Results 31 to 40 of about 4,047,749 (292)

Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications

Frontiers in Neuroscience, 2020
In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro, Martino Sorbaro, Qian Liu, Massimo Bortone, Sadique Sheik   +4 more
doaj   +1 more source

A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging

Engineering Proceedings, 2023
Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging.
Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta   +5 more
doaj   +1 more source

A Review of Convolutional Neural Network Development in Computer Vision

EAI Endorsed Transactions on Internet of Things, 2022
Convolutional neural networks have made admirable progress in computer vision. As a fast-growing computer field, CNNs are one of the classical and widely used network structures. The Internet of Things (IoT) has gotten a lot of attention in recent years.
Hang Zhang
doaj   +1 more source

CNN 101: Interactive Visual Learning for Convolutional Neural Networks [PDF]

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models.
Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau   +7 more
openaire   +3 more sources

Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks

Remote Sensing, 2020
The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment.
Guojie Wang, Mengjuan Wu, Xikun Wei, Huihui Song   +3 more
doaj   +1 more source

Quantum convolutional neural networks for high energy physics data analysis

Physical Review Research, 2022
This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo   +4 more
doaj   +1 more source

Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm

IEEE Access
In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing.
Abdel-Hamid M. Emara, Ghada Atteia, Jawad Hasan Alkhateeb   +2 more
doaj   +1 more source

Empirical Remarks on the Translational Equivariance of Convolutional Layers

Applied Sciences, 2020
In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance
Kyung Joo Cheoi, Hyeonyeong Choi, Jaepil Ko   +2 more
doaj   +1 more source

Creating Deep Convolutional Neural Networks for Image Classification

The Programming Historian, 2023
This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on ...
Nabeel Siddiqui
doaj   +1 more source

YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum

AgriEngineering, 2023
Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like
Hee-Mun Park, Jin-Hyun Park
doaj   +1 more source