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Method to generate cyber deception traffic based on adversarial sample
Published 2020-09-01“…In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.…”
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