The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 percent.
Convolutional Neural Network 1. Introduction Breast cancer (BC) is one of the leading causes of death in women worldwide. In 2020, two million and two hundred women were diagnosed with BC worldwide, which resulted in the death of 685,000 [1]. Tumor size growth and involvement of the sent...
They applied SVM, decision tree (DT), Bayesian network (BN), ANN, and convolutional neural network (CNN-LeNet and CNN-AlexNet), and the results showed that ANN has the best performance with an average accuracy of 97.36%. Elbashir et al.41 developed a lightweight CNN model for detecting ...
CNNs typically employ a predefined set of elements and are commonly utilized for supervised learning. In these neural networks, each neuron is connected to every other neuron in the subsequent layers. The activation function of the neural network converts the input value of the neurons into their...
neural networks, convolutional neural networks, convolution, math, probability In a previous post, we built up an understanding of convolutional neural networks, without referring to any significant mathematics. To go further, however, we need to understand convolutions. ...
neural networks,convolutional neural networks,convolution,math,probability In aprevious post, we built up an understanding of convolutional neural networks, without referring to any significant mathematics. To go further, however, we need to understand convolutions. ...
If you don't consider yourself to be quite the math buff, there is no need to worry since this course is based on a more intuitive approach to the concept of convolutional neural networks, not a mathematical or a purely technical one. Those of you who have practiced any field that entail...
[32] or the use of wavelets[19]. Going along with the line, [24] proposes to calculate a combination of energies of the signal and used an Artificial Neural Network (ANN) to perform the classification. Also, closely related, there are those works that focus on high-level features such ...
So we can see that the deep neural network is not sensitive to the scale of the object. Multi-scale image is only slightly higher in detection accuracy than a single-scale image, but the efficiency is greatly reduced. Therefore, we use the single-scale approach (i.e., the short edge is...
wherecirepresents theithlabel class,Lodenotes the input matrix, and\( {\mathcal F} \)denotes the feature expression. The goal of CNN training is to minimize the network loss functionF(W,b). At the same time, to alleviate the over fitting problem, the final loss functionE(W,b) is usu...