Learning process on networks

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 viengfa 28-10-2024 2 2   Download

Using a data-driven approach to study and predict the shear strength of slender steel fiber reinforced concrete beams has great applicability for the design and construction process. Based on the data-driven approach, an Artificial Neural Network (ANN) model with some hyperparameters optimized by Particle Swarm Optimization (PSO) algorithm is successfully built.

12p vifaye 20-09-2024 2 1   Download

The research subject is the process of economic and mathematical modelling of time series characterizing the bitcoin exchange rate volatility, based on the use of artificial neural networks. The purpose of the work is to search and scientifically substantiate the tools and mechanisms for developing prognostic estimates of the crypto currency market development. The paper considers the task of financial time series trend forecasting using the LSTM neural network for supply chain strategies.

5p longtimenosee09 08-04-2024 9 1   Download

Bài giảng HDL & FPGA - Chương 5: Các vấn đề khác. Chương này cung cấp cho sinh viên những nội dung kiến thức gồm: interesting topics in the field of Reconfigurable Computing (FPGA); Network-on-Chip - bối cảnh ra đời; Signal processing & Machine learning applications on FPGA; Hybrid reconfigurable CPUs; một vài ứng dụng trên FPGA;... Mời các bạn cùng tham khảo!

17p nguyetthuongvophong1010 04-03-2024 5 1   Download

The better-connected countries tend to have lower technology intensity if the technology has become obsolete. Finally, the third chapter is a theoretical approach to the technology diffusion. In particular, the technology diffusion across countries can be generalized as a learning process on networks. Based on a stylized learning model, this chapter examines the impact of the network structures on the speed of the diffusion process.

105p fugu897 03-07-2019 44 3   Download

Link-State Routing Process 1. Each router learns about its own links, its own directly connected networks. (Interface is “up”) 2. Each router is responsible for meeting its neighbors on directly connected networks. (OSPF Hello packets) 3. Each router builds a link-state packet (LSP) containing the state of each directly connected link. (neighbor ID, link type, and bandwidth) 4. Each router floods the LSP to all neighbors, who then store all LSPs received in a database. Neighbors then flood the LSPs to their neighbors until all routers in the area have received the LSPs. 5.

51p vanmanh1008 21-05-2013 69 6   Download

Which of the following are required when adding a network to the OSPF routing process configuration? (Choose three.) In a lab test environment, a router has learned about network 172.16.1.0 through four different dynamic routing processes. Which route will be used to reach this network? A network administrator is analyzing routing update behavior on a network that has both EIGRP and OSPF configured on all routers. Both protocols appear in the output of show ip protocols. However, only EIGRP internal routes appear in the routing tables. Which statement correctly explains the scenario?...

5p phutran76 06-06-2012 158 30   Download

EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent Mohamed Ibnkahla Electrical and Computer Engineering Department, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Email: mohamed.ibnkahla@ece.queensu.ca Received 13 December 2002 and in revised form 17 May 2003 We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory.

9p sting12 10-03-2012 43 6   Download

Assume that gi (x) = 1 (hence gk (x) = 0, k = i), update the expert i based on output error. Update gating network so that gi (x) is even closer to unity. Alternatively, a batch training method can be adopted: 1. Apply a clustering algorithm to cluster the set of training samples into n clusters. Use the membership information to train the gating network. 2. Assign each cluster to an expert module and train the corresponding expert module. 3. Fine-tune the performance using gradient-based learning. Note that the function of the gating network is to partition the feature...

20p longtuyenthon 26-01-2010 109 10   Download

Subnetting seems to be a battle of fighting bits, decimal numbers, and countless methods and processes to convert from one to the other. While the methods may be confusing, the mathematics behind them is the same for all. In this paper, you will learn some of the simpler ways to figure out many of the subnetting questions that you will find on the industry certification tests.

10p huyhoang 06-08-2009 43 4   Download