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i am doing my research under the trust concept in sensor networks.
is there is any formula to calculate the trust value among the sensor nodes?
Millions of personal data belonging to people across the globe from Facebook was gathered and then allegedly used by the political consultancy Cambridge Analytica for politcal purpses, notably election influence. Apartb from the nose-diving market value of Facebook, key questions have arisen: Did Facebook do enough to prevent the data breach? Is this a new low for privacy? Is this a new low for the democratic process? Should Facebook be regulated like any other business? And should Mark Zukerberg step down or shutter the social network? Interesting questions begging for anwers!
To create genetic networks in 24 studies, I have different genes for each study and have rsSNP, P-value, SE, Beta, MAF values and ...:
In the first study:
rsSNP N Position Beta SE P-value Gene Name MAF
And the rest of the studies are the same.
What are the options for creating a Node and Edge to create a gene networks in an Excel file?
And how do I connect 24 study genes together in Cytoscale using MCODE, ClueGO and String apps?
When you simulate 802.11a in ns2.31, what is the maximum practical throughput achived?
This is with regard to peer to peer communication in decentralized network.
I found the notes on this interaction trust value in the Paper named- Self Organizing Trust Model.. written by Ahmet B Can and Bharath Bhargava
If I have a set of input data and a set of output data as shown in attached file,
Can a Neural Network predict an output if the input data out of the given input data?
Example: if the new input is (28,42) ====> what is the output?
I neural networks (8-4-1) were used with PSO using diabetes data, but the result of the outputs is somewhat far from the value of the targets, and here the output value should be close to the value of the targets? What do I have to do to make the output value close to the value of the targets?
Hello ResearchGate community
I am working on a deep learning (CNN + AEs) approach on facial images.
I have an input layer of 112*112*3 of facial images, 3 convolution + max pooling + ReLU and also 2 layers of fully connected with 512 neurons with 50% dropout to avoid overfitting and last output layer with 10 neurons since I have 10 classes.
I have also used reduce mean of softmax cross entropy and also L2.
For training I divided my dataset to 3 groups of:
60% for training
20% for validation
20% for evaluation
The problem is after few epochs the validation error rate stay fixed value and never changes. I have used tensorflow to implement my project.
I hadn't such problem before with CNNs so I think it's first time. I have cheked the code it's based on tensorflow documentation so I don't think if the problem is with the code. maybe I need to change some parameters but I am not sure.
Any idea about common solutions for such problem?