Recurrent Neural Networks (RNN): A special type of neural network, RNN is a complex network that uses the output of a node ...

Generative AI is revolutionizing the field of cybersecurity by providing advanced tools for threat detection, analysis, and ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, have developed a quantum algorithm technology for deep convolutional neural network (CNN) exchange ...

This study uses all-optical electrophysiology methods to provide a valuable insight into the organization of cortical networks and their ability to balance the activity of groups of neurons with ...

Deep neural networks (DNNs) have revolutionized machine learning, yet their success defies classical expectations. They ...

Abstract: Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. Convolutional Neural Networks (CNNs) have demonstrated potential in ...

Morphological profiling allows accurate identification of cell types in dense iPSC-derived cultures, allowing its use for quality control and differentiation monitoring.

An Effective Cloud Detection Network for Remote Sensing Images. Journal of Computer and Communications, 13, 1-14. doi: 10.4236/jcc.2025.131001 . Optical remote sensing technology is increasingly ...

A groundbreaking study by researchers from the University of Namur, Belgium introduces a novel, contactless method for ...

Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep ...

Then, based on G-PLIF, we designed a Multi-Scale-Residual Spiking Neural Network (MSR-SNN) specifically for the classification tasks of neuromorphic vision objects. The concept of a multi-scale ...