Results 31 to 40 of about 5,510,322 (305)
Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures [PDF]
, 2021 Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an
Asmita, Suman, Akshansh, Mishra
core +1 more source
Applied Sciences, 2022 There are a lot of developing countries with inadequate meteorological stations to measure solar radiation. This has been a major drawback for solar power applications in these countries as the performance of the solar-powered system cannot be accurately
Mustapha Mukhtar +6 more
doaj +1 more source
Convolutional Neural Network (CNN): A comprehensive overview
International Journal of Multidisciplinary Research and Growth Evaluation, 2022 Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN
openaire +1 more source
Metals, 2022 Controlled cooling technology is widely used in hot-rolled steel plate production lines. The final cooling temperature directly affects the microstructure and properties of steel plates, but cooling and heat transfer constitutes a nonlinear process ...
Xiao Hu +3 more
doaj +1 more source
A-CNN: Annularly Convolutional Neural Networks on Point Clouds [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution.
Artem Komarichev +2 more
openaire +3 more sources
Information, 2018 Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging.
Abdullah-Al Nahid, Yinan Kong
doaj +1 more source
Understanding of Convolutional Neural Network (CNN): A Review
International Journal of Robotics and Control Systems, 2023 The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans.
Purwono Purwono +5 more
openaire +1 more source
, 2022 Comparison of between the best performing Convolutional Neural Network (CNN) model and the human doctors.
Min-Chang Jang (12133336) +8 more
core +1 more source
Deep Learning: Basics and Convolutional Neural Networks (CNNs)
, 2023 Abstract Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and
Vakalopoulou, Maria +4 more
openaire +2 more sources
, 2022 Basic architecture of the Convolutional Neural Network (CNN) with two convolution and pooling layers.
Neville Sweijd (12431677) +2 more
core +1 more source