Using Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited DataUsing Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited Data
In recent years developing new material and compounds have become more important because of the community’s needs. Material scientist and physicist great effort make significant changes in daily life. But nowadays it is important to make these changes in a short time. In this point of view, artifici...
Saved in:
| Main Author: | Kağan Şarlar |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2021-06-01
|
| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1447764 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Face Recognition using Deep Learning and TensorFlow framework
by: Makrem Beldi
Published: (2023-12-01) -
A TensorFlow implementation of Local Binary Patterns Transform
by: Devrim Akgün
Published: (2021-06-01) -
Auto forensic detecting algorithms of malicious code fragment based on TensorFlow
by: Binglong LI, et al.
Published: (2021-08-01) -
Development of Integrated Choice and Latent Variable (ICLV) Models Using Matrix-Based Analytic Approximation and Automatic Differentiation Methods on TensorFlow Platform
by: Jie Ma, et al.
Published: (2022-01-01) -
Diagnosa Penyakit BrownSpot dan LeafBlast Pada Tanaman Padi dengan MobileNetV2 dan TensorFlow-Lite
by: Rahmat Gunawan, et al.
Published: (2024-06-01)