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Model learning
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This study proposes to test a combination model between CNN network and XGBoost algorithm for weather image classification problem. The proposed model uses deep learning network, namely CNN for feature extraction, then feeds the features into the XGBoost classifier to recognize the images.
6p
vithomson
02-07-2024
0
0
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In this study, we apply the YOLOv8 architecture, take license plates from multiple viewing angles as input, and propose a deep learning model for accurate license plate recognition in various realworld situations.
10p
vithomson
02-07-2024
0
0
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The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.
7p
vithomson
02-07-2024
0
0
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The study sheds light on the role of word embeddings in constructing robust spam detection models, offering valuable guidance for model selection. The methodology, comparative analysis, and future directions are also presented in the paper.
5p
vithomson
02-07-2024
0
0
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This research focuses on developing a method to optimize the DCNN (Deep Convolutional Neural Network) classification model for plant diseases. We enriched the data by incorporating data from two public datasets, PlantVillage Dataset (PVD) and CroppedPlant Dataset (CPD), and we trained the model using two-step transfer learning.
6p
vithomson
02-07-2024
0
0
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This study seeks to introduce a more comprehensive assessment method than previous endeavors, particularly concerning the bond strength of FRP bars with various surface types within concrete, spanning normal, high-strength, and ultra-high-strength concrete.
16p
vithomson
02-07-2024
0
0
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This paper, in general, can be considered a useful reference for both learners and educators in regard to the applicability of the flipped classroom model to foster students’ autonomous learning and their involvement inside and outside the class.
15p
vialicene
02-07-2024
1
0
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In this study, four machine learning models have been studied which are Artificial Neural Networks, Convolutional Neural Networks, Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM). They have been used to forecast the solar power of Nhi Ha solar farm in short-term. First, data from Nhi Ha solar farm were collected and underwent preprocessing before being utilized by aforementioned distinct machine learning models.
8p
vialicene
02-07-2024
0
0
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Part 1 of ebook "An introduction to statistical learning with applications in R" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Statistical learning; Chapter 3 - Linear regression; Chapter 4 - Classification; Chapter 5 - Resampling methods;...
212p
daonhiennhien
03-07-2024
1
1
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Part 2 of ebook "An introduction to statistical learning with applications in R" provides readers with contents including: Chapter 6 - Linear model selection and regularization; Chapter 7 - Moving beyond linearity; Chapter 8 - Tree-based methods; Chapter 9 - Support vector machines; Chapter 10 - Unsupervised learning;...
222p
daonhiennhien
03-07-2024
2
1
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Part 2 of ebook "Artificial intelligence for fashion: How AI is revolutionizing the fashion industry" provides readers with contents including: Chapter 6 - Data science and subscription services; Chapter 7 - Predictive analytics and size recommendations; Chapter 8 - Generative models as fashion designers; Chapter 9 - Data mining and trend forecasting; Chapter 10 - Deep learning and demand forecasting; Chapter 11 - Robotics and manufacturing; Chapter 12 - Democratization and impacts of AI;...
121p
daonhiennhien
03-07-2024
1
1
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Part 1 of ebook "Beginning data science in R: Data analysis, visualization, and modelling for the data scientist" provides readers with contents including: Chapter 1 - Introduction to R programming; Chapter 2 - Reproducible analysis; Chapter 3 - Data manipulation; Chapter 4 - Visualizing data; Chapter 5 - Working with large data sets; Chapter 6 - Supervised learning;...
188p
daonhiennhien
03-07-2024
1
1
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Part 2 of ebook "Beginning data science in R: Data analysis, visualization, and modelling for the data scientist" provides readers with contents including: Chapter 7 - Unsupervised learning; Chapter 8 - More R programming; Chapter 9 - Advanced R programming; Chapter 10 - Object oriented programming; Chapter 11 - Building an R package; Chapter 12 - Testing and package checking; Chapter 13 - Version control; Chapter 14 - Profiling and optimizing;...
181p
daonhiennhien
03-07-2024
2
1
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Part 2 of ebook "Introduction to deep learning: From logical calculus to artificial intelligence" provides readers with contents including: Chapter 4 - Feed forward neural networks; Chapter 5 - Modifications and extensions to a feed-forward neural network; Chapter 6 - Convolutional neural networks; Chapter 7 - Recurrent neural networks; Chapter 8 - Autoencoders; Chapter 9 - Neural language models; Chapter 10 - An overview of different neural network architectures; Chapter 11 - Conclusion;...
107p
daonhiennhien
03-07-2024
1
1
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Part 1 of ebook "Statistical learning from a regression perspective (Second edition)" provides readers with contents including: Chapter 1 - Statistical learning as a regression problem; Chapter 2 - Splines, smoothers, and kernels;...
149p
daonhiennhien
03-07-2024
2
1
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Part 1 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Overview of supervised learning; Chapter 3 - Linear methods for regression; Chapter 4 - Linear methods for classification; Chapter 5 - Basis expansions and regularization; Chapter 6 - Kernel smoothing methods; Chapter 7 - Model assessment and selection; Chapter 8 - Model inference and averaging; Chapter 9 - Additive models, trees, and related methods;...
355p
daonhiennhien
03-07-2024
2
1
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Part 2 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 10 - Boosting and additive trees; Chapter 11 - Neural networks; Chapter 12 - Support vector machines and flexible discriminants; Chapter 13 - Prototype methods and nearest-neighbors; Chapter 14 - Unsupervised learning; Chapter 15 - Random forests; Chapter 16 - Ensemble learning; Chapter 17 - Undirected graphical models; Chapter 18 - High-dimensional problems p ≫ N;...
409p
daonhiennhien
03-07-2024
4
1
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Ebook "ROBOT-PROOF: Higher education in the age of artificial intelligence" proposes a way to educate the next generation of college students to invent, to create, and to discover--to fill needs in society that even the most sophisticated artificial intelligence agent cannot. A robot-proof education, Aoun argues, is not concerned solely with topping up students' minds with high-octane facts.
210p
daonhiennhien
03-07-2024
1
1
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In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.
8p
viwalton
02-07-2024
3
1
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Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC.
11p
vishanshan
27-06-2024
1
1
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