What are the challenges of identifying and mitigating harmful content online?

Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ... The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current ... Nov 26, 2015 ... The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These ... Mar 25, 2025 ... We introduce CNN-TumorNet, an innovative method for brain tumor classification utilizing a deep convolutional neural network (CNN) for the ... Sep 2, 2023 ... Image processing using Deep learning models CNN · Want to upload an image dataset? · Use of Deep Learning specifically CNN which is normally ... Apr 12, 2023 ... This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state ( ... Aug 26, 2020 ... Object detection: With CNN, we now have sophisticated models like R-CNN ... machine learning, and artificial intelligence professionals. Apr 17, 2020 ... Convolution in Convolutional Neural Networks. The convolutional neural network, or CNN for short, is a specialized type of neural network model ... Feb 17, 2019 ... Everything is rational,” Mike Chen, an equity portfolio manager at Boston-based PanAgora, told CNN Business from the sidelines of the Cayman ... May 4, 2021 ... This work focuses on the use of different course of action to train the deep neural network models. A useful way of thinking about model ...