Integrating artificial and human intelligence: a partnership for responsible innovation in biomedical engineering and medicine
Historically, the term “artificial intelligence” dates to 1956 when it was first used in a
conference at Dartmouth College in the US. Since then, the development of artificial …
conference at Dartmouth College in the US. Since then, the development of artificial …
Machine learning facilitated business intelligence (Part I) Neural networks learning algorithms and applications
Purpose The purpose of this paper is to conduct a comprehensive review of the noteworthy
contributions made in the area of the Feedforward neural network (FNN) to improve its …
contributions made in the area of the Feedforward neural network (FNN) to improve its …
Not all samples are created equal: Deep learning with importance sampling
Abstract Deep Neural Network training spends most of the computation on examples that
are properly handled, and could be ignored. We propose to mitigate this phenomenon with a …
are properly handled, and could be ignored. We propose to mitigate this phenomenon with a …
Impact of training set batch size on the performance of convolutional neural networks for diverse datasets
PM Radiuk - 2017 - elar.khmnu.edu.ua
Анотація A problem of improving the performance of convolutional neural networks is
considered. A parameter of the training set is investigated. The parameter is the batch size …
considered. A parameter of the training set is investigated. The parameter is the batch size …
Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging
J Peng, S Kang, Z Ning, H Deng, J Shen, Y Xu… - European …, 2020 - Springer
Background We attempted to train and validate a model of deep learning for the
preoperative prediction of the response of patients with intermediate-stage hepatocellular …
preoperative prediction of the response of patients with intermediate-stage hepatocellular …
Superneurons: Dynamic GPU memory management for training deep neural networks
Going deeper and wider in neural architectures improves their accuracy, while the limited
GPU DRAM places an undesired restriction on the network design domain. Deep Learning …
GPU DRAM places an undesired restriction on the network design domain. Deep Learning …
An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier
Recent technological advancements in information and communication technologies
introduced smart ways of handling various aspects of life. Smart devices and applications …
introduced smart ways of handling various aspects of life. Smart devices and applications …
Hyperspectral image superresolution by transfer learning
Y Yuan, X Zheng, X Lu - IEEE Journal of Selected Topics in …, 2017 - ieeexplore.ieee.org
Hyperspectral image superresolution is a highly attractive topic in computer vision and has
attracted many researchers' attention. However, nearly all the existing methods assume that …
attracted many researchers' attention. However, nearly all the existing methods assume that …
Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach
The most important quality parameter of a product is its nutritional value, but marketability of
agricultural products depends primarily on the overall appearance and shape of the …
agricultural products depends primarily on the overall appearance and shape of the …
An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks
With improvements in the area of Internet of Things (IoT), surveillance systems have recently
become more accessible. At the same time, optimizing the energy requirements of smart …
become more accessible. At the same time, optimizing the energy requirements of smart …